Shrutidhara Kakoti, Author at Skit

Conversational AI Can Help RCM Providers in Early-Out Collections and Here’s How

Posted on April 29, 2024July 31, 2024 by Shrutidhara Kakoti

Early-out collections are a critical phase in which revenue cycle management providers aim to recover outstanding payments from patients within the initial 90-120 days post-bill creation. However, despite its significance, the early-out collection process presents challenges that can hinder the collection efforts and, ultimately, the RCM providers’ cash flow.

In this article, we will explore how multichannel AI solutions can bridge the gaps, expedite collections for RCM providers, and improve the patient experience.

Too Little, Too Late: Why It’s Difficult To Execute a Timely Early-Out Campaign

The phase of early-out collections is crucial. Revenue cycle management (RCM) providers must collect outstanding debts from patients within 90-120 days. This is necessary to swiftly close dues and maintain a stable revenue stream.

The challenge?

RCM providers face immense pressure to resolve outstanding self-pay dues, knowing that delays can have significant consequences. After the 90-day mark, unresolved self-pay dues may escalate to accounts being written off and passed to third-party collection agencies or legal firms, worsening the strain on an already fragile margin in a challenging market.

However, amidst the flurry of calls and the limited timeframe, RCM agents often struggle to connect with patients holding self-pay dues, managing only a few attempts below the optimal frequency needed for successful collections. RCM providers typically do not have enough agents to handle patient outreach at the scale required to execute an effective early-out campaign.

This issue is compounded by the perpetual rise in agent attrition, which not only hampers smooth operations and continuity, but also escalates the cost of recruitment and training, further squeezing RCM providers’ profit margins.

Let’s have a look at the challenges in detail:

Inadequate Number of Follow-Ups

Revenue cycle management providers struggle to effectively engage patients despite most of the accounts being very recent, primarily due to limited scalability. This results in missed opportunities to resolve patient dues, ultimately leading to revenue losses.

Complex Bill Disputes

Patients frequently raise inquiries regarding their bills, requiring time-consuming interactions to address their queries and alleviate concerns. This intricate process demands significant time and resources from RCM providers to ensure accurate explanations and satisfactory patient resolutions. Addressing these queries is essential for maintaining transparency and trust in the healthcare providers.

Thin Margins

With the rise in popularity of high-deductible health plans (HDHP), many patients are left with significant self-pay dues. When patients are unable to complete their self-payments, healthcare providers are forced to write off these dues and a large share of their revenue, therefore affecting profit margins.

Staffing

Most sectors are undergoing struggles related to staffing, caused also by high inflation rates. Revenue Cycle Management (RCM) providers are no different. Hiring challenges are worsened by increasing attrition rates and the expenses tied to hiring and training. This directly affects the efficiency of managing early-out collections and incoming patient inquiries, impacting profits and patient satisfaction.

Can Multichannel Conversational AI Help in Early-Out Collections?

The answer is yes.

But what exactly is Multichannel Conversational AI? In simple terms, it’s a technology that leverages multiple channels like email, SMS, phone calls (Voice AI), and web chat to handlehuman-like, two-way conversations with consumers.

Multichannel AI offers a promising avenue to address the pain points in early-out collections and optimize revenue recovery efforts.

Here’s precisely how Skit.ai’s Multichannel Conversational AI solution can help in early-out collections:

Scalable Automated Patient Outreach

Skit.ai’s AI bot can initiate personalized outreach to patients via multiple channels, such as phone calls (Voice AI), text messages, emails, and chatbots, ensuring effective communication and engagement from the outset. This ensures swift connection and meaningful engagement with patients and, at the same time, offers scalability to RCM providers to reach out to numerous patients in bulk.

Handle Payments and Inbound Queries

Skit.ai’s AI bot can authenticate patients, clarify bill breakdowns, answer patient queries, facilitate on-call payments and text-based payment links, and even set up payment plans, enhancing convenience and reducing barriers to receiving payment.

Improved Efficiency and Reduced Agent Costs

Skit.ai’s AI bot augments human efforts by automating repetitive and time-consuming tasks, enabling RCM staff to focus on resolving complex disputes and providing personalized patient assistance. This includes clarifying bills, patient follow-ups, payment assistance, and post-call activities.

How Will RCMs Benefit from Skit.ai’s Multichannel Conversational AI?

Now that we have explained in brief how Skit.ai’s multichannel conversational AI solution can help RCM providers expedite early-out collections, let’s look at how RCM providers can benefit from the adoption of this technology:

Increased Cash Flow: Extensive outreach through multiple channels boosts patient engagement, leading to higher payment rates.

Shortened Recovery Cycle: Targeted outbound campaigns powered by AI accelerate the collection process, improving cash flow.

Reduced Charge-Offs: By minimizing the number of bills sent to collections, AI helps mitigate bad debt losses.

Solved Staffing Challenges: AI augments human resources, enabling RCM teams to handle larger volumes of accounts efficiently.

Cost Savings: Automation reduces operational expenses and maximizes revenue recovery, contributing to overall financial health.

The Effects on Patient Experience and Compliance Concerns

For healthcare providers,the patient experience is of utmost importance. From reaching out to patients regarding their self-pay dues to collecting payments, your technology partners must ensure that patients have a top-notch experience, and the same applies when they interact with Skit.ai’s virtual assistants.

With Skit.ai, RCM providers elevate patient experience by:

24/7 Inbound Support: Skit.ai’s bots can quickly clarify bill details, alleviating confusion and building trust among patients. Patient queries are promptly addressed whenever they reach out, be it on weekends or after work hours.

Multiple Channels: Patients expect and appreciate the flexibility to communicate through various channels according to their preferences. Skit.ai provides multiple communication channels that enhance engagement and cater to diverse consumer needs and preferences, fostering a positive customer experience.

Convenience: Seamless payment options (on-call and link-based payments) and quick responses to payment queries improve satisfaction and reduce friction in the billing process. Our bots can also negotiate payment plans and set up payment plans for ease of collection.

In addition to delivering exceptional patient experiences, we recognize the significance of compliance for RCM providers, particularly in handling patient information. That is why we are proud to comply with all federal and state regulations, including HIPAA, the TCPA,TCPA, HIPAA, and more; additionally, Skit.ai has data security certifications such as PCI-DSS, SOC 2 Type II, and ISO 27001:2022

Conclusion

Integrating multichannel Conversational AI solutions into early-out collections processes offers a transformative approach for RCM providers. Conversational AI empowers RCM providers to navigate challenges effectively and achieve sustainable financial outcomes in a rapidly evolving healthcare landscape by addressing pain points, enhancing efficiency, and improving patient satisfaction.

Frictionless Debt Collection with Multichannel Conversational AI

Posted on April 19, 2024August 14, 2024 by Shrutidhara Kakoti

Collecting delinquent debts from consumers has always been challenging for collection agencies, whether first- or third-party. Traditional debt collection methods often involve managing numerous accounts manually, which can be unreliable and lead to inefficiencies, delays, and customer dissatisfaction.

However, with the emergence of conversational AI automation, debt collection processes are undergoing a transformative shift. AI-powered multichannel communications is emerging as a powerful solution for the accounts receivables industry, offering a streamlined and compliant debt recovery process with an enhanced consumer experience.

In this blog post, we’ll discuss the ongoing shift toward multichannel communication and the impact these new tools have already had on many collection agencies.

What Is the Problem with Traditional Debt Collection Practices?

Traditional debt collection practices are slow and tedious. Most often they result in inefficiencies and delays. These methods lack flexibility and adaptability, hindering agents’ ability to address unique debtor situations effectively. Moreover, the manual nature of these practices increases the risk of errors and inaccuracies, further prolonging the debt collection process.

Diversified Communication Channels Are Essential

In today’s digital world, consumers expect and appreciate the flexibility to communicate through various channels according to their preferences. Offering multiple communication options not only enhances engagement but also caters to diverse consumer needs and preferences. For instance, some consumers may prefer only text messages or email communication for convenience, while others may only prefer phone calls.

Compliance Constraints Impact Engagement

Compliance regulations play a crucial role in debt collection practices, governing the frequency and manner in which collection agencies can contact consumers. Strict adherence to compliance guidelines is essential to avoid legal repercussions and maintain consumer trust.

However, compliance constraints can limit consumer outreach frequency, reducing engagement opportunities and debt recovery.

Missed Payment Opportunities Due to Limited Agent Availability

The availability of agents is a critical factor in debt collection operations. However, agents cannot be available round-the-clock to address consumer queries and concerns. As a result, collection agencies may miss out on payment opportunities when consumers reach out for assistance during off-hours or weekends.

Elevated Collection Costs

Traditional debt collection methods often incur high operational costs due to extensive manual processes and resource-intensive workflows. These costs include expenses related to manpower, infrastructure, training, and compliance. High attrition rates further exacerbate operational challenges, as collection agencies must invest in recruiting and training new agents to maintain workforce consistency. Non-compliance with regulatory requirements can result in hefty fines and penalties, adding to financial burdens.

Understanding Debt Collection With Conversational AI

Automated debt collection involves using conversational AI software to contact consumers (inbound or outbound) and optimize debt collection at various stages. Organizations can automate numerous stages of debt collection such as consumer outreach, right-party contact verification, payment reminders, negotiation, on-call payments, follow-ups, and answering queries; with conversational AI.

This can help improve the efficiency and effectiveness of debt collection processes, leading to better financial outcomes for the organization and the customers.

What Is Multichannel Conversational AI?

Multichannel communication is a critical component of automating debt collections through AI. Multichannel communication refers to the use of multiple channels, such as email, SMS, phone calls, and webchat, by collection agencies to engage with debtors. By utilizing multiple channels, AI-driven systems can effectively reach debtors, providing convenient avenues for them to address and resolve their outstanding debts.

This multichannel approach caters to consumer preferences by offering a range of communication options, ensuring that each consumer can engage using their preferred channel. Furthermore, this strategy is context-based, meaning consumers can seamlessly switch between channels without losing the context of their previous interactions.

How Does Multichannel Conversational AI Help in Debt Collection?

The debt collection software market has been growing steadily, with most agencies adopting conversational AI to automate their processes and become more efficient while reducing collection costs. The debt collection software industry is expected to reach USD 2045.6 million by 2030. The industry is also anticipated to see further growth with a CAGR of 9.2% in the near future, especially with the emergence of LLMs (large language models) like ChatGPT.

Tailoring Communication to Preferred Channels for Increased Engagement

Reaching debtors through their preferred mode of communication ensures personalized interaction. Understanding that not everyone may be available for phone calls, leveraging various channels allows agencies to effectively engage consumers in ways that resonate with them. This becomes particularly relevant in the wake of the digital shift witnessed post-COVID-19. Embracing multichannel communication expands opportunities for engagement, aligning with evolving consumer behaviors and preferences.

According to a case study by Salesforce, a debt collection agency achieved a 12% increase in its collection rate by implementing personalized communication strategies tailored to customer personas. AI algorithms, debtor data, and consumer behavior are analyzed to customize communication approaches.

Improved Collections with Enhanced CX

Agencies increase contact rates and engagement levels by leveraging multiple communication channels. This improved communication in turn fosters a positive customer experience, as debtors receive timely resolutions and responses. Enhanced customer experience facilitates debt resolution and strengthens the agency’s reputation and customer relationships.

Compliance and Risk Mitigation

Utilizing a multichannel approach for debt collection not only promotes efficiency and effectiveness but also helps businesses comply with evolving regulations in a fast-changing industry. Every communication is documented and traceable and remains within regulatory limits with centralized consumer interactions.

Agencies can stay compliant in many ways with the multichannel approach for communication with consumers. For instance, the 7-in-7 rule mandates that debt collectors cannot contact consumers more than seven times within seven consecutive days. Similarly, the Mini Miranda rule stipulates that collectors must disclose their identity and the purpose of their communication during contact.

Agencies can drive campaigns through various channels, stay compliant with these laws, or even efficiently track and regulate outreach frequencies with each consumer.

Managing Inbound Queries and Ensuring 24/7 Availability

Multichannel communication ensures that consumer queries are promptly addressed whenever they reach out be it on weekends or after work hours. Whether it pertains to payments or other inquiries, AI software can answer consumer queries, ensuring zero wait times and seizing every collection opportunity. Chat options provide consumers with a self-serve menu, enabling them to address basic FAQs and clarify queries easily.

Cost of Collection

Multichannel conversational AI significantly reduces the cost of collection by streamlining workflows, optimizing agent bandwidth, and minimizing manual intervention. By automating repetitive tasks and leveraging multiple communication channels, AI-driven software enhances operational efficiency and reduces overhead costs associated with debt collection processes.

The Impact of Multichannel Conversational AI Automation on Debt Collection

Collection agencies have observed significant impacts on their collections through multichannel conversational AI.

Conclusion

Automated multichannel conversational AI represents a shift in debt collection practices, offering agencies the tools and capabilities to streamline processes, enhance customer experiences, and drive better outcomes.

Agencies can achieve higher contact rates, personalized interactions, and improved efficiency, ultimately leading to faster debt recovery and increased profitability; by leveraging AI-powered systems to automate communication across multiple channels. As the debt collection landscape evolves, organizations embracing multichannel conversational AI will position themselves for long-term success.

How Is the ARM Industry Adopting AI?

Posted on April 4, 2024April 4, 2024 by Shrutidhara Kakoti

The term “AI” is being thrown around a lot these days. Anyone and everyone is trying to integrate AI into their businesses. Similarly, the debt collection industry has adopted AI and is catching on quickly. From automation to data analytics, AI has taken over the ARM space. However, there’s often a lack of understanding about the fundamentals of this emerging technology.

Recently, Skit.ai hosted a panel discussion in collaboration with Accounts Recovery.net. The discussion featured four renowned experts: Heath Morgan of Martin Golden Lyons Watts Morgan, Lucas Brown of NLP Logix, and Amit Ambre of Skit.ai, who answered pressing questions related to AI and its impact on the ARM industry.

The panel covered topics such as the various types of AI and their applications in the industry, compliance regulations for AI, the use of LLMs for improved customer experiences, and the advantages of agencies using AI—the discussion aimed to help non-IT professionals better understand AI technology and its potential benefits for collections.

In this article, we’ll revisit some of the insights from the event.

Understanding the Different Types of AI and What They Are Doing for the ARM Industry

So, What Exactly Is Artificial Intelligence?

“The evolution of AI has been a long time in the making. At its core, AI refers to computers mimicking human behavior, but it’s a broad concept with many applications and iterations over the years. We can even trace the roots of AI back to early statistical methods like linear regression.” — Lucas Brown, Senior Data Science Advisor for NLP Logix.

“The recent boom in computing power is the real game-changer. It’s not just storage; advancements in transformers, neural networks, and deep learning have enabled AI to learn and adapt, exceeding mere information processing. AI can now predict text or anticipate actions with human-like skills. This adaptability is why AI creates such a stir – a stark contrast to its earlier limitations.” — Amit Ambre, VP at Skit.ai.

“Previously, AI was primarily used at the enterprise level by companies like IBM Watson. However, with the release of tools like ChatGPT a year and a half ago, AI became available to individual consumers and developers. This open approach, where users could experiment and find their applications, led to rapid adoption. This shift in accessibility, with tools available for free or at low cost, has flooded the market in the last year.” — Heath Morgan, Attorney, and Partner, Martin Golden Lyons Watts Morgan.

ChatGPT, Machine Learning, Robotic Process Automation, Chatbots… Are all of those different types of AI?

“From a holistic perspective, when forming an AI policy or committee, it’s wise to encompass everything under the “AI umbrella” initially. Then, as you delve deeper, you can determine if you’re dealing with a true black box algorithm, a generative process, or something more akin to a rule-based system.” — Heath Morgan, Attorney and Partner, Martin Golden Lyons Watts Morgan.

“AI is a broad term. While ML focuses on building algorithms that can be trained to predict future actions or outcomes, RPA, in its technical sense, deals with coding specific processes to be replicated in the same way repeatedly. Machine learning can adapt. Deep learning is a further subset of machine learning. Transformers allow deep learning models to work with unlabeled data, come up with a model, and then be further refined for specific tasks. This is how large language models (LLMs) came into play.” — Amit Ambre, VP at Skit.ai.

What are the Types of AI Available for the ARM Industry?

The ARM industry is embracing AI to streamline processes and improve efficiency. This technology takes a few forms: AI can analyze vast amounts of data to predict a debtor’s likelihood to repay, allowing collectors to prioritize their efforts. AI-powered bots can handle initial contact with debtors, answer questions, and facilitate payment arrangements. Additionally, AI can sift through documents and communications, extracting key information and identifying the best course of action for each case.

“Let’s categorize the four main types of AI technology impacting our industry.

Here’s a high-level breakdown:

Data Monetization: This involves maximizing the value of the data we collect, including data collection itself.
Generative AI: This technology can generate content, answer questions, or create new data points.
Robotic Process Automation (RPA): RPA automates repetitive tasks, improving efficiency.
Conversational AI: This allows for interaction with machines using natural language.

These four categories are seeing significant adoption and impacting the debt collection space.” — Heath Morgan, Attorney and Partner, Martin Golden Lyons Watts Morgan.

How Much Better Is a Collection Agency Using AI Doing vs. an Agency That Is Not?

According to the report on “Next-gen Collections Contact Strategy” by BCG, financial institutions using AI have seen:

“I can’t put a number on it, but I’ll break it down into 3 categories:

1) In agencies where account penetration is a problem, Conversational AI can ramp up the scale and achieve maximum account penetration. Agencies with 70-80% untouched accounts leveraged conversational AI to boost collection rates by 30-40%

2) Inbound bots with 24/7 availability for non-working hours are another impactful application. They address the challenge of missed opportunities to speak with consumers and collect payments.

3) Improving Scoring strategy: AI can incorporate a wider range of parameters to predict who to contact and the most effective communication channel and strategy, leading to greater payment predictability and a 10-20% collection rate jump in some cases.” — Amit Ambre, VP at Skit.ai.

“AI adoption requires a scientific approach. Test your ideas, challenge assumptions, and be skeptical. This helps you adapt faster – to new projects, changing customers, or internal shifts. You’ll integrate models quicker, identify consumer behavior changes faster, and adjust more efficiently. A culture of scientific exploration is key to successful AI adoption.” — Lucas Brown, Senior Data Science Advisor for NLP Logix.

“AI should be seen as a digital assistant, handling mundane tasks and freeing employees to focus on higher-level work. This approach creates a co-working environment where humans and AI work together. Employees will upskill to become data-driven decision-makers, leveraging the insights generated by automation and AI.” — Heath Morgan, Attorney and Partner, Martin Golden Lyons Watts Morgan.

How does the new FCC ruling affect the use of AI bots?

With the newest FCC language classifying AI Bots as pre-recorded, how does this impact compliance when using services like Skit.ai or other AI-powered outbound calling systems?

On February 8, the Federal Communications Commission (FCC) unanimously adopted a Declaratory Ruling stating that calls made with AI-generated voices are “artificial” under the Telephone Consumer Protection Act (TCPA). The Ruling clarifies the Commission’s intent to further regulate the use of voice cloning technology used in connection with robocall scams targeting consumers.

Can third-party AI integrate with multiple collection systems for comprehensive reporting? Are there any existing AI-powered reporting systems?

“I don’t think you need AI for this. All you need is a well-defined process – a human demonstrates the steps, and Robotic Process Automation (RPA) can automate it.” — Heath Morgan, Attorney and Partner, Martin Golden Lyons Watts Morgan.

“In the past, you needed a data engineer who would code for you. Now, there’s a plethora of tools, some potentially leveraging AI, that can streamline this process. These low-code/no-code options empower you to build the pipeline without extensive coding expertise.” — Lucas Brown, Senior Data Science Advisor for NLP Logix.

Should You Be Using ChatGPT To Write Responses to Written Consumer Complaints?

“One of the most critical ones is protecting confidential consumer information (PII). When using a service like ChatGPT, you’re essentially disclosing information to a third party. While you might be able to pay for some level of privacy, you lose control over deleting or managing that data. That being said, with proper safeguards in place, using ChatGPT as a tool within the customer complaint response process can be a viable option.” — Heath Morgan, Attorney and Partner, Martin Golden Lyons Watts Morgan.

“ChatGPT presents a data privacy concern. Any information you input can be considered public. It all goes into a database, and you relinquish control over it. This lack of control can lead to various challenges down the line.” — Lucas Brown, Senior Data Science Advisor for NLP Logix.

“You can use it to make the process more efficient. If it’s an activity that involves 10-20 people, it might make sense to build your own model to monitor the responses. Just keep data confidentiality under control.” — Amit Ambre, VP at Skit.ai.

Conclusion

“The rapid evolution of AI, with future advancements (GPT-5.0) on the horizon, presents a significant opportunity. Each iteration brings remarkable improvements in capability. The key takeaway is for every agency, vendor, and company to embrace AI. This involves understanding how to best integrate AI technologies across various processes, adhering to compliance boundaries, and fostering a culture of continuous learning within your organization to maximize the benefits of AI.” — Amit Ambre, VP at Skit.ai.

“The vast potential of AI applications can be overwhelming. Remember, there’s a spectrum of options. Some require significant organizational changes and investment, while others offer easier adoption and lower costs. The key is to find those “sweet spots” – opportunities where you can experiment with AI at a low barrier to entry. These initial successes can then become the springboard for further AI adoption within your organization.” — Lucas Brown, Senior Data Science Advisor for NLP Logix.

“AI adoption is a two-way street: intentional or unintentional. Without a clear AI policy, you risk employees and vendors using AI tools without your knowledge. Do you want unregulated AI use or a policy that dictates how employees can leverage this technology? An AI policy is essential for intentional AI adoption within your organization.” — Heath Morgan, Attorney, and Partner, Martin Golden Lyons Watts Morgan.

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Author: Shrutidhara Kakoti

Conversational AI Can Help RCM Providers in Early-Out Collections and Here’s How