Many debt collection companies are evaluating emerging technologies and looking into digital transformation. You can’t blame them: due to a faltering economy, rising costs, and high agent attrition, new processes and solutions are needed.
As a result, within the next three years, one in every ten interactions with call center agents will be voice bots driven, according to the new Gartner report. These findings are directly attributable to the spectacular rise to the advances in conversational artificial intelligence (AI), along with the mounting challenges we detailed above.
The report also estimates that by 2026, Conversational AI could save about $80 billion in labor costs! That is a significant number, indicative of the merits that early adopters will have in terms of cost, CX, and expansion of top and bottom lines. But, starting early is key to competitive advantage.
It is an open secret that high human agent churn is due to the fact that most calls are low-value and tediously repetitive. By handling these calls, Conversational AI will make the agents’ jobs more exciting and fulfilling, allowing them to focus on high-value and complex calls.
Globally, there are approximately 17 million contact center agents, and their cost makes up 95% of contact center costs. By intelligent call automation led by voice-intelligent technology, Voice AI, a big part of unproductive calls can be taken over by Digital Voice Agents, yielding high cost and CX advantages.
The Direct Cost and Efficiency Benefits of Voice AI for Debt Collection Agencies:
The most significant takeaway for the debt collection agency is that the benefits of Voice AI implementation are tangible and quickly realizable. But before we go into stats, here is a simple explanation of what essentially happens in a debt collection agency when they deploy a voicebot.
Voicebot Functioning
A voicebot is a conversational Voice AI application that can understand what the customer is saying as it is trained for a specific customer problem. It can strike a meaningful conversation with the customer. This happens because the entire conversation design has been done keeping in mind all the possible difficulties a customer can encounter.
So for every customer query, the voicebot has a ready answer as it pulls out relevant information from the client system and informs the customer, cutting the duration of the conversation remarkably.
Digital Voice Agents (DVA) Vs. IVRs: It is worth mentioning here that DVAs are remarkably different from IVRs; in fact, there is no comparison between the two. DVAs are at the cutting edge of the technological spectrum, while IVRs are legacy technology.
IVR can not converse. It is an unintelligent technology that runs a tedious exchange of inputs and outputs. For something as sensitive as debt collections, it is remarkably unsuitable.
Digital Voice Agent is AI-powered, built on Spoken Language Understanding (SLU) and context-rich conversational designs.
For a debt collections company, the two main categories of calls are Inbound and Outbound. Here is the process of value creation:
Inbound Calls: Many agencies cannot process a significant portion of customer calls. From them, a tiny fraction of customers have called to pay and perhaps need guidance.
Answering Non-revenue Generating Calls
The data from various sources is precise: A majority of calls are so simple that answering them by a human agent does not add any value to the company.
We’ve discussed the value of adopting a Digital Voice Agent for call automation. If you want to learn more, take a look at our Resources page, in which we regularly explore current topics related to the ARM industry.
Understanding the Top and Bottom Line Impact of a Voice AI Solution on a Debt Collection Company
The Final Word
Voice AI has proved its capability in bringing about a transformation of contact centers either with a small team or a big one. As its adoption increases, it will become a technology that can deliver sustainable cost advantages as well as a competitive advantage.
Refer to our Voice AI page for more information about its transformative potential.
What’s common among interactive voice response (IVR) systems, ATMs, knowledge base, mobile applications, virtual assistants or chatbots? They are all self-service options that can help dispense answers and resolve queries at lightning speed! Self-service or self-help tools and options make for an empowered customer support team and a loyal customer base.
Self-service equals simplified customer journeys!
In a mobile-driven digital economy, a brand’s relevance and value is measured in terms of the speed, convenience and the level of autonomy offered to their customers. Digital self-service is the central objective of today’s automated customer support, but tailored for better CX and performance. Since the COVID-19 pandemic, the usage of digitized self-service by customers across demographics accelerated with sudden digital transformation (DX). With newer entrants into the market— more digital native brands, always-on, smartphone users and Gen Z customers, numerous possibilities await businesses using digital self-service in their customer support. As per the recent OnePoll study involving over 10,000 respondents from 11 countries to explore humanity’s shifting relationship with digital tech and experiences:
Nearly 58% percent of participants said they will continue their digital brand interactions more than their pre-pandemic levels.
Most study respondents felt that the digital experience was fast and convenient, making it better or on par with the real-world, face-to-face customer service interactions.
Almost 66% reportedly had a ‘good’ or ‘excellent’ experience using online customer service options.
Diving a little deeper, the research summed up the exact reasons for positive reactions:
Instant issue/query resolution (48%).
Convenience (46%).
Speed (45%).
These findings form the crux of self-service. In this blog we will begin our exploration on why self-service tools are truly adept in capturing customers’ interest, and meeting productivity and performance goals of brands’ customer support.
Psychology behind Self-service
Self-service is not the same as automation. Sure, digital self-service gives automated responses to repetitive queries in blazing speed. It is not only about allowing customers to resolve things on their own but also empowering them to address them faster. The overall value of the self-service strategy is measured in terms of its impact on CX. The faster, more convenient and more cutting-edge the self-service options, the better would be the CX scores. Moreover, the intuitiveness and simplicity of self-service helps reduce customer effort while solving problems on their own. This lowers the customer effort score (CES), another key metric for frictionless CX!
An intuitive, anytime self-service strategy across the platforms or channels also helps evade a laundry list of options for customer service-related interactions and unnecessary contact with human agents. This is integral for curbing additional contact center operations costs and allocating resources and human efforts in areas that build proactive and customer-centric impressions. No wonder, in the U.S. 88% of customers prefer self-service for dealing with their everyday problems. The number is equal to the global average of customers that expect brands and businesses to include a self-service support portal.
The Most Common Types of Self-service Options
Now, let’s have a look at this run-down of the widely adopted self-service support options.
Knowledge Base and FAQs: Internet-savvy customers leverage business/brands’ digital presence to find their way from the search engine platform to access information in a variety of forms (videos, landing pages, texts, infographics, illustrations, audiobooks, guides, and icons) for problem-solving. Dedicated FAQ pages that are brief and true-to-context is another form of self-service option that guides customers through specific customer service-related scenarios on the company’s website.
Integrated Contact Centers: Customer data is created across multiple channels. Integrated contact centers bind sales, contact center agents or other representatives with unified data sharing, collaboration and improved access to customer data across customer journeys and omni-channels for customer service. The intention is to boost CX and reduce the need to repeat information as customers navigate different customer service departments to solve issues on their own.
Interactive Voice Response (IVR) Systems: IVRs have existed for more than five decades. They are customer-facing phone systems that offer (inbound and outbound) support with pre-recorded messages and self-service menu responses to customers’ text inputs. They are cost-effective, scalable, and automated alternatives to human agents.
Chatbots: Chatbots use text-based or voice interfaces that are integrated to websites or mobile apps’ chat/message window to interact with customers. They are AI-driven and created based on the planned interaction flow chart to respond to customers in a matter of seconds.
Mobile Applications: Mobile apps with intuitive and interactive UX and UI give information to customers via dashboards, push notifications and updates in their moments of need, on their mobile devices.
Voice AI: A Quantum Leap in Self-service
The common forms of self-service options are the building blocks of the new age customer support. But there’s always the expectation for solutions that drive up the cost savings and operational efficiency while also helping brands’ contact centers meet their CX objectives. Imagine, if brands were able to achieve that while also offering self-service support that was voice-led, personalized, empathetic and proactively responds in real-time! For today’s automation and customer-first era, Skit.ai’s purpose-built Voice AI platform redefines self-service for optimizing customer support not only for better CX but for enhanced employee and business experience.
Built to enable conversations that are modeled on human interactions for prompt query resolution and personalized caller experiences, Voice AI is a next level of innovation in self-service. It delivers the best of voice experiences for brands through their contact centers that go beyond the capabilities of generic voice bots.
Voice AI is built to be domain-specific unlike generic voice-first platforms by Amazon and Google. The spoken language understanding (SLU) layer of Voice AI helps capture short, conversational utterances and is capable of deciphering semantic details that helps identify the right intent.
Voice conversations are the most natural forms of human communication and still remain one of the most sought after brand-customer interactions. Live voice conversations are critical to delivering high-quality customer experience. Customers interacting with self service options such as IVRS and messaging chatbots think before inputting a text command and hit send. Voice AI is a technology built to understand the intricacies of spoken language and not limited to text. It can quickly grasp customers’ voice interactions and filter through pauses and repetitions.
The Digital Voice Agents plug into the contact centers for automating cognitively routine work and independently resolving tier 1 customer problems. This would aid the human workforce to focus on more complex customer queries and contact centers to adopt intelligent human-machine collaboration. This way customers can stay in control and brands also get to pick the best self-service strategy for delightful CX.
Now, let’s dig into various features of Voice AI that makes it a better alternative to conventional self-service support:
Natural human-like Interaction: Digital voice agents that can mimic human-like conversations and comprehend interactions at a semantic level. It doesn’t feel like interacting with IVRs. It feels like holding conversations with the brands’ contact center agent.
Problem Recognition: Customers navigating through the self-service option can feel like they are lost in translation because of the complex IVR loops, limited menus or options that do not cater to their requirements. Sometimes chatbots are built with an ASR layer on top of NLP. They are great for transcriptions, not conversations. They deliver the same experience as going through a rigid IVR system. Digital Voice Agents can understand the right sentiment and nuances of human conversations, allowing the customer support to accurately identify and solve customers’ problems.
Always-on, Human Agent-free experience: One of the core value propositions of implementing a digital voice agent is its ability to function 24/7 for the ‘always-on’ customers without the dependency on human agents. This translates to cost savings by automating high-volume, zero-value and repetitive customer queries.
Quick Resolution: Self-service platforms optimized by powerful AI-capabilities and strong data sets based on customers data can be used for competitive advantage. It allows fast resolution, impacting customer satisfaction and CX.
Diversify Customer Service at a Lesser Cost: When more problems that are unique in nature can be handled by voice agents and automated, it helps brands’ customer service be a one-stop-shop for addressing customer queries at a fraction of a cost.
Smarter Human Resource Allocation: Self service options in contact centers make it easier to address trivial problems or anything that is repetitive in nature using Digital Voice Agents. Human agents can be allocated only for complex customer service issues, allowing for better resource planning and empowered customer support teams.
Make Self-service More human: Digital voice agents add a human touch to the overall experience without involving a human. The datasets are designed for SLU and built for domain-specific words which makes it easier to hold contextual conversation with the customers even via self-service options.
Hyper-personalize Customer Support: Brands can guarantee hyper personalization leveraging Voice AI’s extensive language support. It helps break spoken language barriers for enhanced query resolution and overall caller experience.
If you still have questions, refer to the infographic below for a brief comparative analysis between Voice AI and three most popular self-service tools.
Features
Voice AI
IVR Systems
Chatbots
Mobile Apps
Primarily Built for Voice Input
Yes
No
No
No
Analytics and insights Capabilities
Very High
Low
High
Low
Elasticity of Customer Service
High
Low
Moderate
Low
Hyper-personalized and Contextual dialog Capability
High
Low
High
High
Handling time
Lowest
Very High
Moderate
Low
Quick Query Resolution
Quickest
Slowest
Quick
Quick
Our Titbits
Envisioning customer service in the age of self-service is all about setting the right priorities. With the hope of keeping up with the trends for relevancy, brands and businesses need not steer away from their cost, profits and resource management goals. That’s the core objective of reimagining customer self-service using Voice AI. Brands across industries can supercharge their CX with befitting self-service strategies to be more result-oriented and insight-driven to add a competitive edge.
Our reflections for the future—customers never settle and self-service alone is not enough! Therefore, we believe Voice AI is the most robust and well-rounded technology to improve customer support capabilities that go beyond conventional contact centers, adding a desired level of autonomy and self-sufficiency in customer service.
Refer to our Voice AI page for more information on actively engaging with your customers and unlocking the power of self-service. Book a demo with one of our experts–www.skit.wpenginepowered.com
The U.S. economy has been shrinking, with many experts already pointing out that technically we have already entered a recession, as the economy has now contracted for two consecutive quarters. Fears of a recession have dominated most sectors of the economy over the last few months, and the ARM (Accounts Receivable Management) industry is no different.
The economy is slowing, inflation is high, and the Federal Reserve has been increasing its interest rates, and yet the data suggest that we find ourselves in a more complex and nuanced situation. Unemployment is still very low and the economy has been adding hundreds of thousands of new jobs each month, suggesting that it’s not all doom and gloom.
The latest reports, however, predict there will be a “mild recession” between 2022 and early 2023, with inflation being a major indicator of the direction the economy is headed towards, according to authoritative institutions such as Bank of America and Wells Fargo.
How can debt collection agencies prepare for a recession, and what do we know from previous economic crises that can guide us through the uncertain period ahead of us?
What Happens to Collection Agencies During a Recession?
Times of economic uncertainty are a mixed bag for collection agencies.
During a recession, the consumers who have the ability to be more conservative with their spending habits may choose to be more careful than usual. This might result in less borrowing. However, accounts might start increasing significantly as soon as the economy recovers.
On the other hand, for the people who already owe money, it might be more difficult to pay off their balances with jobs and income in jeopardy—leading to more defaults.
Additionally, the agency itself might need to take measures to cut down on costs. This may result in a reduction in staff, which will directly affect collection rates.
How Did the ARM Industry Fare in Previous Recessions?
Data gathered by the advisory firm Kaulkin Ginsberg shows how the ARM industry reacted to the last two major economic crises in the United States.
During the Internet bubble bursting — also known as the Dotcom crash — the ARM industry experienced a boom, with its revenue increasing from $8.2 billion in 2000 to $9.3 billion in 2001—a 13.4% increase. Between 2002 and 2005, the industry experienced continued growth at a similar rate.
The Great Recession of 2008, however, put the ARM industry to the test. The revenue fell by 14.4% from 2007 to 2009. Debt collection agencies struggled to collect payments, as consumers did not have the ability to pay off their debts. Many lenders scaled back their operations, leading to less accounts for collectors to manage.
And yet, just like in the previous crash, the years following the Great Recession were pretty good for the industry. While growth rates did not resemble the pre-recession boom, the industry still grew at an average rate of 4.16% per year.
5 Steps Collections Agencies Can Take To Prepare for a Recession
Optimize All Processes
To make your organization recession-proof, the first step is to optimize all of your internal processes and workflows. Analyze the existing processes and the customer journey and ask yourself: Can you identify any pain points? Where are resources missing and where are they abounding? Are there any workflows that can be shortened or reshuffled? Are there any tech tools to add to your stack that can help with any of the issues you’ve identified?
Invest in Agent Retention
Agent attrition in collection agencies is very high, and this creates additional expenses, as agencies need to cover recruiting, hiring, and training costs each time an agent leaves their job. Investing in agent retention is a must for agencies preparing for a recession. You want to keep your agents happy and make sure they don’t feel overly stressed or overwhelmed with calls. Consider adopting tech solutions that could take over some of the most repetitive and tedious agent workload.
Prepare for Account Volume Fluctuations
As account volume gets more volatile, agencies may experience more fluctuations in volume of outbound calls, needing more or less resources depending on the time. Agencies should develop a strong plan to address these scenarios; plan ahead even if you might not be experiencing this issue yet.
Offer Plenty of Payment Options
Once a customer is ready to pay, you should make it as easy as possible for them to pay using the method they prefer, including mobile payment apps. Collection agencies have started adopting PayPal and Venmo as payment methods, and the first data suggests that adoption is very successful.
The majority of Americans (79%) use mobile payment apps, according to a survey by NerdWallet. When looking specifically at the younger generations, the numbers go up: 94% of millennials use mobile payment apps.
Invest in Customer Self-Service
The existing data on customer service and customer experience indicates that customers expect companies to offer self-service customer care options. 39% of U.S. consumers find it very important to have access to a fully self-serve customer care option available to resolve their issues, according to a report by Emplify.
Self-service for a collection agency includes the ability to easily make payments and solve smaller issues by using the agency’s website, a mobile application, or an AI-powered Digital Voice Agent. More on that in the next section!
How Debt Collection Agencies Can Rely on Voice AI To Prepare for a Recession
Looking ahead and adopting technological solutions that can help you automate processes and improve the customer and employee experiences is one of the best ways to future-proof your collection agency, especially as we prepare for a likely recession.
Voice AI — AI-powered Digital Voice Agents to perform your outbound calls and collect payments from your customers — is becoming more and more popular and common among collection agencies in the United States.
In a recession, you not only want to save money, but you also want to ensure you maintain a competitive edge over your competitors. Looking into the adoption of artificial intelligence technologies that can automate your operations is key to securing a competitive advantage.
Automation: The Digital Voice Agent calls all of the customers on your portfolio and it then filters out the complex cases that need human agent intervention.
Coverage: The Digital Voice Agent can be scaled up according to the agency’s needs, so you can have optimal coverage of your accounts.
Recovery: The Digital Voice Agent can easily schedule follow-up calls, honoring the regulatory guidelines, spread over weeks/months, and ensure better recovery rates.
Compliance: Minimize errors and abide by existing laws and regulations by adopting a fully-compliant technology.
Cost and speed: Digital Voice Agents are efficient, effective, and cost significantly less than human collectors.
Customer experience: Offer a smooth and pleasant experience to your customers.
Scaling: Scale up and down as needed with just one click.
For more information and a free demo, you can schedule a call with one of our collections experts. We’ll be happy to help!
The need for digital transformation (DX) can hardly be overemphasized. The need for DX and automation is becoming more conspicuous in the debt collection space. Globally, companies are expected to spend a whopping $1.8 trillion on DX technologies, and what’s more incredible is that DX spending will sustain the momentum and grow at a whooping CAGR of 16.6% between 2021-2025 (IDC DX spending guide).
While the investment and gung ho surrounding DX are real, typically, companies find it hard to succeed at DX, and further challenging is to sustain that success. Only ⅓ of companies succeed at DX, and a much smaller fraction has been able to sustain that success. This blog focuses on one technology that has proven to have a high business impact, and success rates, while being easy and quick to deploy, i.e., Voice AI.
Debt collection space has not remained insulated from the recent tumultuous years. The industry is amidst epochal changes as challenges mount in 2022. The overall grim economic forecast, inflation, and frequent regulatory changes make it imperative for debt collection companies to transform. In 2010, U.S. businesses placed $150 billion in debt with collection agencies, who could collect just $40 billion of that total. On delinquent debt, the industry averages a 20% collection rate, a decrease from 30% a few decades ago.
Technology is the only potent tool capable of overcoming core challenges and transforming debt collection companies.
Ironically, 7 in 10 U.S. small businesses put off technology decisions and are invested deeply in day-to-day tasks, according to a 2021 study from Xero, a global small business platform. The implications of this are clear–companies will not be able to incorporate technology that is vital to their long-term survival. No wonder the majority of DX efforts result in digital grief. Hence the discussion on a technology that brings about quick and easy transformation is vital. But first, let’s deep-dive into the challenges that are crippling collection agencies.
As CXOs look forward to improving the performance of debt collection agencies, here are the core problems they are trying to solve:
Efforts have been made to solve these problems, emanating out of 8 core challenges:
What Digital Transformation (DX) or Being Digital-first will do?
DX is essential if a company wants to thrive in the long run. But it is a precarious journey, and only when prudent technology incorporation is done, it brings about positive outcomes.
For the same purpose, we delve into the nuances of technology that a debt collections agency can incorporate. Post transformation, debt collection agencies can leverage technology to be more agile, more efficient, and automate most of their processes.
Technologies Enabling Debt Collection Companies
We have classified the technology into two parts – Those that help in communication and customer support. and those that support the business function.
A. Customer Support Technologies
i. Voice-Based Conversation Technologies
We can look at voice-based technologies from a standpoint of their newness. This is important because most debt collection companies have to decide what to upgrade, integrate and replace.
Dialers and Telephony
IVRs
Voice AI or Voicebots
Voicemails
Voice Analytics
The larger discussion here would be about legacy systems. Dialer and telephone are very important and can be of great value if they are on the cloud. IVRs are still useful, but are equally frustrating, so a decision to either replace them or upgrade them is a big one. Voice analytics is a new and emerging tech, and debt collections companies will benefit if they leverage it. We will discuss Voice AI in detail, as the potential for value creation is incredible.
ii. Text-Based Conversation Technologies
They have been the oldest ones and have also been a part of legal mandates. These technologies are a significant part of interactions with customers, notifying them at the right time.
Chatbots and Text Messages
Using Email in Debt Collection
Text messages have been a vital part of debt collection as they are mandated by regulations. Chatbots are new and are improving rapidly, but since debt issues are complicated, it is not the favored go-to modality for problem resolution.
B. Technologies Supporting Business Function
These technologies power the business function of debt collection agencies and help them operate at better operational efficiency and agility.
Collections CRM for Debt Recovery
Debt Collection Compliance Software
Payment Gateways
They are very essential and can help debt collection agencies perform operationally better.
Analysis of Cost Structure
To assess the impact of technology, it will be necessary to analyze its impact on cost and revenue. Typically the cost structure of a debt collections agency is like this:
Even a cursory look at the graph makes it abundantly clear that wages are the most significant element of the cost structure, ranging near 42%. Hence a technology that helps debt collection agencies minimize this cost via automation will have a significant impact on the structure of collections agencies.
Call Automation via Voice AI
Voice AI is the most disruptive technology of our times since it automates the most expensive part of contact center operations – calls and conversations.
It is one-of-its-kind technology that can enable debt collection companies to make complete calls without requiring a human agent. Lately the Voice AI technology-based SaaS platforms have become quite affordable and quick to deploy. Hence are creating large competitive advantages for early adopters.
AI-enabled Voice Agents have been optimized to understand spoken language and strike intelligent conversations. The voice engine picks up not only what the customer is saying but also the semantics of the conversation.
Perhaps it is the most disruptive of all the present technologies as it is empowered to answer customer calls, and can reach them out independent of human agents. They are also excellent at updating customers and adhering to compliance requirements. They are proven to cut costs and improve agent productivity and collections rate.
Solving the Biggest Challenge – Automating Voice Conversations
A major chunk of the cost of a debt collection agency involves human agents’ salaries and similar expenses towards that end. Today, for the first time, companies have the technology to automate voice conversation and make calls possible without the need for human agents for as much as 70% of call volume.
We are in this section to delve deep into this new era of technology that will help companies transform truly.
The rapid rise in call volumes, defaults, demand for remote resolution of disputes, and diminishing CX have resulted in collection agencies scrambling to catch up.
The need for better outbound collections efforts—along with managing increasing volumes of inbound inquiries from customers—is putting pressure to scale contact center teams, an undesirable and herculean task.
Call center turnover (30 – 45%) has always been a challenge and has generally been twice as high as the industry average (13.5 – 18.5%), while collection agencies perform worse, with some reporting as high as 100% employee turnover. The concatenation of these factors—higher call volumes, regulations, and agent turnover—has made companies lookout for technology solutions such as Voice AI-enabled contact center automation.
Let’s compare the challenges collections agencies are facing to how a conversational AI-enabled Intelligent Voice Agent meets every challenge.
Beginning the DX Journey With Voice AI
Of all the technologies, the deepest impact has been seen with the deployment of Voice AI. This is because a major part of what a debt collections company does is conversations and automating them is going to create an unprecedented amount of value.
Once a voicebot or Voice AI agent is deployed, here is what that happens:
Automated campaigns with clear data documentation
Clear capture and documentation of the disposition/intent
No breach of compliance as the virtual agent stays true to script
Handing the same volume of calls with a much smaller human agent team
Improve compliance adherence by Voice AI strict adherence to scripts, timings, and regulatory changes
Immediate cost savings and revenue expansion
7 Reasons to Adopt Voice AI For Debt Collection
Augmented Voice Intelligence or AVI is the blend of Conversational AI and human intelligence. It creates meaningful conversations with customers to support them throughout their entire collection journey while adhering to compliance and regulations. Let’s delve deeper into the 7 core reasons:
Read in detail about these reasons in this Article
Here are a few outcomes contact centers have been able to achieve and are equally applicable to debt collection agencies:
Near 50% reduction in contact center operational cost:
Debt collection companies can work with a small team of human agents and handle the same amount of accounts. This is due to the automation as a majority of calls by Voice AI Agents.
The debt collection companies save on the hassle of recruitment and large wages.
The voicebot would also help companies cut down on agent commissions that typically range between 20-25% of the agent’s fixed compensation and is paid over and above the fixed component. This happens because the voicebot can enable payments without the need for human agents or does end-to-end automation. The higher the proportion of the payments the voicebot enables, the higher will be the saving on agent commissions.
Over 35% automation of customer support efforts:
For a debt collections company, the split–80% (Outbound) and 20% (Inbound) holds true. Let’s look into the proportion of call automation:
Inbound: Though it depends upon the number of uses the voicebot is trained for, at an evolved stage, it can handle as much as 70% of total inbound calls. Escalating only the complex cases to human agents. Also, even if the call is escalated, the voicebot will capture the intent and establish the right party contact before transferring the call to a human agent. This adds value and saves agent time, and this reduces the cost.
Outbound: Typically a voicebot will make multiple rounds of calls for the entire database before it can capture the soft PTP (propensity to pay). Only on the select accounts, the human agent will make the call. In many instances the voicebot does the job, in the same manner, a human agent would and thus creates value by replacing his effort. For instance, it can successfully establish:
Wrong party contact
Call back
Debt dispute
Reminder calls
Capturing disposition to pay
And more.
About 40% reduction in Average Handling Time
Overall companies across industries have observed a drop in average handling times. This is because even in most simplistic use cases the voicebot will verify the consumer, identify his/her intent, and summarize the interaction. This helps the human agent close the query faster.
Smoother Recovery with Better CX
Making the right call, to the right person at the right time makes a world of difference in collections space. Voicebot with its meticulous follow-ups, with the right message, can help customers make payments more conveniently. Hence companies see better recovery with better CX.
When going for DX, a piecemeal approach is the best. It is most prudent to start with technology with the biggest impact on the performance of the company and has the highest ROIs. But concurrently it must be easily accommodated into the current process with slight modifications. Voice AI possesses all the qualities, making it an ideal point to begin the DX journey.
AI-enabled Voicebots such as Skit.ai’s Digital Voice Agent thus has helped companies transform their contact centers with positive business outcomes.
For any questions on selecting the right Voice AI vendor and the technology, please schedule a meeting on www.skit.wpenginepowered.com
The debt collection industry is a heavily regulated space; the number of laws and regulations in place can be quite overwhelming. Whenever a new technology or solution emerges, therefore, it is natural to wonder whether it is compatible with the existing laws and whether the provider is fully compliant.
As more collection agencies look into adopting a Conversational Voice AI solution to automate their collection calls, it can be confusing to go through the regulations and determine which ones apply and which ones don’t.
In this article, we’ll unpack one important law — the Telephone Consumer Protection Act — and analyze its key provisions from the perspective of a Voice AI provider.
An Overview of the TCPA
The Telephone Consumer Protection Act (TCPA), first passed by the U.S. Congress back in 1991, is one of the most important laws that regulate telemarketing and the use of automated telephone equipment.
The TCPA is a law that governs and regulates all telemarketing calls, auto-dialed calls, pre-recorded calls, and unsolicited faxes. Though this law primarily focuses on protecting consumers from unwanted communications, it also governs and prescribes restrictions in the context of debt collection calls.
The TCPA authorizes the Federal Communications Committee (FCC) to exempt certain types of calls from its restrictions, including “calls made to residential lines that are not made for a commercial purpose, calls made for a commercial purpose that do not contain an unsolicited advertisement, calls from tax-exempt nonprofit organizations, and healthcare-related calls.”
Does the TCPA apply to debt collection calls? And how does it affect the use of Voice AI?
The Key Provisions of the TCPA and Collections
Calling Curfew
Solicitors can’t call customers at night time (indicatively, between 9:00 p.m. and 8:00 a.m.). However, the specific hours are determined by each state. For example, certain states do not allow calls on Sundays (e.g. Alabama, Louisiana, and Mississippi, among others). During the week, the starting time when calls are allowed varies by state—between 8:00 and 10:00 a.m. Calls need to be interrupted between 6:00 and 9:00 p.m. depending on the state.
National Do Not Call List
The National Do Not Call Registry was created to stop unwanted sales calls; anyone can register their phone number. Good news! This provision only applies to telemarketing calls. Luckily, debt collections do not qualify as telemarketing. However, if the customer explicitly asks not to be called, the collection agency needs to honor the request.
Self-Identification via Voicemail
If the collection agency wants to leave a voice message to the customer, then the collector must identify themselves and the agency and provide their telephone number.
Calling Mobile Phones
Nowadays, fewer people have landlines at home, and virtually everyone owns a cellphone. Still, the TCPA rules that callers can’t call a mobile phone without prior consent when using an automatic dialer, artificial voice, or a pre-recorded message. Therefore, one must obtain direct consumer consent prior to the use of these technologies to contact cellphones.
In the next section, we’ll see why Voice AI is not considered an automatic telephone dialing system (ATDS).
How the TCPA Impacts the Use of Voice AI
Over the years, there has been much confusion about what exactly qualifies as an automatic telephone dialing system (ATDS) under the TCPA. In 2021, the Supreme Court released its decision on Facebook v. Duguid, settling this long-standing uncertainty.
In a unanimous decision written by Justice Sonia Sotomayor, the Supreme Court established that an automatic telephone dialing system (ATDS) is a system or device that either:
Stores a telephone number using a random or sequential number generator;
Produces a telephone number using a random or sequential number generator.
Here’s why our Voice AI technology does not come under the purview of the ATDS definition under TCPA:
Skit.ai’s solution does not randomly or sequentially store or produce telephone numbers.
Our solution is designed to call the phone numbers provided by our clients, which are based on the accurate and complete databases of the clients, which enables Skit.ai to perform the services throughout the contract period.
Since our clients have prior express consent to send communications to the identified consumers, Skit.ai can communicate with the consumers on behalf of its clients through its Voice AI technology.
Please note that the information in this article is not intended to be legal advice and may not be used as legal advice. For more information and to request a free demo, you can use the chat tool below to schedule a call with one of our collections experts.
You’re not sure about Voice AI, you have some doubts, and you need some guidance? Are you wondering what a Voice AI solution can do for your company or agency; which risks are involved; and will this technology help you get ahead of your competition?
This guide seeks to answer all of your questions about Voice AI.
This is a unique ebook designed to enable informed and quick decision-making for debt collection CXOs — a comprehensive step-by-step guide for CXOs in the debt collection space to explore Voice AI technology and understand its core capabilities and the qualities of an ideal vendor. Additionally, we’ve included a section detailing the entire implementation process, from ideation to execution and beyond.
The ebook is divided into three sections:
Section 1: Fundamentals. In order to be able to take the informed decision, one needs to know about the product or services. This section contains the fundamentals of Digital Voice Agents, the tech behind it, and why it is important for the debt collection space.
Section 2: Selection Criteria. This section details the capabilities that a debt collection company must look into when considering Voice AI vendors. Several capabilities and complexities should be considered before making a decision.
Section 3: Implementation Guide. This section is a deep dive into the process of implementing a Voice AI solution, from ideation to execution, every step, in granular detail. This will prove vital in not only ensuring final success but also in time and ease of execution.
Section 1: Fundamentals of Voice AI
From its peak in 2009, consumer debt grew by $2.3 trillion to almost $14 trillion in 2019. In 2010, U.S. businesses placed $150 billion in debt with collection agencies but recovered a fraction, i.e., just $40 billion. The industry averages a 20% collection rate on delinquent debts, decreasing from 30% a few decades ago. Overall, the performance of debt collection companies seems to be facing major challenges.
Rapid changes in regulatory and customer experience expectations are taking place in the collection space and are posing serious challenges to collections agencies.
Typical Challenges
High number of untouched files: One of the third-party debt collectors has over 1 million files across portfolios, but because of the lack of human resource bandwidth, they are not able to reach out to all of them. Though they might send automated text messages to all of these, they know it’s not enough. They could actively pursue and call only 30-40K prioritized files with an outstanding balance of more than $1,000. The agency is not able to get any collection out of 960,000 files that are completely untouched.
High wrong party contacts: The menace of having a wrong contact number and associated problems is prevalent in the industry and is eating away the margin. Every call placed to a wrong party causes a financial loss for your business. These calls are simply non-value adding for any human agent.
High number of non-revenue-adding calls: Other than wrong-party connect, there are many other calls which do not add much value. For example, requests to dispute a debt, through an inbound call or outbound; another example is second-party contact or speaking with a customer who is busy and wants a call-back later. For an agency, any call that does not resolve in payment in the immediate future does not add much value.
Lack of persistent efforts and follow up: One of the most important things in collections is persistency. One industry expert argued that it requires 16 calls to reach an average consumer.Another industry expert, a large debt buyer, stated that, when searching for a consumer, it places between 50-75 calls per debt before achieving RPC.When trying to establish contact, consumers sometimes ask to get a call-back at a later time. After agreeing to pay, collectors have to remind the consumers on a regular basis. If your agents are not able to follow up persistently, collection rates are bound to go for a toss. And it’s humanly impossible to be able to follow such a strict schedule.
Compliance and script breach: Compliance requirements have become stricter. It’s essential for a collection agent to follow a strict script, be it Mini-Miranda, communication protocols such as 7-7-7, or keeping their cool after a bad day.
High attrition: Attrition in our industry is at an all-time high. One of our customers jokingly said that a McDonald’s worker earns more than a debt collector. Average attrition in some of the cases we’ve seen is around 200%, meaning that the average employee stays at the company for no longer than 6 months. With such high attrition rates, hiring, training, and employee-related costs have become extremely high.
Scaling up/down: At times, when you have a new portfolio or file, the workload increases. However, it’s not wise to hire agents only for such surge periods, so operations leaders end up deciding to work only with the available resources. This approach significantly reduces the sped of collections.
These issues ultimately result in lower collection rates and high collection costs.
Before we dive into how Voice AI solutions can help debt collectors, let’s understand the fundamentals of what a Digital Voice Agent is and how it works.
The Tech Behind a Digital Voice Agent
What is a Digital Voice Agent?
A Digital Voice Agent is an AI-powered conversational robot (commonly known as a voicebot) that has the ability to interact with a user and take a certain sets of actions to meet an end goal. It is very similar, but not the same, as voice assistants like Apple’s Siri, Google Assistant, and Amazon’s Alexa.
How is it different from voice assistants?
Voice assistants are designed to handle one or two turns of conversation to meet generic day-to-day tasks and are not designed to retain context longer than that.
Example of a single-turn conversation
Intelligent Voice Agents, on the other hand, are designed to solve specific problems which require much more than two turns of conversation, just the way humans solve queries by first asking multiple questions to understand the context and all the required information to solve a given problem.
For example, a lost credit card can be blocked by asking a series of standard questions. The first couple of questions are to verify the caller, and the next set of questions are to confirm which credit card should be blocked, and then followed by an action where the customer is issued and sent a new credit card. Typically, this is a 6-7 turn conversation that generic voice assistants are not designed to handle. Specialized voice AI agents are required to be built and trained to handle such tasks.
Digital Voice Agents sit on top of telephony and dialer systems. So apart from these two, fundamentally, there are at least five components (engines) to any voice bot:
Components of a Digital Voice Agent
ASR (Automatic Speech Recognition): This converts the voice into text transcription. This is alternatively called speech-to-text or STT Engine.
SLU (Spoken Language Understanding): This is the brain of the voice bot. It extracts intents and entities (data points) from the text sentence produced by ASR and then comes up with the best possible action. That action can be performed in terms of voice reply or sending a document or a text message, or transferring the call or raising a ticket etc.
TTS (Text to Speech): The block that translates the text into voice to generate a reply.
Dialogue Manager (Orchestrator): The block that manages the flow of data among the above three blocks and the flow of the conversation.
Integration Proxy: These are integration sockets that connects with CRMs, Payment gateways, Ticketing systems, etc in order for voice agent to be effective and efficient in end-to-end automation.
These processes happen in real time and within milliseconds. This is only one turn of the conversation and the process is repeated for subsequent turns.
All of these processes are performed in the cloud after the voice packets are received from a user. So it doesn’t really matter which device the caller is using—whether it’s a smartphone or a feature phone or a wired telephone. Skit’s Digital Voice Agents leverage all of these layers to seamlessly plug into contact centers and augment the work of human agents.
How are Digital Voice Agents different from chatbots?
Technically, an AI-powered voice bot has two extra engines that a chatbot doesn’t need. Since chatbots do not deal with voice, the two engines related to voice (ASR and TTS) are not required. The text input is fed directly to NLU and the intents and entities are extracted and the response is synthesized in text format and relayed back to the user.
Furthermore, voice queries on call bring with it certain challenges like noisy backgrounds, different accents and dialects of speaking the same language, language disfluencies and unique way of adding filler words and pauses, barge-in by a person while the other one is speaking; all of which directly impact accuracy.
And for the same reason, voice bots are much more difficult to build. Everything has to be real-time within milliseconds and there is little to no room for error, else communication experience is hurt.
What sets voice bots apart is that they’re faster. Voice is the quickest and most natural form of human communication—faster than typing or navigating drop-down menus with a mouse. It continues to be one of the most sought-after by end customers seeking support.
How Is Augmented Voice Intelligence Different from IVR?
What is an IVR?
Interactive Voice Response (IVR) is an automated phone routing system that interacts with callers and gathers information by giving them multiple choices via a menu. The system then performs actions based on the answers of the caller through the telephone keypad, which is also called DTMF (Dual Tone Multi Frequency).
IVRs are used by companies or contact centers to route calls based on the choices made by the caller in order to organize call queues of call centers. Through the caller’s selection, the system can determine if the caller wants to contact the billing department, the technical support team, or simply wants to talk to a human operator.
IVR in its backend is a top-down tree structure in which input from user determines which downstream node the call will flow to. End of the node can be either human agent transfer node or self-serve node. In case of self-serve node, a pre-recorded message is fetched from the database and played, for example, in account balance enquiry node, a pre-recorded message with be played along with a variable value, in this case fund balance.
IVR is also used to provide information like promos, updates, or other important information or instructions. One example is to inform callers that the system will record the call.
Lately, IVR providers have come up with voice response instead of DTMF. For example, to reach the billing department, the caller has to say “billing” instead of pressing a key on the the phone. This works on keyword matching. However, if caller utters a long sentence and doesn’t include the relevant keyword, IVR would not be able to recognize the input.
Typically, an Outbound IVR (Interactive Voice Response) is also used to reach out to a large number of customers in a personalized manner using different interaction channels, such as voice messages. The most common use cases are feedback, promotions, announcements, reminders, etc.
Robocaller or outbound IVR has essentially two components in it: (1) a dialer capability and (2) a text-to-speech engine (Advanced Outbound IVRs) or a recorded voice message (Robocaller). Businesses can upload thousands of contacts to the dialer and configure certain parameters such as number and time of retry attempts, time of call etc. The dialer calls these contacts and plays a voice message which consumers can listen to. At the end of call, the consumer can provide keypad based number input to listen to the message again and perform other tasks.
Limitations of IVR
In the 1990s this technology was a game-changer and led to a significant improvement in efficiency. However, today this system is ineffective and unnecessary, to say the least.
Even the most sophisticated outbound IVRs ail from persistent challenges as enumerated below:
Unidirectional Communication: IVRs are capable of only unidirectional communication with a limited DTMF (keypad-based) feedback mechanism.
Low Engagement: IVRs have extremely low engagement rates owing to their non-conversational unidirectional communication.
Right party contact: Inability to capture conversational inputs and run verification to check for right-party communication. Today, you cannot pass on debt related information to the wrong contact even inadvertently.
Lack of ability to capture important dispositions: Robocallers or outbound IVR can’t capture meaningful dispositions that can be used downstream, such as:
Willingness to pay, and expected date and mode of payment
Refusal to pay and associated reasons
Debt dispute and reasons
Willingness to pay partially and offer payment arrangements.
Ability to capture call-back date and time for busy customers.
Lack of insights for segmentation: Inability to segment the pool of consumers based on disposition to help debt collection companies make meaningful strategic decisions.
Inability to reach out to consumers on their preferred time: Since Robocaller cannot capture disposition for busy consumers, it cannot intelligently call back or arrange call back from human agents.
Payment assistance and goal completion: Cannot help or guide the willing consumer to make the payment during the call.
Human-Agent Dependence: For a large number of calls, human agents are needed to reach to a meaningful end result.
Compliance adherence: Since every call campaign is triggered manually, compliance is in the hands of the operator who is running the campaigns.
Customer Experience: Because this system is extremely impersonal, it miserably fails at contributing to CX.
IVRs, even at their best, do not contribute to CX or major productivity gains, whereas a bad IVR experience can prove very costly. The State of IVR in 2018 noted that 83% of customers would avoid a company after a poor experience with an IVR.
The more pressing problem still remains:
“How to automate the mundane, repetitive and non-value additive tasks human agents are doing”
For a long time, we did not have an answer, or we did not have a commercially viable technology solution, but today we have, and it is Intelligent Voice AI Agent.
Digital Voice agents are AI-powered virtual agents that allow customers to converse intelligently, without having to punch 1,2,3,4 on their screen to hold meaningful contextual conversation. It is able to converse with your consumers just like your human agents.
It is capable of understanding, interpreting, and then analyzing conversational voice input expressed by an individual and responding to them in an everyday language.
A Virtual Voice Agent goes beyond understanding words, and determines what the consumer is saying based on underlying semantics, without relying on specific keywords. Using machine learning, a Virtual Voice Agent is continuously improving itself and the customer experience.
A Comparative Look: Digital Voice Agent vs Outbound IVR
Section 2: Selection Criteria
Debt collection is not a simple industry. It is heavily regulated and involves a whole gamut of laws, which keep on changing. Additionally, it’s affected by the pressure to cut down on costs for the collection agencies.
For the first time, there is a technology that answers most of the challenges faced by debt collections agencies. Still, incorporating this tech presents its own set of risks.
Being experts and experienced in the debt collection space, we at Skit.ai have outlined a guide that helps CXOs understand what capabilities to look for when selecting and evaluating a Voice AI vendor.
Look for these core capabilities as you decide how to transform your debt collection business with Voice AI.
Deep Understanding of Business Operations and Processes
A voice technology company can have an impressive tech stack but may still not be suitable for you if they lack domain or industry expertise. They need to understand the nuance of the business and the consequences of conversations, reach out, and promises.
Why is it important?
A deep knowledge and understanding of business operations and processes in the collections space is essential, because debt collection is a complex, heavily regulated industry. Lack of knowledge is not only risky from a compliance standpoint; it can also hinder the creation of intelligent and intuitive conversation designs.
Designing a DVA is as much an art as it is a scientific and technical process.
The conversation with a consumer will be drastically different for a debt which is 30 days old compared to the one that is 5 years old, consumers might not remember the debt or card after some time. Conversation design will drastically change on various factor such as:
Nature of Debt: Knowledge of intricacies of different types of debt – credit card, healthcare, mortgage, telecom, etc.
Age of Debt: Knowledge of nuances involved with debt with different ages. A 30-day DPD debt is remarkably distinct from 180 DPD debt.
Conversation Design Capabilities: Is the vendor capable of managing the subtle differences and incorporating those in conversation designs.
If these factors are not considered, the end product would be suboptimal and end consumer will drop out of the conversations.
Consequences of lack of expertise in the area
Here are some of the issues you are likely going to run into if your Voice AI provider does not meet the aforementioned standards:
Higher involvement at every step: If they are not familiar with the business challenges and operations, they are going to reach out to you for every issue they encounter and seek help in designing flows.
Poor quality of voice agents: A voice assistant or agent can only be as good as its conversation designs. It takes humongous effort and time to create natural and intuitive flows that already understand the most probable customer queries and follow-up questions. Only an experienced voice solution provider can help you succeed in having a voice agent with a stellar performance.
Longer implementation time: There will be multiple to-and-fros as your vendor will come back to you asking for input every step of the process.
Internal resource time and effort: You expect your Voice AI vendor to do most of the work on its own, but that may not happen if there is a lack of expertise. You will end up dedicating a big team to help them design a functioning voice agent. This will disrupt your organizational functioning on an ongoing basis.
Higher cost: Longer implementation time, higher internal resource involvement, and higher need for testing will ultimately culminate in a higher cost for you, both directly and indirectly.
Ability to Handle End-to-End Automation
You should expect your Digital Voice Agant to have the capability to deliver end-to-end automation. In other words, they must have the capability to handle calls from start to finish without the help or intervention of a human agent.
Why is it important?
These days, AI-powered Digital Voice Agents should be capable of handling conversations end-to-end. It would be limiting to use DVAs only for call routing and to identify right-party contacts and transfer calls to human agents.
On average, 70% of customer requests fall into the tier-I bucket; this means that a Voice AI agent must be able to automate, End-to-End, a majority of calls.
This is the most vital capability of a Voice AI solution as entire value creation, productivity enhancement, and business performance rest on it.
Imagine the kind of value that can be created by taking away more than 70% of frustrating calls your human agents are handling.
Here is a list of a few capabilities that augment End-to-End Automation:
Capability to collect payment on call
Debt dispute handling (end-to-end)
Sending digital validation
Identifying RPC and WPC
and more.
Consequences of lack of expertise in the area
What happens if the vendor you are speaking with does not have a high-end-to-end automation capability?
Impact on scalability: We know that maintaining a large human agent team is a painful task. The highest attrition rates, not only make it an operational hassle but also escalate the costs to retain them, and keep them engaged and satisfied. With End-to-End Automation capability, Voice AI technology is minimizing your reliance on human agents. You do not need to recruit more when call volumes surge, nor do you need to have a larger team if you want to deal with a bigger portfolio of delinquent accounts. Let’s compare to make the point crystal clear:
Vendor 1: End-to-End Automation capability of 70%: You need human agents for just 30% of complex calls. This means 24/7 majority of your customers will be able to solve their problems instantly, without IVRs and then to human agents. You need to keep a minimal team, a happy team that will work even better as they are now not dealing with interesting and value-creating calls. This has a lasting positive impact on cost structure, HR costs, and other indirect costs.
Vendor 2: No End-to-End Automation: Though the Voice AI agent will be able to identify the right-party, you will always need human agent for every call as call is transferred from DVA to a human agent. This means you will always need human agents for DVA to realize the value since there is no end-to-end automation.
Platform Approach for Rapid Roll outs and Time-to-Market
A platform approach has its typical advantages. Cloud-based modularity makes enhancements and tweaks very easy.
Why is it important?
A platform gives visibility into the system, and for many elements, the adopting company can have the option to tweak things such as conversation flows to better voice agent performance. Additionally, it is easier to deliver upgrades and enhancements collaboratively and transparently.
For instance, Skit.ai offers access to the Skit studio platform, which gives its clients a comprehensive view into how things are moving along. This makes the entire BTDME — build, test, deploy, monitor, and enhance — journey significantly smoother.
Having a user-friendly platform also helps with the integration of third-party applications such as payment gateways, CRM, and other business applications. In the long run, these capabilities can be the difference between winning and losing.
Consequences of lack of expertise in the area
The lack of a platform converts the Voice AI solution into a black box. You have no idea about its functioning, and you will depend on your vendor for everything. This will not only elongate the enhancement process but will also make it costly.
More often than not, time is everything. Consider the damage a wrong information-based conversational flow can do if not updated in time. The compromise on agility is severely debilitating for any company sensitive to CX and changes in consumer behavior.
Compliance Expertise and Experience
Everyone in the debt collection space is aware of Reg F. and the challenges it posed to debt collection agencies as they work to understand the implications and ensure proper compliance. If your vendor does not have the required knowledge and expertise on compliance and regulations, the consequences can be problematic for your agency.
Why is it important?
Leaving alone the increasing fines and penalties imposed by the regulators way more significant are getting involved in lawsuits and court battles.
Companies must seek a vendor who knows the law in and out. Considering the direction of regulations going stringent by the year, the significance of expertise in this area can not be hyperbolized.
Various tasks such as data scrubbing are difficult for a human agent but a breeze for Voice AI and can prevent a potential lawsuit. Furnishing statutory information such as Mini Miranda or relating to other laws is easy for voice AI agents, but your vendor must have the in-depth expertise to train the voicebot for it.
Consequences of lack of expertise in the area
There are two significant disadvantages if your vendor lacks in this area:
Lost Advantage: One indisputable fact is that Voice AI Agent is better at ensuring compliance. Human agents are prone to err and engage in false promises and indecorous use of language. A state-of-the-art voice AI agent makes compliance adherence bulletproof. But if your vendor is conversant with regulations you not only run the risk of breach of compliance but also you miss out on one of the biggest advantages associated with voice AI agents.
Cost Implications: Running into lawsuits costs companies dearly that are already dealing with thin margins.
Business Performance: Faltering at one regulation, or one lawsuit puts the entire company on a backfoot and triggers introspection which slows down the entire business.
Looking into MLops, capabilities are essential as they have a lasting impact on the performance and competitive edge.
Why is it important?
At the core of Voice AI lies the capability of the algorithms to learn and improve as more and more conversations are fed into it.
The more extensive this capability, the more robust will be the learning gains, and the ability of the system to improve the conversations.
Consequences of lack of expertise in the area
The absence of AI/ML or only feeble attempts at it has severe consequences because as companies who are updating their AI/ML models, regularly feeding more and more data will create superior conversations, and will augment their capability to handle conversations.
Technology Ownership
This means having a proprietary technology stack and not relying on open source technologies.
Why is it important?
A score of reasons are there for you to look for proprietary technology.
Process Efficiency: If a Voice AI company is using its own tech, they have labored hard to optimize it, as well as the integration they are using. This enhances the overall performance to a great extent and makes a world of a difference.
Constant Improvement: Having ownership of the tech stack helps in rapid improvements and releases.
Safety and Security: For a sensitive industry such as debt collection, safety and security are of grave importance. Having tech ownership enables companies to have greater control over the flow of data.
Control: It is as simple – we can not control what we don’t own.
Consequences of lack of expertise in the area
Lack of tech ownership has many negative consequences. It slows down the entire process. Also, your vendor will not have control over the process because it is using many third-party integrations, and failure at one will cause the failure of the entire process.
In essence, the entire experience is compromised because of inferior performance if the vendor does not have ownership of the core tech stack. Every company uses integrations, they are the best ways to scale capabilities, but it should not be the case for the core tech stack.
Actionable Analytics and Dashboard
A unified view of the entire process and the ability to analyze and have actionable insights.
Why is it important?
Every conversation is a potential treasure trove of value. Companies must not waste such valuable resources and an ideal vendor must possess the capabilities to draw insights from data such as dispositions.
Look for capabilities such as bucketing dispositions into meaningful buckets, forwarding disputes to select departments, and more.
A dashboard to monitor the effectiveness of conversations is an essential feature. Also, analysis of AHT trends and more are a must.
Consequences of lack of expertise in the area
We can not improve that which we can not measure. Not having the capability to run analytics will impact business performance improvements and will lead to competitive losses.
Section 3: Implementation Guide
In this section of our guide, we’ve compiled a list of essentials to help your company properly onboard your chosen vendor and implement their Voice AI solution for debt collection.
Have the vendor sign an NDA (non-disclosure agreement)
In order for the DVA to be effective, you will have to share a lot of information for your vendor to be able to understand the consumer persona. Always sign an NDA before sending any documents or sensitive information.
Form a steering committee and assign Single Point of Contact (SPoC)
Ensure to have a focused approach to incorporate the Voice AI from the very beginning of the process. A steering committee can have a mix of expertise from technology to business, operations, and HR.
Always pilot and follow a lean approach in pilot
This is of serious importance. Lean means that your pilot should be undertaken in such a way that your organization gets disturbed in a minimal manner. Avoid unnecessary integrations that will increase the load and complexity of the pilot and can affect the results in a complex way. Also keeping it lean will minimize your and your team’s involvement so that your sunk cost in terms of time investment is low if the project goes south and doesn’t bear the fruits.
Pilot the biggest segments you handle
Going all out is not the best strategy here. Segment the portfolio you are handling in terms of volume and value. Prioritize 2-3 different segments for the pilot and provide representative call recordings for your vendor to understand the consumer persona. Also help your vendor with call dispositions i.e. different kind of flows your typical calls end up in, for example, percentage of calls that are wrong party, debt dispute, cease communication requests etc. This will help your vendor plan the development strategy.
The Voice AI agent will be as good as the information you feed it. It is essential that you provide to the vendor all the essential information, e.g. if you have 12 types of customers, then provide the audio recording of each type of customer. Failing that will result in poor conversation flows that are designed for only a few types of customers.
Additionally, the number of files shared is also important to help in the training of the voice agent. It is best if you share actual conversations in large volume so that it makes ML models better.
Review the call-flows
After reviewing the call recordings, your vendor should be able to come up with the conversational design, call flows, and scripts. Once your vendor is ready with conversation designs and flows, it is crucial that specialists from your organization review and help them refine the those. This step will have a lasting impact on DVA performance.
Stress-test the DVA before rolling out
A lot of people delegates the UAT (User Acceptance Test) tasks to junior resource or ignore all together. It’s the worst mistake to make especially in the debt collection space where one small mistake can be costly. It’s important to stress-test the DVA built by the vendor before deploying and rolling out for customers.
Pilot on as many consumers as you can
You can pilot on 100 calls per day for a week and decide to go for the full-scale implementation. However, for an AI solution, 100 calls are not a representative enough sample, especially for debt collection applications. In case of outbound, 80% of the call might go unanswered, so you will be left with 20% of the calls to test the bot. If you pilot on 20 calls per day for 5 days, you have piloted only on 100 calls, which might not be a bog enough datapoints to base your decisions on.
At Skit.ai we recommend at least 10,000 calls/day for about 4 weeks.
Calculate ROI for Go/No-Go decisions
You must run an ROI exercise, to understand what quantum of value the Voice AI solution will create for your company before moving any further.
This exercise must be done for one year period, ideally for 2-5 years. The variables involved are simple – call volume, cost of the human agent, cost of deploying voice agent, number of integrations, inbound/outbound, call complexity, and deployment type. Your vendor should be able to provide you with notional value creation/cost savings.
Value creation is not as simple:
Higher levels of voice automation will lead to higher augmentation of human agents – productivity, efficiency, and engagement
More top line as the same set of agents will now handle a larger number of accounts
Better recovery rates as the voice AI agent will be more persistent in collections
Better disposition capture for precise campaigns
Time-bound campaigns and 100% coverage on all accounts
You may choose to factor in direct and indirect benefits out of voice AI deployment.
A lot can go wrong here, so it’s better to be aware of the risks of lack of proper technology architecture planning.
Be clear about the call volumes you expect over the years because you need to assess the supporting tech infrastructure around it. Relevant integration, legacy telephony assessment, CRMs, gateways, and more must be assessed and optimized for minimum human interventions and sufficient to last the planned phase.
It must be duly noted that running a Voice AI solution is a process, a continuous journey filled with improvements and upgrades. In order to sustain and be further along the learning curve, training the Voice AI solution on new data is vital.
Upgrades and Training for Sustainable Competitive Advantage
New use cases, business verticals, customer regulations, and more — we live in a dynamic world, and constant effort to innovate the voice solution are essential for being at the top of the game and beating the competition.
Conclusion
It is essential to assess a voice solution in granular detail before moving forward with it. We hope this guide will help you in your buying journey.
For more information and a free demo, you can schedule a call with one of our collections experts. We’ll be happy to help!
Let’s begin by addressing the elephant in the room—the collection rates have dramatically fallen in the last decade. The State of Debt Collection 2020 Report reveals that in 2010, U.S. businesses placed $150 billion in debt with collection agencies, of which they could collect just USD 40 billion. On delinquent debt, the collection rates have declined to 20% (industry average), a decrease from 30% as recorded a few decades ago.
Anyone from the debt collection space would be cognizant that the industry has been under pressure from all fronts—inflationary pressures, agent attrition further fuelled by the great resignation and increasingly stringent regulations after Reg. F, and economic downturn.
Never before was the need for automation direr than it is in 2022!
What is Voice Automation for Debt Collection Companies?
Before we go into the transformative role Voice AI can play in the debt collection industry, let’s understand voice automation.
Voice Automation –Refers to the automation of voice calls, decoupled from the assistance of a human agent. This means the capability to answer customer queries with the machine, striking intelligent, multi-turn conversations.
Consider this scenario where an AI-enabled Digital Voice Agent interacts with a customer and facilitates an on-call payment.
The demo is a perfect example of how an intelligent Voice Agent can help consumers willing to pay and facilitate a quick payment with remarkable ease.
Outbound Call Automation: A Digital Voice Agent can call consumers and establish the right party contact, remind them about the due date, capture their dispositions, raise dispute requests, accept and schedule a payment on call, or help them negotiate, and arrange a payment plan for a better recovery.
Automating Tier-1 Inbound Consumer Queries: The Voice AI agent can answer tier-1 calls, which are as much as 70% of total inbound calls, without the need for a human agent. Also, even when a Voice AI agent calls customers, it can answer all basic questions and handle tier-1 queries discussed in the outbound section above.
Hitherto, only IVRs have played a limited role in increasing the containment rate with the self-service option. IVR’s effectiveness can be debated, especially when reports have revealed that it plays a role in decreasing customer experience. The problem with IVR technology is that they have reached the culmination of what it can do for debt collection agencies. It is time to move beyond IVRs, especially when tech advancements have brought us to the sweet spot of cost-effective incorporation of AI-enabled solutions such as Voice AI.
Addressing the Elephant in the Room: Core Debt Collection Challenges
Debt collection companies face these core problems:
Dormant Files: Every debt collection agency sits on a pile of inactive accounts, as high as two-thirds of their portfolio, that they can not process because of its economic infeasibility. This is a sour point, and they are looking for tech solutions that can help them address this pain point.
Non-Revenue Generating Calls:
Wrong Party: Proportions of wrong party contacts vary depending upon many factors, such as the age of debt, but it can be as high as 70-80%. All the calls made by human agents that turn out to be wrong contact numbers are pure costs with no return.
Dispute: The next big chunk of the volume of calls is usually when a consumer fails to recognize the debt or disagrees with the outstanding amount. The regulations require debt collectors to raise the dispute request to investigate the debt further and provide relevant information to the consumers before any collection activities. Those calls where debt is disputed by the consumer or asked for more details of the debt eventually turn out to be a pure cost activity.
Cease-and-Desist: Be it inbound or outbound, there is always a set of consumers who ask agencies to stop all collection communication with or without any good reason. There is no real scope of value creation by a human agent in this case as well.
Attorney Representation: Often, the consumers ask to contact their attorney and not to approach them directly. All agents do in this case is update the system to not reach out to these sets of consumers as required by regulations.
Call Back Requests: More often than not, the consumers ask the agent to call some other time, in some cases beyond the working hours of the agency.
Right-Party Contact (RPC) Cycle: Traditionally, a human agent will call consumers to establish if the contact number is correct. Any debt collection agency has so many files to process that they can only call a fraction of them within a time frame, and take long to cover all consumers, if at all. The shorter the cycle, the larger would be the scope to improve recovery.
Propensity Based File Segmentation: Ideally, a debt collection agency would like to segment their portfolio into different buckets based on the consumer propensity to pay. But sadly, with a large number of files, it is challenging to do this within a limited time frame, if at all.
Agent Bandwidth Optimization: Agents are the most precious organizational resource and their time/bandwidth optimization is an utmost priority for them. But in absence of RPC and disposition capture, it is near impossible to optimize their time and effort.
Service Level Maximization: The number of calls a debt collector addresses per agent per hour is vital for enhancing operational performance.
Compliance: The regulations have become increasingly stringent; this has two implications:
The penalties and fines are levied at instances of breach of regulations. They are typically bearable expenses, though they affect profitability.
Lawsuits filed by consumers: They do real damage as they consume time as well as cost, and are typically much higher than government fines and penalties.
With the coming of Reg. F, a new conversation has begun on the compliance of new-age technologies. Being AI-enabled and capable of striking an intelligent multi-turn conversation, Voice AI finds itself better placed to meet compliance. (read more about it in this Voice AI compliance white paper by Mike Frost and Skit.ai).
Voice AI is based on AI/ML, Automatic Speech Recognition (ASR), Spoken Language Understanding (SLU), Text-to-Speech (TTS) technologies, and more. A confluence of such great technologies enables Voice AI to understand the spoken word and respond to it most intelligently. Here is the gist of how a Voice AI Agent can create value for debt collection agencies:
Segregating Right and Wrong Party Contacts:
With the great capability for executing thousands of concurrent calls, Voice AI can call and establish the right or wrong parties in a matter of minutes, for a significant portion of files. No technology has been able to accomplish this except Voice AI.
Value Creation: At a fraction of the cost, a debt collection company can identify if the contact is right or wrong without involving their human agents. Time and cost advantages can help them improve performance in a big way.
Enabling File Segmentation by Capturing Disposition:
Classifying customers into 4-5 different segments solves a lot of problems for the collection agency. A Voice AI agent can call thousands of customers and, based on dispositions, can segment millions of accounts into various buckets such as consumers who disputed the debt, consumers with cease-and-desist requests, consumers with attorney representation, consumers who agreed to a payment plan, etc.
Based on this segmentation, accounts can be allocated to respective specialists and departments for further processing.
Value Creation: Only after capturing the disposition for the entire portfolio, the company will be able to draft an optimal strategy and optimize the time spent by their agents.
Compliance Adherence:
Since the coming of Reg. F, the compliance has become difficult to keep and the corresponding implication of its breach is getting higher. With large portfolios, it is difficult for agents to execute their follow-ups with perfection.
Mandatory rules such as the 7/7/7 rule, along with Mini Miranda, use of decorous language, and more, make it difficult for the human agent to always stick to the script especially when a majority of calls are repetitive and low-value.
Value Creation: Voice AI agent, once trained for compliance will always stick to the script, and use the right language. It will also stick to the schedules of follow-ups increasing the probability of conversion as well as saving the company thousands of dollars in fines and lawsuits. This also increases the speed of the company processing its portfolios.
Value Out of Non-Revenue Generating Calls:
Voice AI, at a fraction of the cost – 1/6th, can process these calls (mentioned in the above section) and help human agents avoid these and focus on value-creating calls.
Voice AI, with its consummate coverage of debt portfolio, can help debt collection companies have a more precise understanding of their consumers and devise better strategies. Below is a graph that depicts VaR and the ideal file segmentation and corresponding strategy.
Strategizing with Voice AI
Age of Debt and Voice AI: A debt collector will typically have a mixed portfolio with debt lying in various age brackets. Typically the older the debt, the lower the probability of recovery, and hence Voice AI is more suited to engage with these accounts.
It must be noted here that the segmentation is only possible after the Voice AI Agent covers the entire portfolio to uncover consumers’ propensity to pay.
Capturing Propensity to Pay: Once the Voice AI Agent has captured the disposition of the consumer, a debt collector can then segment or classify it and assign it according to the disposition.
Strategizing for Value at Risk: Since Voice AI costs one-sixth of a human agent, and is as effective for simpler conversations, it is ideal for Voice AI agents to address these accounts and follow up meticulously.
High Willingness to Pay (WTP) – High Value: When the willingness to pay is high, the voice AI agent can call promptly and facilitate on-call payments or remind them to pay. Debt collectors have been able to achieve a high degree of success in this category.
High and Low Willingness to Pay (WTP) – Low Value: This segment of the portfolio is prohibitively costly for human agents to process because of its low value, making it ideal for the voice AI agent to process it and help prop up recovery rates.
Low Willingness to Pay (WTP) – High Value: High value and low willingness to pay makes this segment of consumers ideal for human expertise. Human agents can deploy their cognitive skills to convince and help them pay.
Optimizing Campaigns:Armed with new insights on consumer behavior, debt collectors can refine and optimize their campaign strategy. An ideal mix of Voice AI, human agents, and SMS/emails, can make a difference.
Impressive Contact Center Outcomes with Voice AI
No, the capability of Voice AI is not just based on conviction and hope, there are solid stats to second every value proposition.
It must be noted that the higher volume that Voice AI Agent handles, the greater the scope of value creation. This is necessary if a debt collector wants to strategize based on consumer disposition.
Here are a few outcomes that the debt collectors as well as other contact centers have achieved with Voice AI:
Up to 38% improvement in service levels
Nearly 50% decrease in operational costs
Up to 70% automation of your consumer support efforts
Reduction of 40% in average handle time
Conclusion
There are multiple challenges from diverse fronts plaguing the debt collection companies. They can break the status quo and make the necessary changes on many fronts such as cost, performance, recovery rates, compliance, and speed.
Voice AI technology has been successful in value creation for debt collection companies. However, it takes an expert vendor and meticulous execution to achieve desired results.
To further understand the nuance of Voice AI and the scope of transformative value it can create for your business please – Book a Quick Appointment.
The world and Indian economic growth find themselves precariously placed in 2022, with bleak economic data raising concerns. People are reeling under the increasing cost of living, and rising defaults on loans are beginning to be a worrying trend. The rise in default rates was seen in every segment, without exception, with Loan Against Property (LAP) having the highest default rate at 4.14% in November 2021; loans due past 90 days in the two-wheeler segment are also high at 3.64%, up 140 basis points, while consumer durable loan delinquencies are the third highest. Delinquencies in the credit card segment have eased sharply by 77 basis points to 2.22% (data from credit monitoring agency Transunion Cibil). Also, the spike in fund disbursement will increase the asset size of NBFCs, while the GNPA rate will also increase significantly to 6-8%.
On account of inflationary pressures, and a slowdown in the growth of fresh credit, the situation is becoming challenging for companies in the debt collection space. To maintain profitability, NBFCs are faced with the dual challenge of improving their performance, while improving cost-efficiency at the same time. This calls for a different approach, leveraging technology such as Voice AI to automate a significant portion of their outbound customer reach outs.
In this blog, we shall explore the role of Voice AI as the ‘agent of change’ in the growing debt collection space, and why the shift to Voice AI is one of the most profitable moves NBFCs and debt collection agencies can make in 2022.
Voice AI Is Redefining the Future of Debt Collection
Customer experiences are critical to the brand and business performance agree 73 percent of the business leaders, suggests a study by the Harvard Business Review. The global proliferation of voice-led technologies and voice-assisted interfaces built on AI-based NLP across industries have set massive expectations in the way customers prefer digital interactions and engagements. IVRs have proven to decrease CX, and bulk robocalls have proved ineffective. This is of significance in debt recovery, because as companies lose time, the probability of recovery dwindles.
On the other hand, AI-enabled voice agents are capable of engaging in meaningful conversation that runs beyond generic reminders by gathering insights and feedback that may facilitate on-call payment, rescheduling, and dispute resolution.
Money talks are uncomfortable, Voice AI exactly helps debt collection agencies achieve that, allowing human/machine partnership, the future of intelligent work.
Human-Machine Partnership: Voice AI platform built specifically for the debt collections industry also helps automate voice conversations while enabling context transfer capabilities from across modalities (text, chat, email, and speech), empowering the agents to operate without burnouts whenever call volumes peak. Automation of cognitively routine work also allows more time for contact center agents to prioritize their bandwidth and use it for solving complex challenges without the need to upscale the team.
All in all, integrating Voice AI can help create three ideal scenarios—NBFCs can improve their Collection Efficiency Ratio while reducing the cost and even improving customer experience!
Skit.ai’s Augmented Voice Intelligence in Action
Challenges Facing Indian Debt Collections Companies (NBFCs)
These are challenging times for NBFCs trying to improve their recovery rate. As CXOs look forward to improving the performance of the debt collection agencies here are the core problems they are trying to solve:
Intensifying competition is putting pressure on profitability
Low Collection Efficiency Ratio
High Cost of Collections
Slower campaigns and limited customer coverage
Before we deep dive into how Voice AI can solve all the major challenges, let’s first look at the definition of Collection Efficiency Ratio.
Understanding the Performance of a Debt Collection Agency or an NBFC
The entire performance of a collection agency can be expressed to a large extent by these metrics:
Collection Efficiency Ratio
The collection efficiency ratio is a measure of how well the collection department collects debt. It’s essentially a way to determine the effectiveness of the collections team.
The collection efficiency ratio is calculated as a percentage that depicts the proportion of debt collection achieved out of the total portfolio. The higher the percentage, the better the debt collection performance of the company.
How to Calculate the Collection Efficiency Ratio
The total collectible amount for month X – This includes the overdue at the start of the month as well as all the due dates throughout the remainder of the month.
Remaining recovery amount for month X – This is the amount remaining on a specific day that the team failed to collect.
A higher percentage depicts greater success in the collection of debt, ie., better debt recovery.
How Important is Collections Efficiency Ratio for NBFCs?
The collection efficiency ratio gives an overall monetary recovery status. Irrespective of the number of accounts recovered, at the end of the day, the Rupee value counts.
It is also a good measure because it gives a clear picture of value at risk (VaR) which is the most important thing to monitor for a debt collection agency.
Age at List (AAL)
Time is important when it comes to debt collection. If your account is 7 months old, its recovery rate drops to 50%. After 12 months, it drops to only 25%.
Therefore, Age-at-List is one of the most important KPIs in the collection. This is the average number of days your account has been in expired status. AAL provides a general overview of the collection cycle. This is an excellent comparison indicator between collection agencies and the industry.
Successful agencies are aiming for a low AAL. A high AAL indicates that the agency needs to be more effective in debt collection. However, AAL tends to fluctuate. Therefore, you need to review the data for about a year to gain valuable insights.
The Digital Voice Agent can be trained to pursue B0 and B1 buckets and will be very effective with its precise regime to convert the default accounts. This will lower the age of debt and improve the performance of the company.
RPC rate (Right Party Contacts)
The RPC rate is the first of the more specific metrics in this list.
This KPI measures the ratio of all outgoing calls to a valid phone number for the person (or “right person”) for whom the collection was requested. For collectors, the higher the score, the better the success rate of finding the debtor.
Of course, the first step in collecting claims is to find and contact the right person. If your company has a lower RPC rate than its competitors in other industries, you need to think carefully about what’s wrong and how to improve them.
Percentage of Outbound Calls Resulting in Promise to Pay (PTP)
The PTP rate is just as important as the RPC rate when measuring efficiency and is the next logical step to a successful collection. It measures the percentage of all calls that end with the debtor’s promise of payment. That is if the RPC rate measures the success rate of dialing the appropriate person, this metric measures the success rate of those RPC calls. This is another percentage that you want to get as close to 100 as possible.
Voice AI agents can help companies with lower PTP figures buy prompt calls for which the best agents can train the voicebot whose perfect timing and schedules will help improve the PTP stats.
Profit per Account (PPA)
Finally, PPA measures how much profit each account in your collection makes on average. In short, this KPI measures the impact each account has on revenue.
This metric is calculated by dividing the company’s gross profit (calculated by subtracting total operating expenses from total revenue) for a particular period by the total number of overdue accounts managed for that period.
A Voice AI Agent can help reduce the operational cost to the tune of 50%, along with a faster sales cycle, improving this performance metric.
Improving these numbers is a big challenge and we will now go into detail about how Voice AI can help debt collection agencies and NBFCs transform their performance.
Transform Your Collections Efficiency Ratio with Conversational Voice AI
The effort of a collection agency is to collect as much as possible and as fast as possible from every account, i.e. a higher recovery amount with a shorter collection cycle will improve the majority of performance indicators mentioned above.
Voice AI Core Benefits for Debt Collection Agencies and NBFCs:
Voice Automation: Voice AI will help your company by automating 70% of the calls, primarily tier-I calls, and by automating payment reminders and collection calls at B0 & B1 buckets. This would cut down the time & effort of human agents and they can focus on RTP cases. This would reduce the burden on human agents and improve the use of their time on meaningful and complex problems.
Augmenting Agent Productivity: Since the human agents focus just on RTP cases that require more empathy and intelligence, their efficiency and productivity take a big leap. This has an impact on CX as well as recovery rates as agents are not wasting their time on trivial tasks.
Cost-Efficiency: Be it collection calls or running outbound campaigns for reminders or notifications, etc., the Voice AI Agent can do everything at a fraction of the cost. This has long-term and big benefits for the company.
Quality and Compliance: The Voice AI Agent never fails to follow proper protocols and thus, avoids any potential legal challenges. Also, its delivery is always the same and hence its service quality does not deviate with mood, as with human agents.
9 reasons why NBFCs and Debt Collections Agencies Should Not Miss Out on Voice AI
Allowing human agents to focus on RTP (Refuse to pay) accounts. Thus increasing the profitability by converting high-risk accounts.
Automate simpler calls like reminders & FAQ – Proactively reminding debtors at the right time and helping them with FAQ related to their loan/upcoming payment. plays a far bigger role in collections and Voice AI does it perfectly.
Improve contact-ability & customer coverage – Voice AI is capable of contacting millions of customers in a matter of weeks. Thus the company can reach out to every customer for payment reminders, follow-up on DPD cases, and assist with queries related to the loan.
Shorten collection cycle – Faster reach outs and quicker conversion with Voice AI Agent helps shorten the collection process.
Persistency in follow-ups and call-back requests – Human agent may err in follow-ups but the Voice AI agent follows up as well as calls back at the requested time without fail. This is a big help and improves collections.
Capability to handle spikes in volume – Traditionally the call center teams are unscalable over short periods, but with Voice AI, this is not the case as it can handle any spike in call volumes.
Here are some other unique capabilities of conversation Voice AI for debt recovery:
Feeds data to the CRM tool and provides analytics for further action
Persuades customers to pay at the earliest, offering payment plans and options
Helps agent plan post-call follow-up campaigns with Voice AI Agent
These unique capabilities prove indispensable and give the debt collection agency or the NBFCs a big edge over their competitors.
How a Digital Voice Agent Adds Value to Collections Efforts
The Digital voice agents are highly effective at B0 & B1 collection buckets with no human assistance. The later bucket requires a bit more contextual conversation and dunning from the collection agent.
The digital voice agent gets a custom design flow to interact with customers at Bucket Zero and Bucket 1 for debt collection. At bucket Zero, the Voice AI gives out a “The last day of payment” reminder, requesting the debtor to maintain the payment amount balance in their account for auto-debit. The Voice AI also assists in converting manual payment to auto-debit.
At bucket one (1-30 days), the debtors are expected to pay off the due within the grace period so that their CIBIL is not impacted and does not affect their eligibility for future loans. The Voice AI communicates the same to the customer and assists them with on-call payment. The Voice AI can make calls, follow-ups, and take call-back requests concurrently at any time during the week and to which the human agent has limited capacity.
For situations when the customer is refusing to pay or is unable to maintain sufficient balance, the digital voice agent dispositions them accurately and notifies them of a call back from the human agent.
Voice AI Outcomes
It will be interesting to note that the Voice AI agent helps in improving every performance metric.
Collection Efficiency Ratio – Higher, overall collections help improve the collection efficiency
Age at List (AAL) – faster collections help in keeping the debt age towards the lower side
RPC rate (Right Party Contacts) – Voice AI calls and identifies the Right Party Contact so that human agents do not have to waste time. This is a radical improvement, without spending much money and time
Percentage of Outbound Calls Resulting in Promise to Pay (PTP) – Voice AI agent calls at the right time and to the right person when they prefer to interact, thus increasing the probability of recovery
Profit per account (PPA) – since with Voice AI agent, the costs as much lower, it has a direct impact on this metric
Here is what a debt collection agency or an NBFC can expect from Voice AI agents such as Skit’s Digital Voice Agent.
49% of the total collection value recovery per campaign
Debt recovery from 78% of delinquent accounts without any human assistance
Lower the cost of collection by 40% by addressing the collection challenges like unreachability, unresponsiveness, callback request, and collection after business hours
Achieve 90% of collections in the first 3 days
Increase customer coverage by 30%
Speed up the collection campaign TAT by 70%
80% contact-ability rate
84% engagement rate with customers
68% disposition capture rate on CRM allowing your agents to focus on accounts in the B2 bucket onwards for personalized interaction for recovery
The data mentioned above has been taken from project implementations, and will certainly vary for each company. Thus, it is indicative at best, of the results that can be achieved and the potential of the Voice AI agent.
For any questions on the application, operations, outcomes, and pricing of a voice AI agent in the debt collection space, feel free to contact us – Book A Demo.
For more information on debt collection space and the role of voice AI, please visit the page.
You’ve been exploring Voice AI as a possible solution to automate your debt collection agency’s operations; you’re considering adopting an Augmented Voice Intelligence solution to scale outbound and inbound calls for collections. Congratulations—you’re in the right place.
A Voice AI solution can significantly reduce your collection costs and improve the success rate and duration of your collection campaigns. However, not all Voice AI vendors are the same. How do you choose the right vendor for your agency?
Given our extensive experience with the collections space and our tech expertise, we’ve put together a list of topics to consider when meeting with providers and choosing the best one to move forward with, from the understanding of business operations to technical capabilities.
If you can’t count on your Voice AI vendor to fully understand the collections space, you will end up being significantly more involved with every step of the process, which will ultimately take longer, cost you more money, and lead to a disappointing return on investment.
Compliance with Debt Collection Regulations
The very first thing to look for in a Voice AI solution that handles outbound collection calls is the company’s level of understanding of the existing laws and regulations related to collections in the U.S.
A well-trained Digital Voice Agent can comply with the regulations with a consistency and precision that can be hardly achieved by human agents. However, it’s crucial to check whether the provider is up to date with the current laws.
The main collections-related regulations in place in the United States are:
Fair Debt Collection Practices Act and Reg F: The FDCPA, most recently updated with Regulation F in 2021, is the most comprehensive U.S. law that restricts, for example, call frequency and calling hours, and mandates the reading of the “Mini-Miranda.”
Telephone Consumer Protection Act: The TCPA ensures that numbers in the Do Not Call registry are never contacted; this can be easily achieved with Voice AI.
Federal Fair Credit Reporting Act: The FCRA protects information collected by consumer reporting agencies.
Payment Card Industry Compliance: PCI regulations ensure that the Voice AI provider takes the appropriate measures to protect stored cardholder data and encrypt the transmission of the data.
Health Insurance Portability and Accountability Act: HIPAA is one of the most well-known privacy laws in the United States.
Read more about meeting debt collection compliance with Voice AI in our blog post.
Provider’s Understanding of the Collections Space
This point goes beyond regulations: How well does the Voice AI provider know and understand the collections space as a whole? Their understanding of the structure and overall operations of a collection agency is likely going to be a helpful factor in the collaboration between the agency and the provider.
The provider should be able to understand the agency’s structure, the challenges related to employee retention and call scalability, as well as best practices for outbound collection calls. This way, you can trust that they will design an optimal conversation flow to facilitate your collection efforts.
Different factors will affect the conversation design. For example:
Nature of debt: There are different types of debt, including credit card, healthcare, student, etc.
Age of debt: A 30-day past due debt is very different from a 180-day past due debt.
Ability to Handle End-to-End Conversations
A Digital Voice Agent needs to be able to handle outbound collection calls from start to finish, without any human intervention—from verifying the user’s identity to completing the transaction.
The Digital Voice Agent will therefore initiate the call, remind the user of the due payment, register the reason of delay, persuade the user to pay right away, collect the payment or offer alternative payment plans, and ultimately feed the data it has gathered during the call to the CRM tool.
One important question to ask providers is: What kind of access will the collections agency have over the Voice AI?
The ideal provider will offer access to a dedicated and user-friendly platform, from which the agency will be able to view and tweak conversation flows.
Additionally, having a good platform will also help with the integration of third-party applications, such as payment gateways, CRM, and other business applications.
Want to learn more about how the technology behind a Digital Voice Agent works? Check out our dedicated blog post.
Actionable Analytics
Once the Voice AI solution goes live, will you be able to easily visualize and analyze its performance and results?
As more and more users speak with the Digital Voice Agent, you gather precious data that you don’t want to waste. Your Voice AI vendor should give you access to a dashboard to monitor the effectiveness and quality of the conversations.
MLOps (Machine Learning Operations)
At the very core of Voice AI lies the capability of the algorithms to continuously learn and improve as more conversations take place.
MLOps stands for Machine Learning Operations and it’s somewhat similar to DevOps. It’s an organizational model and culture designed to help the involved teams manage the operational processes behind machine learning.
AI companies that have a good MLOps system in place are likely to develop a better technology set to improve with time.
After Go-Live: Continued Voice AI Training
Your Digital Voice Agents are ready to go live and start calling your customers to remind them of their due payments. What now?
After the Voice AI platform goes live, the work is far from finished. The Digital Agents must be maintained for further optimization of the technology and the conversational experience, also to ensure they understand out-of-scope intents. The solution must also be monitored, especially at the beginning, for quality assurance purposes.
According to a recent Gartner report, failing to monitor automation tools in post-production is one of the most common mistakes companies make when implementing automation.
Additionally, it’s important to note that Voice AI solutions are typically rolled out in multiple phases: with time, additional capabilities and use cases may be added.
Therefore, your agency will want to work with a Voice AI provider with a clear plan for post go-live training and handling.
In conclusion, watch out for these key questions to ask your Voice AI vendor.
For more information and a free demo, you can schedule a call with one of our collections experts. We’ll be happy to help!
Owing to far-reaching repercussions, compliance management has become an issue of gravitas. It’s a challenge of change. Often, frequent regulatory changes create ambiguity for collection agencies. For instance, Regulation F of the Consumer Financial Protection Bureau (CFPB) came into effect on November 30, 2021, and is the most significant debt collection rulemaking. Any creditor–either the original issuer or a debt buyer–faces challenges in responding to it. And even more tedious is training and retraining agents, reiterative setting up processes and tools to meet regulatory requirements.
When it comes to compliance, the devil is in the details. A human agent under varying stress and performance pressure is prone to make mistakes. But even an innocuous breach of compliance results in hefty fines and penalties. Even without state or local mandates around debt collection practices, federal regulations must be followed to avoid penalties or lawsuits from consumers or enforcers. CFPB levied $1.7 billion in civil penalties and over $14.4 billion in relief for American consumers in the last ten years. Compliance has thus evolved as a significant pain point for debt collections agencies.
We have reached a point where compliance is not just an expense item but also a source of differentiation for collection agencies. Unsurprisingly, most debt collection agencies are looking for tech solutions that can help them be more agile and efficient. Voice AI is one emerging solution with the most disruptive potential and growing use cases.
Too Many Calls, Too Little Communication
One of the prime objectives of compliance is to protect the customer from unfair practices and harassment. CFPB bases much of its enforcement authority on the concept of UDAAP (unfair, deceptive, and abusive acts or practices).
A call at the right time, to the right person, and with the right message can achieve the 3 Cs of debt collection: Cost, Compliance, and Customer Experience. A human agent may struggle to accomplish the triad, making too many or too few calls, but it’s a cakewalk for an intelligent voice agent.
The formal, statutory fees and levies, which are increasingly hefty, represent just the tip of the compliance cost iceberg (around 10%) of total regulatory costs. The broader cost of compliance is much bigger, making it a formidable force.
Here are the common challenges faced by debt collection agencies today:
Ever-Expanding List of Laws: Fair Debt Collection Practices Act (FDCPA), Telephone Consumer Protection Act (TCPA), Federal Fair Credit Reporting Act (FCRA), Payment Card Industry compliance (PCI), and Health Insurance Portability and Accountability Act (HIPAA) are a part of a growing list of regulations, adherence to which is a core driver to the success of debt collection agencies and similar financial institutions.
High Cost of Continual Training and Vigilance Process: A survey of sector firms by the Credit Services Association (CSA) reveals that in staffing terms, the proportion of resources involved (in compliance) seems to trend generally between 15% and 25% of total resources. That is a significant percentage and an opportunity to cut down the cost.
Client Expectation and Audit Requirements: Clients of collections agencies are deeply wary of meeting compliance and exert pressure, even more than regulators, to comply. As per a report by CFPB, collection agencies with large clients face 17 audits in a year. That’s an average of 3 audits every 2 months. The lack of transparency between debt collectors and consumers makes it difficult for agencies to facilitate these audits effectively. It is a formidable challenge to meet such high expectations cost-effectively.
Insufficient Time to Design and Implement Compliance Effectively: A rapid and frequent change in regulation leads to collection agencies running from pillar to post to update their processes. Deploying AI-enabled voice agents can minimize the training and guidance cost.
High Cost of Not Meeting the Compliance Requirements: Failing to meet the compliance requirement has, in the past, led to grave heavy consequences. Encore and Portfolio Recovery Associates, two giants in bad debt collections, were fined $18 million in 2015. They were forced to refund or halt collection of over $160 million in consumer debts. Violating the Do Not Call registry can cost agencies anywhere between $500-$1500 per case, as per TCPA. Moreover, razor-thin margins make the total cost of attorney fees, settlement costs, and the opportunity cost of time too much for agencies to bear.
Voice AI and its Ability to Empower Collection Companies Manage Compliance
More often than not, compliance is a matter of adhering to protocols and procedures. AI-enabled digital voice agents that can religiously follow a given set of instructions prove far superior in adherence to the regulatory framework.
There are numerous instances where small mistakes land collection agencies in trouble. Here are some simple yet powerful examples of how Voice AI can help with compliances:
Honoring Do Not Call Registry and Data Scrubbing: The telephone Consumer Protection Act (TCPA) maintains a register of subscribers who do not want to be called for telemarketing calls and automated dialer calls unless you have consent to do so otherwise. It’s essential to scrub the data before dialing these contacts and check for permission. Solution is to scrub the data against certain database such as Do-not-call registries (external and internal), consumers represented by attorneys and debt settlement companies, deceased consumers, serial litigators, bankrupt consumers, cease-and-desist order consumers. Unlike human agents, who can fumble, digital voice agents perform this with the help of APIs in a fraction of a second.
Calling Within Permissible Hours: FDCPA does not allow collection agencies to contact customers outside of 8:00 a.m. to 9:00 p.m. local time unless the consumer has given explicit consent. Additionally, customers with night jobs may not wish to be contacted during the day. Such personalization in large portfolios prove to be a daunting task for a human agent but an effortless one for a digital voice agent.
Calling Frequency: Regulation F of CFPB limits the frequency of calls under the 7/7/7 rule, restricting the agencies from attempting to establish communication with their consumers for more than 7 times in 7 days. The 7/7/7 rule includes voicemail, unanswered calls, and messages left on the consumer’s phone, and excludes email and text messaging. Furthermore, agencies cannot try to establish contact in the next 7 days after a successful communication. It’s taxing for human agents to consistently follow these rules for the entire customer base while optimizing time and cost at the same time. On the other hand, configuring machines to follow all these rules is possible with a click.
Mini-Miranda is mandatory as per FDCPA in the first communication in any channel. Digital voice agents never fail to comply with such regulatory requirements.
Failure to Discontinue Communication Upon Request: Communicating with consumers in any way (other than litigation) after receiving notice with certain exceptions can lead to lawsuits. Machines follow strict protocols and comply with the request submitted by the consumers.
Communicating with Consumers at Their Place of Employment: It’s illegal to contact the consumer after being advised that this is unacceptable or prohibited by the employer. Human agents under dier conditions fail to honor guidelines. On the other hand, since machines reachout at the right time and frequency have high conversion rate while meeting compliance.
Contacting a consumer represented by an attorney: Agents must not contact the consumers who have chosen not to be contacted by agencies and have signed up attorneys for communication with certain exceptions.
Communicating with a Consumer During Validation Period: Human agents can make a mistake and try to establish communication with the consumer or pursue collection efforts after receiving a request for verification of a debt made within the 30-day validation period. On the other hand, Digital Voice Agents are configured to not engage in any such activities and trigger the automatic collection calls once validation period is over.
Misrepresentation & Threatening Arrest or Legal Action: With variable incentive as a major wage component, it’s quite common for debt collectors to misrepresent as attorney or law enforcement officer. FDCPA prevents such kind of misrepresentation and has punitive enforcement directives. Digital voice agents follow strict protocol and never succumb to such malpractices.
The abusive or Profane Language used during communication related to the debt is prohibited. Digital voice agents never fall back to such practices in order to achieve the results.
Communication with Third Parties: revealing or discussing the nature of debts with third parties (other than the spouse or attorney) is prohibited except to know the location of the debtor without mentioning debt related information. Intelligent Voice Agents can confirm the right party before giving out any information.
Raise a Dispute: Voicebot can also help consumers raise a dispute over a call and tag it in the CRM so that the relevant team can pick it up.
Validation: Upon asking for validation information, the voice bot can immediately send the electronic copy of the validation notice and mark the contact with a relevant tag so that human agents can see the status, and neither the voicebot nor human agents try to communicate to the consumer for the next 30 days.
Raise Tickets: Voicebot can even raise tickets to send the physical copies of the validation notice if explicitly requested by the consumer.
With Distinct Advantages, Voice AI Will Play a Bigger Role in Compliance Management
Apart from numerous other use cases, the utility of Intelligent voice agents in improving the compliance of debt collections agencies is fast emerging and very promising.
Apart from the direct costs of compliance, indirect costs such as fines and penalties take a heavy toll on companies. Today, compliance has become more than an expense but a source of differentiation. Many companies have already begun adopting Voice AI, and its ever-expanding use cases will help them create a distinct competitive advantage.
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