How Voicebots Can Help Collection Agencies Prepare for Tax Season

Tax season is the busiest time of the year for collection agencies. According to a recent report, 44% of Americans say they earmark their tax refunds to pay off their debts or bills. With 3 in 4 U.S. residents receiving a tax refund from the government during this season each year, the number of people who will wisely take advantage of the reimbursements to pay off their debt is high.

In 2023, the average tax refund for individuals in the U.S. was $3,054.

Collection agencies know it’s important to take advantage of this window of opportunity to maximize their recovery rates and agency margins. During tax season, the industry usually experiences a peak in payments, paired with a general openness of consumers to engage with collectors. Many consumers will be relying on tax refunds to pay off their debt at this time of the year.

Now is the perfect time for agencies to prepare for tax season and the volume surge in outbound and inbound calls. In this article, we’ll explain how Voice AI (the technology behind a voicebot) can transform tax season for the better, making it a less stressful and more profitable time for collection agencies.

The Challenges Collection Agencies Face Before and During Tax Season

While tax season undoubtedly represents a window of opportunity, it also presents several challenges for collection agencies. The best way for management to tackle these challenges is to prepare in advance and to involve their collectors on the floor in these preparations.

Here are some of the most common challenges collection agencies face before and during tax season:

Hiring new collectors: To handle the surge in call volume, collection executives often seek to hire new collectors to join their staff. Hiring takes time and resources; since the COVID-19 pandemic, it’s become more challenging to find new talent, as people are inclined to seek more flexible jobs, and salaries have become more competitive. You’ll need ample time to find new talent and train new hires.

Training staff to prepare for the season: Whether newly hired or seasoned, all collectors should receive the appropriate training before the beginning of tax season. All training materials should be easily accessible, focusing on the challenges and skills specific to this time of the year.

Updating the agency’s compliance management system: Every agency should have a compliance management system — often found within the collections management software. This system is used to store and organize the current laws and regulations of the ARM industry. Before tax season begins, the agency’s compliance officer or manager should ensure that the system is up to date with the latest regulations, including state laws; outdated regulations should be removed. Additionally, this system should be easy to access and browse for collectors.

Planning a successful settlement campaign: The surge in collection volume encourages some agencies to offer small discounts for a limited time; other agencies take it to the next level by planning a wide-scale settlement campaign. For a settlement campaign, the agency focuses on a specific group of accounts, typically consumers with higher recovery rates and debt whose age falls within a specific timeframe. If the agency services third-party debt, then it also must coordinate the campaign with the original creditors. Executives must decide what balance reduction they are going to offer those consumers and the running time of the campaign. The entire process can make the agency extremely busy, and things are likely to get hectic for the collectors on the floor.

How Voice AI Can Make Your Life Easier During Tax Season

Voice AI, the technology behind voicebots, has become one of the favorite automation technologies in the accounts receivables industry. Voice AI enables collection agencies to automate collection calls, both inbound and outbound, making it much easier for executives to scale their collection campaigns without the need to hire additional or seasonal agents.’s Voice AI solution initiates thousands of calls to consumers within minutes, establishes right-party contact, reminds them of the outstanding balance, and encourages them to make a payment or captures promise-to-pay. The solution easily transfers calls to your live agents so they can speak to the most engaged consumers and collect payments on-call.

It’s important to note that Voice AI is not IVR (interactive voice response), an outdated and unpopular solution commonly used in customer service. Unlike IVR, Voice AI can handle intelligent, two-way conversations with consumers.

Call automation with Voice AI is transforming collections across the board, as it enables collection agencies to handle many more accounts simultaneously, recovering payments at a fraction of the cost. Additionally, this technology augments the work of live collectors, who are empowered to handle more complex cases and focus on more revenue-generating tasks; whenever agents get a transfer from Voice AI, they receive the context on the consumer’s previous interaction with the voicebot in real time.

While this technology is helpful all year round, during tax season it becomes particularly essential. Here’s why:

Make it super easy for consumers to pay. Any roadblock in the payment process can significantly hinder the recovery of the debt. That’s why customer experience plays an important role, and making the payment as easy and frictionless as possible is a priority for your agency. Voice AI makes the process smooth and pleasant for consumers.

No need to hire additional collectors during tax season: Voice AI enables executives and managers to scale their operations, without the need to hire additional collectors during this busy season. This way, they can continue to rely on their trusted team and get the extra help they need from the Digital Voice Agents, who are unlimited in number and can handle thousands of calls simultaneously. Collections with Voice AI are significantly cheaper; additionally, voicebots don’t take any commission!

Fewer concerns about compliance thanks to Voice AI: Executives can worry less about complying with laws and regulations since the solution is fully trained to comply with regulations at the state and federal levels. Unlike live collectors, the automated agent is always compliant and does not go off script.

Execute a smooth settlement campaign at scale: With Voice AI, collection agencies can execute a settlement campaign at scale, reaching thousands of consumers in a very short amount of time to offer the settlement and collect the payments.

When Should You Start Preparing for Tax Season?

While it’s never too early to get started, we see many agencies evaluate partners and vendors before Thanksgiving, just as the holiday season approaches and many U.S. residents are known to use their credit cards for holiday spending.

However, make no mistake: it’s also never too late! At, we pride ourselves on our fast and efficient implementation process. From the moment you adopt our Voice AI solution, you can go live and start using the platform in as little as 48 hours.

Are you ready to take the next step toward call automation with Voice AI? Schedule a free demo with one of our experts using the chat tool below!

How ARM Companies Can Boost Right-Party Contact with Voice AI

What Are Connect Rate and Right-Party Contact (RPC)?

Debt collection agencies invest time and resources in getting in touch with consumers. In theory, all it takes for a collector to speak with a consumer is to hit the call button, but in reality, it’s not that simple; oftentimes, the number is wrong, the consumer does not answer the phone, or the wrong person picks up the phone.

Connect rates and right-party contact rates are two metrics that significantly affect the outbound operations of a contact center—including a collection agency.

The connect rate measures the percentage of calls that are picked up over the total outbound calls initiated. The right-party contact rate is the percentage of calls in which an agent is able to connect with the target consumer, which could be either the debtor or a relative who has been given permission to handle the debt. Right-party contact (RPC) is the most accurate measure of the effectiveness of an agency’s outbound calling efforts.

In this article, we will explore how conversational voice AI technology can efficiently verify right-party contact, leading to significant time and cost savings for collection agencies.

☎️ Factors that affect connect and right-party contact rates
❌ Wrong number
⛔️ Busy line
💬 No answer
📩 Voicemail
🙅🏽‍♀️ Wrong party answers the phone

Why Right-Party Contact Can Be a Challenge for Collection Agencies

Collectors know it very well: reaching consumers can be tricky.

Given the limitations imposed by the TCPA and the FDCPA, collectors can’t call debtors at any given time of the day. While timing is everything, even a well-staffed agency can only contact consumers so many times in order to reach them, as the number of available collectors is limited and you don’t want them to spend too much time trying to reach the same numbers too often.

Right-party contact can be a serious challenge for collection agencies. Collectors (and their managers) want to spend as much time as possible actually speaking to consumers and collecting payments — and as little time as possible trying to reach people on the phone. Calls not resulting in RPC don’t lead to a collection and result in an overall waste of resources.

This is where automation and artificial intelligence come into play.

How Voice AI Solves the RPC Issue for ARM Companies

With rising costs, staffing challenges, and shrinking margins, accounts receivables companies are looking at digital transformation and automation as valid solutions to their operational challenges.

Contact centers in all industries have been relying on automatic dialing systems (or auto dialer software) for decades. These systems make the dialing process faster and easier, boosting agent productivity; in addition to queueing calls and dialing the target number automatically, they also screen out inactive numbers, busy lines, and answering machines, drastically improving the contact center’s connect rate.

But what about right-party contact? 

Once the collector reaches a person on the phone, they must establish whether the person they are speaking to is the right party (the consumer or debtor) or not. The right party could also be a third party (a person authorized to handle the debt or an attorney representing the debtor). This process can take a few minutes.

A conversational voice AI solution like can handle the actual call — rather than just the dialing process.

Once someone picks up the phone, the voice AI solution confirms right-party contact and authenticates the consumer through SSN, DOB, or zip code; it then engages with the debtor, offering ways to pay off their debt. If needed, the solution will negotiate a payment plan or transfer the call to a live agent, who can assist with more complex queries.

The entire process is faster and cheaper, and allows the collection agency to save on resources, enabling live agents to focus on more complex calls and engage with consumers who are already authenticated. Below, you can see a step-by-step summary of how’s voice AI solution handles a debt collection call:

Are you interested in learning how voice AI can transform your collection agency’s results? Schedule a call with one of’s experts using the chat tool below.

What Is Call Automation and How Can It Impact Debt Collections?

Let’s face it: debt collection agencies often sit on high-volume portfolios of accounts, as they lack the capabilities and resources to contact all consumers in a timely manner. Ultimately, some agencies give up on reaching all those accounts to focus solely on the larger ones.

ARM companies usually handle thousands of new accounts each month, but many of those accounts might be left untouched due to the lack of bandwidth. For each account, agents need to establish right-party contact (RPC), remind the customer of their outstanding balance, and offer ways to help them pay off their debt, such as a payment plan. More often than not, customers are not available right away, and the agent has to call them back at a different time.

What if I told you that you could automate this entire process?

Yes, you heard that right. A conversational voice AI solution can handle your collection calls on your behalf. In this article, we’ll explain how this type of solution works.

What Is Call Automation?

Nowadays, 88% of consumers expect organizations to offer a self-service support portal. Contact centers in all industries — from banking to e-commerce and, of course, accounts receivable management (ARM) — are turning to automation as a strategy to overcome the challenges of managing both inbound and outbound calls with customers.

In this rapid-changing environment, marked by the surge of generative AI, conversational AI has emerged as a key debt collection software to solve automation challenges. These tools are capable of handling conversations with consumers from start to finish, without the need for any human intervention.

Voice AI technologies may sound “new” to you today, but they are set to become the industry standard in the collections and payments space within a few years. Early adopters are already reaping the benefits as they are ahead of the learning curve.

When they hear “call automation,” many people tend to think of IVR (interactive voice response) systems. Think, “To make a payment, press 1…” In recent years, voice automation, AI, and speech recognition technologies have significantly evolved, also with the emergence of conversational voice AI and large language models, delivering a much more sophisticated technology than IVR. You can think of IVR as the “grandfather” of voice AI.

A conversational voice AI platform delivers a human-feeling and effective two-way conversation with a consumer, answering questions and providing context-specific information.

Once you upload data for a collection campaign, the solution can initiate thousands of calls to consumers, establishing RPC and reminding them of their outstanding balances; the solution then helps them pay via select payment gateways or negotiates a payment plan.

What Does an Automated Collection Call Sound Like?

Because’s technology is powered by AI, no interaction will be identical to the other; every customer is different, and each call is personalized. The technology is built to handle a natural-sounding back-and-forth conversation with the consumer following their responses, cues, and questions ad hoc.

If you want to learn more about our approach to customer experience (CX) and how we build a persona for our voice AI solution, read our article about how elevates CX in collection calls.

The voice AI platform handles these scenarios:

Is an AI-powered Collector Compliant?

Compliance is one of the most common pain points and concerns for executives working in collections. There are many regulations at both federal and state levels, and sometimes consumers may file lawsuits against ARM companies, causing major expenses on the agencies’ part. Additionally, regulations often change, and collectors sometimes struggle to keep up with the new developments.’s conversational voice AI solution fully complies with the current laws and regulations related to collections and phone calls, such as Reg F, the TCPA, and more. We ensure that the solution initiates calls only at the permitted times of the day and within the correct frequency. We prioritize information security; we have, among others, ISO 27001:2013 and PCI DSS certifications and use AES-256 encryption.

It’s actually easier to ensure that an AI solution rigorously complies with regulatory requirements; this is because the solution:

  • never goes off-script
  • always provides identity disclaimers
  • only calls customers at permitted times
  • always honors do-not-call registries
  • never resorts to threats or aggressive language.

Do you want to learn more about call automation for collections and payments? Are you looking to adopt a Voice AI solution for your business? Schedule a call with one of our experts by using the chat tool below!

How Is the U.S. Planning To Regulate AI in Financial Services?

From automation and decision-making to fraud detection and customer experience, the applications of artificial intelligence in financial services seem endless. As companies, both large and small, navigate this evolving landscape and its plethora of vendors and solutions, many ask themselves: How will this technology be regulated once our legislative branch starts looking into it more seriously?

At, we organized a panel discussion hosted by our friends at Accounts Recovery with three renowned experts, to whom we asked the most pressing questions on AI in financial services and the regulatory environment. What regulations should we expect? More specifically, which aspects of AI will regulators be more interested in scrutinizing?

In this article, we’ll discuss the current role of AI in the financial sector — with particular attention to the accounts and receivables industry — and report some of the insights from the industry experts we interviewed during the event.

Understanding AI’s Impact on Financial Services

AI in financial services is not a prediction or a catchphrase. According to an international survey published in 2020 by the World Economic Forum and the Cambridge Centre of Alternative Finance, 85% of financial services providers already use AI in some form. Additionally, 77% of the responding institutions reported believing that AI would become essential to their business in the following two years. With the launch of ChatGPT in 2022, these numbers can only be higher now.

Some of the most notable applications of AI in the sector, according to Deloitte, are:

  • Conversational AI (such as chatbots and voicebots) for consumer interactions
  • Fraud detection and prevention
  • Customer relationship management
  • Predictive analytics
  • Credit risk management

The Regulatory Framework in the United States

Over the last few years, there have been efforts for legislators to study and regulate the use of AI in various industries, including the financial services industry. But while other foreign legislative bodies have been notably faster than the U.S. at passing timely legislation, there has yet to be a successful attempt at the federal level here in the United States.

In 2022, a bipartisan privacy bill, the American Data Privacy Protection Act (ADPPA) was introduced in Congress, but it did not make it through the Senate and has ever since been abandoned. Later in 2022, the White House published a policy document named the “Blueprint for an AI Bill of Rights,” seeking to provide guidance on the different rights that lawmakers should keep in mind when framing the discussion on the regulation of AI across industries.

In September, the U.S. Senate Committee on Banking, Housing, and Urban Affairs held a hearing about “Artificial Intelligence in Financial Services” to discuss AI’s applications, risks, and benefits in the industry.

The witnesses who spoke at the hearing were Melissa Koide of FinRegLab, who spoke about credit underwriting; Professor Michael Wellman of the University of Michigan, who raised concerns about algorithmic trading and market manipulation; and Daniel Gorfine of Gattaca Horizons, who focused on the opportunities presented by AI.

Most recently, the White House issued an executive order on artificial intelligence, establishing guidelines for AI safety and security. The order includes requirements that aim to protect consumers from threats to privacy, discrimination, and fraud.

Insights from the Experts: Possible U.S. Regulations of AI

The following quotes are excerpts from the webinar hosted by Accounts Recovery. Watch the recording to listen to the entire conversation and get the full context. The four experts who spoke are Dara Tarkowski of Actuate Law, Heath Morgan of Martin Golden Lyons Watts Morgan, Vaishali Rao of Hinshaw Culbertson, and Prateek Gupta of

(Please note: The information provided in this article does not, and is not intended to, constitute legal advice; instead, all information is for general informational purposes only.)

Key Takeaway 1: Look at the European Union for Guidance

The United States is pitifully far behind the EU, the UK, areas of APAC, and Australia in the way they’ve approached the technology and the utilization of the technology. If we want to see which direction our country will go in terms of AI regulations, we have a five-year playbook of what it looks like in the rest of the world.”

“What we’ve seen from the hearings that have been held in Congress; at its base, the concern by lawmakers and regulators and a lot of the practitioners, is that bad data leads to bad outcomes, which is selection bias. Then we’ve got process bias, which means that bad methods and bad processes lead to bad outcomes. Philosophically, those are the two issues that lawmakers are trying to address in whatever sector.”

“If you’re looking for guidance, put together a framework that is largely compliant with what the European Union has already laid out as the ethical and safe use of AI. In a global economy, it would be foolish of the United States to deviate too much from what the rest of the world is already adopting.”

Key Takeaway 2: This Is Not About Replacing People with Technology

“In our industry, the usage of these types of technologies is not and should not be to replace people or to replace the thoughtfulness and the consideration of the decisioning. However, a lot of these technologies can help speed up and improve our decisioning, so that people can make better and faster decisions, which is better for both businesses and  consumers.”

Key Takeaway 3: AI Must Provide Value to Consumers

When it comes to the use of chatbots and voicebots, “you can’t keep consumers in an infinite loop with the artificial intelligence system and not let them talk to an actual human being whenever the AI is unable to provide a resolution. One of the focuses needs to make sure that AI provides value to the consumer, and is not used as a way for companies to create a hurdle between consumers and live agents.”

Key Takeaway 4: Waiting for Regulations May Not Be the Best Strategy

Should we wait for regulations before adopting AI solutions to avoid any risks? “You can’t bury your head in the sand and say: ‘We’re not going to deploy this technology until there are regulations.’ It really isn’t a question of whether you are going to adopt this technology—it’s a matter of when. The more you accept that and look into having risk assessments, an AI policy, and an AI committee, the better you’re going to be. The technology is coming to you through vendors and consumers before you know it.”

Key Takeaway 5: Set up an AI Task Force

“Set up an AI task force, so you can set up a framework on how to use AI properly.”

Want to learn more about Conversational Voice AI and how it can benefit your business? Use the chat tool below to schedule a free consultation with one of our experts!

How’s Voice AI for Debt Collections Complies with State-level Regulations

State-level Regulations Are Just as Important as the Federal Ones

Virtually everyone working in the accounts and receivables industry is familiar with Reg F, the law passed in 2021 to update the Fair Debt Collections Practices Act (FDCPA). Reg F provides parameters for call frequency in debt collections; in particular, the 7x7x7 rule, which allows a maximum of 7 calls in a 7-day period, and allows the collector to follow up only 7 days after having had a conversation with the consumer.

However, some states have stricter laws when it comes to the debt collection industry and call frequency.

When training new agents or deploying a new software solution for your collection strategy, it’s important not to forget these state-level regulations, which are just as important as the federal ones.

Examples of State-specific Regulations for Collection Calls

Here are three examples of state-level regulations that limit call frequency permissions further than Reg F.

Massachusetts: According to the Attorney General’s regulations, creditors and collection agencies are allowed to make a maximum of 2 attempts of communication via telephone (calls or text) in a 7 consecutive day period.

New York: New York’s law is similar to Massachusetts’. Also here, collectors are not allowed more than 2 attempts of communication (calls, texts, letters, emails, etc.) in a 7-day period.

North Carolina: Collection agencies are allowed to make only 1 attempt of communication to a particular third party in a 7-day consecutive period to obtain location information.

How’s Compliance Filters Tackle State Regulations

Working with legal and compliance experts, at we’ve compiled the different state-level regulations and have integrated them into our Voice AI solution’s compliance filters.

Our solution identifies the state of the consumer through the zip code of their most recent address and identifies the applicable regulations in real-time during the campaign initiation process. This way,’s solution never dials out a non-compliant call to a consumer.

Want to learn more about how Conversational Voice AI can help you streamline your collection strategy and comply with all regulations? Schedule a call with one of our experts using the chat tool below.

5 Unexpected Capabilities of Conversational Voice AI for Collections

It’s unlikely, for anyone working in the accounts and receivables industry, to not have heard about Voice AI. Whether you’ve attended an industry event or you’ve visited an industry website, you’ve encountered this technology, which many collection agencies across the country have been adopting to accelerate and improve their collection strategy.

There are many benefits to using conversational Voice AI for debt recovery. Automation, compliance, business growth, cost-effectiveness—different organizations benefit from it differently. Many agencies have reported that, since adopting Voice AI, they’ve been able to acquire larger debt portfolios, thanks to the increased call volume. Others have reported that the consistency of the technology has been a game changer; after all, artificial intelligence “never has a bad day.”

But what are some of the lesser-known benefits of adopting Voice AI for collection calls?

Inbound Traffic Boost

This is every collector’s dream—increasing the inbound traffic from consumers who want to speak to an agent and resolve their debt. Thanks to Voice AI, which acts as a first line of communication with consumers, you can automate most of your outbound traffic, RPC, and even PTP. The solution can easily transfer calls to your agents, informing them of the relevant context and previous interactions.

“ is helping us optimize agent bandwidth, as it enables our agents to spend more time answering high-value inbound calls,” said one of our clients, Rebecca Roberts-Stewart, COO of LJ Ross Associates. “With as our first filter, our long-term goal is to ramp up call automation and increase inbound calls. The Voice AI platform has already helped us take steps in that direction, with the 40% boost in inbound traffic as a testimony to the solution’s efficacy.”

Intelligent Conversations

Both our clients and the consumers interacting with our conversational AI solution are positively impressed with how intelligent the bot sounds. No matter what the user on the call says, the solution knows how to handle it, offering relevant and timely information and finding ways to solve problems in real-time.

The solution is context-rich, meaning that it keeps track of previous interactions to offer the best possible experience to the user.

Positive Customer Experience (CX)

Consumers who have interacted with one of’s virtual assistants can testify to its ability to deliver a positive customer experience.

First of all, with Voice AI, consumers don’t have to wait—they get the assistance they need right away, without having to listen to a Mozart symphony or to a time-consuming IVR menu.

Voice AI establishes right-party contact (RPC) in less than a minute; if the call is transferred to a live agent, consumers won’t need to repeat the RPC step, as their identity has already been authenticated.

According to industry data, the vast majority of consumers (88%) expect organizations to provide self-service support. We’re not surprised: the back-and-forth with Voice AI is easy and painless.

Rigorous Compliance

With a multitude of ever-evolving federal and state regulations, collection executives and collectors often struggle to keep up with the changes. Compliance is one of the most significant pain points and concerns for the industry, as non-compliance can result in major financial losses for creditors and agencies.

Artificial intelligence can make your compliance more rigorous and your collection strategy more secure.’s Voice AI solution adheres to all telephony, data security, and collection-related regulations, such as the FDCPA, Reg F, and TCPA, among others.

Voice AI never goes off script or forgets a regulation; you can trust that, with all the correct compliance filters in place, the solution will rigorously follow every rule, including the Mini-Miranda and call frequency restrictions.

Perfect Timing

You can always count on artificial intelligence to be timely and precise.

An important aspect of the regulatory environment for debt recovery is call frequency, as outlined by Reg F and other state-level laws. Voice AI always complies with those rules, only initiating calls at the right time of the day and never exceeding the maximum number of call attempts allowed by the applicable regulations.

Additionally, follow-up timings with AI are always precise. If a consumer tells the AI that they’re not able to speak at a given moment and asks the solution to call back at a different time, you can be sure that the AI will call back at the exact time requested by the consumer.

Are you interested in learning how Voice AI can accelerate your collection strategy? Use the chat tool below to schedule a call with one of our experts.

The Importance of Data Security for Debt Collection Agencies

Data Breaches Are No Joke, and They’ve Been Spiking

Data breaches are no joke, and many collection agencies have learned it the hard way—with pricey settlements or even facing bankruptcy as a consequence. A data breach usually involves the leak of user data such as names, email addresses, and passwords. The second quarter of 2023 saw a 156% increase in data breaches globally, with North America leading as the most affected region, according to a new report published by Surfshark and shared by our friends at Accounts Recovery. The United States accounted for 49.8 million leaked accounts in Q2.

The disturbing data highlights the importance of taking data protection measures for collection agencies in the U.S. In a time dominated by digital transactions and interactions, it’s hard to overstate the significance of data security.

For collection agencies, which handle sensitive financial and personal information on a consistent basis, maintaining strong data security measures is not just a legal requirement; it’s a critical aspect of building trust with clients and safeguarding sensitive information.

How can collection agencies better protect their customers’ data and prevent a breach? How should agencies prepare themselves in the event of a breach? What’s a good incident response plan? In this article, we’ll answer these questions and also provide notable examples of data breaches at debt collection agencies in recent years.

Data Security: Legal and Regulatory Requirements

The best-known U.S. law for enforcing the protection of sensitive patient health information is HIPAA. However, there are several other laws that enforce data security for ARM companies.

The Gramm-Leach-Bliley Act (GLBA) is the main privacy law aimed at financial institutions, including collection agencies, and it has been updated with two rules: the Safeguards Rule (2003) and the Final Rule (2021). The latest update to the law includes new requirements, such as encrypting all customer information; multi-factor authentication; secure disposal of customer information; and security awareness training for the staff.

Other data protection and privacy laws collection agencies should be aware of are the Fair Credit Reporting Act and the Dodd-Frank Wall Street Reform and Consumer Protection Act.

Notable Examples of Data Breaches at Debt Collection Agencies

American Medical Collection Agency (AMCA) (2019)

In 2019, the third-party debt collection agency American Medical Collection Agency filed for bankruptcy in the aftermath of a data breach that affected at least 20 million U.S. citizens. Sensitive data such as social security numbers and credit card information were compromised in the breach. In 2021, the company reached a settlement with multiple states.

Professional Finance Company (PFC) (2022)

In 2022, Professional Finance Company (PFC), a Colorado-based collection agency, informed more than 650 of its healthcare provider clients that their data may have been compromised in a massive breach, which affected about 1.9 million patients. The information that was compromised included patient names, addresses, social security numbers, and health insurance data.

NCB Management Services (2023)

Earlier in 2023, the collection agency and debt buyer NCB Management Services said it was the target of a data breach exposing the sensitive information of nearly 1.1 million individuals. The company claimed that the attackers no longer had any of the information on their systems, possibly after an alleged ransom payment had been made.

What Are the Best Practices for Data Security?

Standards and Certifications

Following the relevant standards and seeking the relevant certifications for your business is a key starting point to ensure rigorous data security. One is the Payment Card Industry Data Security Standard (PCI DSS), the main information security standard used by the major card brands. ISO 27002 is an international standard that provides best practices on information security controls; ISO 27001 is a framework for implementing information security management systems (ISMS) to protect sensitive information. Additionally, SOC certifications provide assurance over a service organization’s controls, ensuring security, compliance, risk management, and transparency for stakeholders.


Encryption is crucial for both data storage and transmission. It protects the data from unauthorized use and can be implemented on data whether it’s in transit or at rest.

Access Controls

Limiting access to data within the company is a way to protect it from malicious parties. Depending on their roles and responsibilities, employees should have role-based access to sensitive data and documents.

Security Audits and Assessments

Security audits and assessments should be routinely conducted to ensure that the protection measures are up-to-date and effective. Keep in mind that third-party auditors are generally better than self-assessments, even though they are more costly. Audits can help you identify vulnerabilities and enable you to act fast and address them.

Employee Training

Security awareness training platforms such as Vanta and MetaCompliance offer easily digestible online training sessions to sensitize your employees to the importance of data security. These platforms can train employees to recognize phishing attempts, use diverse and strong passwords, etc.

Vendor Management

As a collection agency, you’re likely using third-party vendors for several processes. Whenever you select and onboard a new vendor, always inquire into their data security practices, as they’ll likely have access to your consumers’ data.

Monitoring and Logging

By consistently tracking and recording all system activities and access, debt collection agencies can detect and respond to any suspicious or unauthorized activities. This proactive approach enables agencies to safeguard sensitive data and ensures compliance with regulations.

Incident Response Plan

What’s your collection agency’s incident response plan? What steps will you follow in case there is a data breach? You’ll need to notify the affected parties, work with regulatory bodies, and more.

When It Comes to Data Protection, Technology Is Your Friend

There are several tools you can use to safeguard your collection agency’s data. Here we are listing the most important ones.

Intrusion Detection Systems (IDS): These systems monitor network traffic and can identify malicious activities or unauthorized access to your data. Whenever the system detects a threat, it sends an alert or takes action to stop it.

Firewalls: These are barriers between your internal networks and external ones, monitoring traffic between the two. They’re a good first line fo defense against cyber-attacks.

Data Loss Prevention (DLP): These solutions can detect unauthorized sharing of sensitive data by monitoring your data whether it’s at rest, in motion, or in use.

Multi-factor Authentication: One of the most “annoying” measures, MFA requires your employees to take multiple steps to log into your systems rather than only relying on a password.

API Security: Given that every cloud-based system is heavily dependent on API-based integrations, API security is another topic you will want to dive deeper into when securing sensitive data.

Conclusion: How Protects Consumer Data

At, we are deeply committed to protecting our clients’ sensitive data and ensuring the privacy of their consumers. From encryption for data at rest and in transit to the ISO 27001: 2013 certification, from strict access management to physical security controls, we’ve implemented multiple measures to ensure maximum data protection.

If you would like to learn more about it, reach out to one of our experts using the chat tool below!

Leveraging Cognitive Science to Improve CX with Voice AI

How Human Cognition Impacts the Way Users Interact with Voice AI

When developing and configuring a conversational Voice AI solution, it’s imperative to take into account the experience that end-users will have when interacting with the solution. No matter what the use case is, users should be able to utilize the voicebot to reach a satisfactory resolution, while also having a pleasant experience.

CX is one of the elements that drive the work of Conversational User Experience (CUX) Designers, who ask themselves multiple questions when designing a Voice AI solution: Who is the client and what is its brand identity? What target persona will be interacting with the voicebot, and what use cases will the solution help them with?

To maximize the quality of the user experience and the consequent CX, conversation designers take into account cognitive science. The goal is to design intuitive, effective, and engaging interactions; cognitive science can provide insight into how users process information, make decisions, and interact with technology.In order to understand the role of cognitive science in CUX, we must first define the term “cognitive load.” According to the American Psychological Association, cognitive load (or mental load) is the “relative demand imposed by a particular task, in terms of mental resources required.” As humans, we can only hold so much information in our minds at any given time; our minds are limited, and we can’t overload them. That is why minimizing the cognitive load plays an important role in ensuring a positive user experience.

Let’s analyze these aspects one by one:

Natural language processing: CUX designers take into consideration the way users process language, including speech recognition and text-to-speech conversion, as well as the interplay between different elements of speech, such as prosody, pitch, emphasis, and the consequent tonality, which further contributes to perceptual and contextual semantics. NLP is essential for building effective conversational systems. This process also includes researching and implementing algorithms that accurately recognize and respond to human speech.

Memory and recall: The user’s ability to remember and recall information when necessary is essential to conversation design. The cognitive load is directly affected by the complexity and quantity of the information given to the user. Designers consider how the information is presented and stored, and ensure that users can easily and quickly retrieve it.

Attention and distraction: Understanding how people allocate their attention, what contributes to selectivity in attention in a given context, and how easily users can be distracted. Designers must structure the conversation to keep the user’s attention focused on the task at hand, resulting in better engagement and performance.

Emotion and motivation: Emotions play a significant role in shaping human behavior and decision-making. Designers consider how users may feel about the interaction and how to motivate them to engage with the voicebot. Secondary UX research about user demographics and socio-economic and geo-cultural backgrounds can provide valuable insights to improve CX.

Decision-making and problem-solving: Conversations often involve decision-making and problem-solving, and understanding how people process information and make decisions is crucial for effective conversation design. Factors include biases, heuristics, and cognitive load.

How Do You Reduce Cognitive Load in Conversation Design?

What are the best ways for conversation designers to reduce the users’ cognitive load in a conversation with a Voice AI solution, consequently improving the customer experience? Here are some guidelines you can follow:

Simplify prompts and confirmations: Using as few and simple prompts and confirmations as possible helps reduce the need for users to remember and respond to multiple options, ultimately leading to an optimal cognitive load and user experience. This is easier to accomplish with a well-designed conversational Voice AI than with legacy technologies such as IVR systems, in which users are forced to listen to long menus of mostly irrelevant options.

For example, a legacy IVR system will offer a lengthy menu of options, such as: “For your account balance, press 1; for information on your upcoming payment, press 2; to update your personal information, press 3 … To hear this options again, please press #.”

Instead, a Voice AI solution will simply ask: “How can I help you?”

Another example is the prompt for a user’s date of birth. A poorly-designed voicebot will say: “Please enter your date of birth in the following format—two digits for the month, two digits for the day, four digits for the year,” or a similarly lengthy and confusing prompt.

Instead, a well-designed voicebot will ask: “Could you please say or enter your date of birth?”

Use natural language: Use natural language and avoid complex sentence structures to reduce the cognitive effort required to understand the conversation.

See below an example that highlights the difference between a more robotic language choice and an alternative with more natural-sounding language.

Robotic language: Unfortunately, the payment amount that you have given is less than the acceptable minimum amount of $50. Can you please state an amount that is equal to or higher than $50?”

Natural-sounding language: “Sorry, but the minimum we can accept is $50. Can you please tell me how much above that amount you can afford to pay today?”

Provide clear cues: Open-ended questions can prompt a multitude of responses from the users; the voicebot might not understand many of the possible answers. Therefore, using clear cues to indicate when the user should speak, and using audio cues to confirm that the system has understood the user’s response should be adopted as a standard practice.

For example, here’s what the Voice AI solution will say to negotiate a payment plan: “We offer a choice of 2-month, 4-month, and 8-month payment plans. Which payment plan would you like?”

Another way to provide clear cues is the use of an audio signal informing the user that something is happening; in jargon, this is knows as an “earcon” (a brief, characteristic, harmonized and structured sound and its job is to communicate a specific message, event, status to a user or convey a task being performed).

This type of audio signal gives the user a cue that something is happening (e.g. a payment is being processed), instead of just having plain silence, which can lead to confusion. An earcon, for example, could be the sound of someone typing on a keyboard, which signals that the information is being processed.

Use progressive disclosure: Progressive disclosure is a strategy in interaction design to reveal information gradually and start only with the most essential information. Providing information to the user in a step-by-step manner, rather than overwhelming them with too much information at once, leads to increased engagement and enhanced experience.

See the example below:

Voicebot: “To set up a payment plan, can you tell me how much you are comfortable paying each month?”

User: “$60.”

Voiebot: “Thanks! Based on a $60 monthly payment, we can set up a payment plan with a duration of 4 months. Your payment plan will start on the next billing cycle. How does that sound?”

The reiteration of the monthly payment amount also serves as an implicit confirmation.

Contextual design: Using context to guide the conversation reduces the need for the users to provide additional information. For example, just as we do when we talk with a waiter at a restaurant, if the user has already provided their name, the system should use that name in subsequent interactions. As the conversation progresses, the voicebot will have more and more context and will be able to utilize the information it has collected to improve the user experience.

The voicebot shouldn’t just rely on context of the specific conversation taking place, but also on the context of previous interactions with the same user. Acknowledging previous interactions is a good idea.

Test and iterate: Testing the bot’s conversations with users and iterating the flows based on their feedback helps improve the user experience (UX) and reduce the cognitive load. The conversation flow can be optimized based on the different users’ needs. Additionally, different types of debt, different users, different demographics often require slightly different approaches.

There is no doubt that leveraging cognitive science in the design and development of conversational Voice AI solutions can significantly enhance the customer experience (CX).

By understanding how human cognition impacts user interactions, conversation designers can create intuitive and engaging interactions that reduce cognitive load, leading to more positive user experiences.

By applying these insights and best practices, business can rely on voicebots to meet their customers’ needs and optimize the use of their own resources. As the technology continues to advance, the potential for Voice AI continues to grow.

Want to learn how Voice AI can transform your business? Use the chat tool below to schedule a meeting with one of our experts!

How Elevates CX in AI-powered Collection Calls

Debt Collection and Positive CX: Is It an Oxymoron?

Discussing customer experience and debt collection in the same sentence might sound like an oxymoron: for most people, the experience of being reminded about an outstanding debt is not particularly thrilling. Yet, the fact that collection calls are not the most welcome calls a customer may receive does not mean their experience should be dry—even negative.

At, we offer an effective and easy-to-deploy conversational voice AI solution for the ARM industry. There are many ways to make the interaction between a user and a voice AI efficient, easy to navigate, and painless.

What is the role of Conversation User Experience (CUX) Design in fostering a positive customer experience (CX) in AI-powered debt collection calls? In this blog post, we’ll share the best practices we’ve adopted to enhance CX in our automated collection calls.

The Role of CUX Design in Improving the Customer Experience

When creating and configuring our conversational voice AI solution for collections, our designers prioritize three components, all of which are essential and will ultimately influence the customer experience when interacting with the voicebot: voice, verbiage, and interaction.

Voice is the audio component of the voicebot: Does it sound male or female? Young or old? What accent does it have? What’s the inflection of the voice? How does it sound—friendly, professional, clear, direct? How fast does it speak? Fast enough to keep the user engaged, but slow enough for the average user to understand? All these questions are taken into consideration when designing the voice AI solution. There are no correct answers, as different use cases and demographics require different characteristics.

Verbiage is the content of the voice AI’s communications during the call with the user. The aim is to make the voice AI solution speak in a natural language so that the interaction can flow smoothly and naturally. Designers take into account grammar, choices of terminology, and other utterances to ensure that the voicebot sounds natural.

Regarding terminology, designers usually seek to balance industry-specific jargon and simple terminology to accommodate users lacking the background and context around the call.

Voice and verbiage, paired together, contribute to creating the digital agent’s “Persona.” For example, that could be a 30-something-year-old female agent, with a confident yet empathetic voice, sounding efficient and eager to help the customer; she could have a midwestern accent and a friendly, yet professional attitude.

The interaction capability of the voice AI solution is the third key element that defines the user experience. This element is the voicebot’s ability to handle an effective back-and-forth with the user. Timing, here, is crucial: when does the AI pause, and for how long? The devil is in the details: missing a comma can change the meaning of a sentence and make it difficult for the user to understand.

How long does the AI wait to reply after the user has spoken?

How does the AI express its prompts? For example, at the beginning of the call, the voicebot will want to verify the user’s identity for authentication purposes; to do this, it will likely suggest the preferred format of the user’s response:

Example: Can you please verify your date of birth? For example, “July 1st, 1985.”

If the AI pauses between the question and the suggested response, the user might respond before the suggestion, leading to mistimings, disfluencies, interruptions, and a potentially failed interaction. To optimize the interaction, a CUX designer will configure the prompt so that the back-and-forth can take effect as smoothly as possible.

The success of the voice AI solution depends on these three pillars. But the customer experience goes well beyond that—let’s explore more aspects in the following sections.

Common CX Concerns: Quality of Speech Recognition and Agent Transfers

One common concern related to customer experience with conversational voice AI is the quality of the ASR, i.e., speech recognition. The fear is that the technology won’t understand the user’s responses and extract the correct “intents” and thus fail to deliver a smooth, natural-sounding interaction. The technology behind speech recognition and natural language understanding has dramatically evolved over the last few years. While this used to be a major problem a few years ago, today it’s less of a concern.

Of course, poor connection or background noise can still hinder the tech’s ability to understand what the user is saying. That’s where a repair strategy comes into play to take the conversation back on track and prevent misunderstandings. Whenever the AI fails to hear the user’s response, it can politely ask them to repeat or rephrase it. Similarly, when the user is uncertain about how to respond, it can offer to repeat it more clearly or rephrase it using different words.

Another common concern relates to agent transfers. Users often fear that the voice AI solution won’t let them easily transfer the call to a live agent if requested. That’s not the case with’s solution. Whenever the customer’s needs are too complex for the AI to handle, and whenever the customer requests it, the solution will always transfer the call to a live agent from the collection agency.

The Role of Personalization as an Effective CX Tool

To achieve a seamless customer experience, a company must know its customers. That is why, in addition to outlining the voice AI’s persona, we also consider the user persona, i.e., the user demographics. Incorporating personalization into the conversation with the voice AI solution helps make it more engaging and fosters trust. However, it’s important to maintain a balance—while personalization is great, you also don’t want to overdo it in order to protect the user’s privacy. This was recently highlighted in data showing that the majority of consumers expect personalization, as long as the data is handled responsibly.

One small touch is incorporating the user’s first name throughout the conversation. For example, after the user authentication is completed, the voicebot can say: “Thank you, Sarah,” to confirm that it’s verified the user’s identity.

Showing that the voice AI solution is aware of the context of the conversation can also improve CX. For example, during an inbound call, the voicebot may say: “I see that you have an outstanding balance of 241 dollars and 50 cents. Is this what you are calling about?”

After the user has made a payment, the voicebot can express enthusiasm like this: “Good news, Sarah! I received your payment of 241 dollars and 50 cents.”

Regional languages and dialects also ensure that the solution is tailored to specific markets. For example,’s voice AI solution speaks over half a dozen languages along with understanding several regional accents.

Incorporating Empathy in Automated Collection Calls

When it comes to sensitive use cases such as debt collection and medical-related calls, empathy is an important component of the voice AI solution’s capabilities. The choice of words, tone, and inflection used by the voicebot can greatly affect the voicebot’s ability to convey empathy, particularly when a user expresses their inability to pay off their debt.

For example, the user may say: “I just lost my job, I can’t deal with this right now.”

How should the voice AI solution respond? The role of empathy in AI is a complex matter: If the voicebot says, “I’m sorry to hear that,” it might irritate the user, given that a computer cannot truly grasp the emotions of someone who has lost their job. However, a common phrase like “I completely understand the situation” is a conventional expression to indicate that the AI solution has acknowledged the user’s challenge.

The voice AI solution is designed to offer options to reach a satisfactory resolution. If the user can’t pay off the debt right away, the solution can offer a few alternatives, such as a payment plan or the ability to connect again in the future.

When designing the voicebot to express empathy, we want to avoid the so-called “uncanny valley” effect. If the voicebot switches abruptly from an overly empathetic statement to a neutral tone, it can cause the user to experience unease and irritation. Therefore, there needs to be consistency in the voicebot’s naturalness and tone, avoiding excessive variation and unexpected changes in its behavior.

And Finally… Regular Quality Checks

While old systems were static and rigid, new-generation conversational voice AI solutions like are dynamic and adaptive. The solution is built to improve over time. Additionally, after the solution is implemented, CUX Designers regularly perform quality checks and listen to calls with customers to ensure that the voice AI functions correctly. This way, they’re able to regularly train the solution to add new capabilities, understand more user utterances and intents, and offer the most appropriate responses.

Are you curious to watch’s voice AI solution for collection calls in action? Contact us using the chat tool below and schedule an appointment with one of our collection experts!’s Augmented Voice Intelligence Platform takes a giant leap with Generative AI‘s Augmented Voice AI Platform is now powered by Generative AI. With the incorporation of Generative AI, we are taking a giant step forward in capabilities of our Conversational Voice AI solution. The conversations are about to become more natural, and the capability to handle complex conversations significantly better leading to better CX and more collection for collection agencies.

At, we embrace the future and prepare to go beyond it.

The Giant Leap in Capabilities of’s Augmented Voice Intelligence Platform with Generative AI

With the application of LLMs, we are seeing a big jump in the conversational capabilities of our solution:

  • Higher Conversational Accuracy: Significantly high conversational accuracy as LLMs are capable of understating conversational complexities with a profoundly superior understanding of context, sentence parsing, and response accuracy.
  • Better Handling Complex Conversations: Superior understanding of complex conversations that were earlier escalated to human agents. This would cut down the percentage of calls passed over to live agents drastically.
  • Out-of-scope Calls: As LLM-powered voice AI solutions can handle a wide array of questions and issues, its ability to handle out-of-scope calls is thus much more.
  • Better and more natural bot utterances.
  • Faster Voicebot Creation: Incorporating Generative AI give a big boost to the speed at which new voicebots can be created as the inherent complexity and effort involved in the design, and creation is a fraction of earlier effort.

Massive Performance Gains with Generative AI Springboard

Not resting with massive gains we are achieving with basic LLMs, we intent to take it a notch further up and outperform human collectors.

1. Going Beyond Human Agent Performance

An agents performance rests on two things: ability to communicate and technical skills. At, we see that with current LLMs, we can achieve superlative communication skills, and by training extensively with end-user data, we can achieve high degree of technical skills. Hence our solution can excel on both the fronts.

To share a rough estimate: the best performing agent finds success on 5% call (out of connected calls), while low performers convert ~2% of calls.

With generative AI, we take a big jump. From the current voicebot conversion capability of around 1-2%, we expect the performance to jump 3-4 folds. Beyond this, our Reinforcement Learning platform learns from outcomes to personalize the conversation to figure out the ideal strategies, learning from thousands of daily conversations.

2. Better, and More Natural Spoken Conversations

Generative AI with its unparalleled conversational capabilities needs to be complemented with equally capable speech synthesis and understanding systems that produces the right speech given the output from LLMs. And that is one of the major areas from the many below:

  • A more natural-sounding TTS voice
  • Conversational context handling prosody of generated audio
  • Full duplex and backchannels in speech conversations

Ultimately, we will be able to deliver the most engaging conversations that delight consumers by solving their problems better than human agents.

Business Outcomes of Incorporating Generative AI

Below are the major impact areas we will more the needle on:

  1. Higher Collection Rate, ~5%: It is a difficult number to quantify, but as the incorporation of Generative AI matures, we expect its collection capability to move beyond 5%, surpassing even the best of human agents.
  2. Lower Agent Dependency, reduction by 50-80%: As the voicebot will be able to handle more complex queries we expect 50-80% reduction in agent touch points.
  3. Higher Resolution Rate, ~100%: Better accuracy and better conversation with higher engagement will help us achieve a conversational resolution rate of close to 100%.
  4. Creating New Voicebots: The effort involved in creating a new voicebot will see a significant dip, as the complexity will be remarkably lower.
  5. Entering New Markets with Ease, 15X faster: Entering into new markets, training for new use cases and applications needing less effort and resources. We are estimating the process to be 15X faster.

The Way Ahead

Though the improvements in our Augmented Voice Intelligent Platform are visible and clear, we will further our efforts to achieve greater performance gains and stay ahead of the curve.

To learn more about how Voice AI can help support your human resources and scale their collection efforts with call automation, schedule a call with one of our experts or use the chat tool below.