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Author: Shrutidhara Kakoti

How Auto Finance Companies Can Collect End-to-End Without Any Agent Intervention

Posted on by Shrutidhara Kakoti

The current economic volatility is affecting auto finance companies directly, and so are inflation and other consumer behavioral trends, making it a genuinely complex space to be in.

Tracking the Fitch Ratings on Subprime Auto ABS (Asset Backed Security) provides a better understanding of the circumstances in 2024:

  • Subprime auto delinquency rose to 6.39% in February, the highest in the decade.
  • The industry usually expects a recovery in March and April due to tax refunds. Delinquency rates did drop to 5.23% in April.
  • Even with this drop, the delinquency trends are the highest in the last decade. The recovery rates were the second worst, second only to the April 2020 rates.

With the auto finance industry expected to grow at a 7% CAGR, controlling delinquencies in a less affordable market troubled by high inflation rates is a challenge.

In addition to these macroeconomic stresses, auto finance companies face an acute skilled labor shortage. This cumulative effect has made auto finance companies scout for automation solutions that can solve their challenges on all fronts.

Relying solely on traditional collection strategies is not enough in this current landscape. Adopting innovative technological solutions has become a necessity. Before Conversational AI, no tech had the capability to automate collections and customer support calls without the need for agent intervention. 

How Multichannel Conversational AI Can Empower an Auto Finance Company with Automation

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

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

Conversational AI for Inbound Communications 

Inbound communications are a gold mine for collections. Customers often reach out with various inquiries, frequently seeking help to make a payment or process their transaction.

Without Conversational AI: Most auto finance collection processes depend on agents to handle inbound customer inquiries. However, if customers attempt to reach your business during off-hours or on weekends and holidays, they often can’t get connected with an agent, leading to missed payment opportunities. Moreover, maintaining a well-trained team of agents amid rising attrition makes it increasingly challenging to provide reliable inbound support, making it harder for customers to simply make their payments.

With Conversational AI: All inbound communications are seamlessly managed. Every call is answered, every SMS and email receives a reply, and no payment opportunity is missed. It can also schedule follow-ups and calls for later if requested or needed.

Conversational AI’s Impact:

  • Better collections as willing consumers have access to easy payment options
  • Better disposition and intent capture 
  • Improvements in CSAT scores
  • Better customer experience as financiers can listen to and record every customer query.

Conversational AI for Outbound Outreach

Conversational AI revolutionizes customer outreach by automating end-to-end collections, allowing auto finance companies to efficiently scale their operations. 

Without Conversational AI: Traditionally, reaching out to customers involved significant manual effort, with agents making individual calls and sending messages. 

With Conversational AI: Collections are streamlined, enabling thousands of calls to be placed and SMS or emails to be sent within minutes. This not only saves time but also ensures that outreach efforts are consistent, widespread, and personalized, significantly improving scalability and operational efficiency.

Automated Outreach for Better Auto Finance Collections

Multichannel Outreach for Maximum Engagement

Today’s customers—especially those from Gen Z—prefer to interact across various communication channels. They are no longer limited to a single mode of communication like phone calls; instead, they expect to be reached via SMS, email, or other digital platforms. Conversational AI excels in this area by facilitating multichannel outreach, ensuring communication is tailored to each customer’s preferred method. By engaging customers where they are most active, auto finance companies can achieve higher response rates and better engagement.

Seamlessly Consistent Communication

A key advantage of using Conversational AI for outreach is its ability to integrate and never lose the context of communications across all channels. Whether a customer responds via email, SMS, or phone, the AI ensures that all interactions are a part of a unified communication strategy. This prevents any communication gaps or inconsistencies, providing customers with a smooth and coherent experience.

End-to-End Collections without Agent Intervention

Conversational AI can establish right-party contact, capture promise-to-pay (PTP), and facilitate on-call payment collection without needing any agent intervention until asked for or during any complex situation. Customers can easily make payments using a card-on-file or through a secure text-based payment link, streamlining the payment process. This approach can also lead to faster collections without compromising the customer experience, ensuring that payments are collected promptly while maintaining a positive relationship with the customer.

Facilitating Negotiations and Payment Plans

Conversational AI is not just about collecting payments—it can also handle more complex interactions, such as negotiating payment terms and setting up customized payment plans. For customers facing financial difficulties, the AI can offer flexible solutions that align with their current situation, such as extended payment deadlines or installment plans.

Automated Payment Reminders

Conversational AI can automatically send payment reminders via SMS or other channels to further enhance the payment collection process. These reminders can be scheduled at optimal times to ensure they are received when the customer is most likely to take action. By proactively reminding customers of upcoming or overdue payments, the AI reduces the likelihood of missed payments and improves overall collection rates. This feature is especially useful in maintaining regular cash flow and ensuring customers remain on track with payment obligations.

Impact of Conversational AI

Conclusion

Multichannel Conversational AI offers auto finance companies a powerful tool to automate and optimize their collections process. By integrating multiple communication channels and leveraging AI-driven technology, companies can enhance customer engagement, streamline operations, and achieve faster debt recovery without sacrificing customer experience. 

Whether it’s managing inbound communications, automating outreach, facilitating negotiations, or sending timely payment reminders, conversational AI provides a comprehensive solution that drives better outcomes. As the auto finance industry evolves, embracing this technology will be key to staying competitive, improving profitability, and building stronger, more responsive customer relationships.

Are you interested in learning more about how Conversational AI can improve your collections strategy? Book a demo to schedule an appointment with one of our experts!

Why the Auto Finance Industry Needs Contact Center Automation

Posted on by Shrutidhara Kakoti

The auto finance industry is experiencing significant transformations driven by market dynamics, consumer behavior, and technological innovations. Here are the key trends shaping the future of auto finance, focusing on the implications for Buy Here Pay Here (BHPH) dealers and the role of Conversational AI and contact center automation in streamlining operations, which will help the industry navigate turbulent times.

Key Trends

Increased Vigilance Required for BHPH Players

The demand for used cars has surged, putting pressure on BHPH players to be more cautious and vigilant about their loan approvals and collection processes. With the rise in used car sales, BHPH dealers must maintain stringent oversight to mitigate risks associated with subprime auto loans. Effective loan management and collection strategies are crucial in ensuring financial stability and minimizing delinquencies.

Negative Equity and Rising Debt

Negative equity on car loans is emerging as a major concern. As car prices stabilize, many buyers are left with higher-than-average debt, resulting in them being underwater on their loans.

Rising Used Vehicle Loan Rates

Used vehicle loan rates have increased, averaging a 23 basis point (bps) rise year over year. This could potentially lead to higher delinquency rates and higher repossessions.

Longer Loan Terms at Record Levels

Both 60-month and 48-month auto loans are at their highest levels in the last 15 years. This shift towards longer loan terms makes monthly payments more affordable and may extend the repayment period. Without an efficient collection strategy, it may become difficult for auto finance companies to recover the loans. 

Near Record-High Amounts Financed

The average amount financed for auto loans is nearing an all-time high of around $40,000 USD, reflecting the rising costs of vehicles.

Affordable New Car Rates and Transaction Trends

According to Moody’s Affordability Index, while the average transaction price for new cars has declined in 31 months due to more affordable rates in 2024, it remains one of the highest in a decade. This indicates a shifting market where affordability is improving, but high transaction values persist. 

The Solution to Overcome the Current Environment: Contact Center Automation with Conversational AI

As the auto finance industry faces various challenges—from rising loan rates to increased negative equity—innovative solutions for a compelling collection are more critical than ever. Contact center automation with Conversational AI has emerged as a powerful tool for auto finance and BHPH companies.

Inbound Contact Center Automation: Enhancing Consumer Experiences

Zero Wait Time: Traditional Contact centers often frustrate consumers with lengthy IVR menus and extended wait times, leading to high drop-off rates. With an average of 15% to 20% of consumers dropping off at the IVR menu, there is a significant loss of collection opportunities. Implementing conversational AI systems like Skit.ai, which provide contact center automation, can eliminate wait times and enhance consumer satisfaction by providing immediate assistance.

Personalized Consumer Interaction: Conversational AI integrates with existing CRM systems to offer a personalized approach to consumer service. This integration allows the AI to recognize the consumer’s identity and recall previous interactions, providing a seamless and customized experience. Such systems can fetch consumer profiles in milliseconds, improving the efficiency and effectiveness of the service.

Best Engagement Channels: Today’s consumers are less likely to answer calls from unknown numbers, with over 90% ignoring such calls. Engaging consumers through SMS and voice can lead to higher response rates, as text messages have double the response rate of voice calls. Offering payment channels through both mediums can improve engagement and collection rates.

Read our blog: Automate Your Auto Finance Collections with AI-Powered Text Messaging

24/7 Inbound Support: The lack of support over weekends often leads to missed collection opportunities. By providing 24/7 consumer support, auto finance companies can ensure continuous engagement and reduce the chances of delinquencies.

Outbound Contact Center Automation: Maximizing Engagement and Recovery

Increased Attempts and Engagement: Higher engagement is essential for ensuring timely payments. Infinite scalability in outbound contact center automation allows for more attempts to contact consumers, which is crucial for BHPH players who cannot afford prolonged delinquent cycles. Increased engagement during the DPD 0-21 phase can significantly enhance recovery rates.

Prioritizing Loan Payments: Engaging consumers over weekends can prevent auto payments from being deprioritized. Most consumers get paid on Fridays, and without engagement, they may spend on non-discretionary items. Automated calls over the weekend can remind consumers of their auto payments, reducing Monday delinquencies.

Multichannel Payment Integration: Offering multiple payment channels and automating collections through phone payments can streamline the process. Integrating card-on-file or user-defined card options and setting up auto payments can improve collection efficiency.

Payment Negotiations and Alternative Plans: Consumers facing unforeseen events such as job loss or medical expenses need proactive engagement. Offering alternative payment plans based on their payment history can enhance consumer satisfaction and ensure better recovery rates.

Benefits of Using Skit.ai for Contact Center Automation

Experience and Trusted Name: Skit.ai is a trusted name in the auto finance industry, featured among the top 500 companies in Auto Remarketing. It collaborates with renowned names such as Veros Credit, PeakBHPH, and Sensible Auto, ensuring credibility and reliability.

Low Lift Integration Effort: Skit.ai offers seamless integration with built-in dialer platforms and CRMs like DealerSocket- IDMS and Automaster. It also integrates with common payment gateways such as PayNearMe, making the transition to automated systems smooth and efficient.

Conclusion

Integrating Conversational AI and contact center automation is not just a technological upgrade but a strategic shift toward a more efficient, consumer-centric, and financially robust collection model. Companies that embrace these technologies will be better positioned to navigate the complexities of the modern auto finance landscape, stay ahead of the competition, and deliver superior experiences to their consumers and stakeholders.

As the auto finance industry evolves, adopting conversational AI and contact center automation will be key to enhancing operations, providing a better consumer experience, and improving recoveries with minimal effort.

Curious to learn more about how Skit.ai’s Conversational AI can maximize your account penetration? Book a free demo with one of our experts.

Rise of Delinquent Accounts in Subprime Lending

Posted on by Shrutidhara Kakoti

The auto finance industry, a crucial pillar in the automotive market, experienced a turbulent Q2 in 2024. The rise of delinquent accounts in subprime lending has become a significant concern for industry stakeholders. Subprime lending, which targets borrowers with lower credit scores, is inherently riskier, and recent economic pressures have worsened these risks. This blog delves into the current landscape of the auto-finance industry, especially last quarter Q2, and discusses how the industry can tackle this concern.

Subprime Lending in the Auto-Finance Industry

Subprime lending involves offering loans to borrowers with lower credit scores, typically below 620. These borrowers are considered higher risk due to their credit history, including previous delinquencies, defaults, or bankruptcies. Lenders often charge higher interest rates and fees to compensate for the higher risk. In the auto-finance industry, subprime loans enable a broader demographic to purchase vehicles. However, this lending segment is also more vulnerable to economic fluctuations.

The Current Landscape: Delinquent Accounts on the Rise

In 2023, the auto loan delinquency ratio at U.S. banks reached its highest level in the past decade. According to S&P Global Market Intelligence data, the delinquency ratio at U.S. banks was 3.32% at the end of 2023, the highest since 2013. This increase occurred even though the industry’s total amount of auto loans fell to $530.38 billion from $548.40 billion in 2022, marking the first year-over-year decline since 2013.

Fitch Ratings says delinquent accounts and net losses have been trending higher while recovery rates have fallen, signaling weakened performance across the board in Q2 of 2024. Historically, the first quarter of the year benefits from a seasonal boost as borrowers utilize tax refunds to catch up on delinquent loans. However, in 2024, this boost was notably weaker. Economic pressures, coupled with greater outstanding balances from weaker-performing assets, have diminished the positive impact typically seen from January to April.

In April 2024, the delinquent account rate stood at 5.23%, a decline from the all-time high of 6.39% recorded in February. This decrease follows the typical pattern where borrowers use their tax refunds to catch up on loan payments. However, the seasonal improvement this year was less pronounced than in previous years, with delinquent account rates at 4.67% in April 2023 and 3.86% in April 2022.

Recovery rates also suffered in April 2024, dropping to a low of 43.03%, a stark contrast to 54.96% in April 2023 and 62.51% in April 2022. This decline in recovery rates highlights the challenges lenders face in recouping funds from delinquent accounts.

Additionally, the net loss rate in April 2024 was 7.90%, significantly higher than the 6.16% observed in April 2023 and the 4.13% in April 2022. This increase in net losses underscores the financial strain on lenders within the subprime auto loan market.

What Can the Industry Do to Reduce Delinquent Accounts?

While the auto-finance industry cannot directly eliminate the rise in delinquent accounts among subprime borrowers, it can take steps to improve recovery rates. This cannot be done by simply increasing the number of collection agents. Although adding more agents might boost recovery rates to some degree, it would also significantly raise operational costs, which is not the way any company would want to go. 

So, Is There a Solution?

The answer is yes. 

Technology, particularly Conversational AI, has been a game changer for the auto finance industry. With the rising delinquencies, leveraging Conversational AI has become essential for auto finance companies to enhance their collection efforts and automate processes.

But how does Conversational AI help?

Conversational AI and automation technology can significantly enhance collection processes. By automating end-to-end collections, engaging borrowers, and ensuring compliance with regulatory requirements, these technologies contribute to higher recovery rates. A multichannel conversational AI platform can call and text customers any day of the week, engaging them in human-like conversations while maintaining compliance. It can handle the entire collections process, including customer verification, disposition capture, and payment processing, without needing agent intervention. 

Conversational AI can dial thousands of calls per minute and send thousands of SMS, ensuring scalable, comprehensive, and compliant engagement across your consumer portfolio. Conversational AI can handle inbound queries and collect payments at any time, enabling 24/7 collections without requiring agent intervention. Additionally, AI-driven analytics provide valuable insights into borrower behavior, allowing lenders to customize their strategies and enhance overall collection efficiency.

Skit.ai’s Multichannel Conversational AI

How Skit.ai helps in recoveries from delinquent subprime accounts

Conclusion

The second quarter of 2024 has been a turbulent period for the auto-finance industry, marked by a rise in delinquent accounts within subprime lending. While economic pressures and weaker-performing assets have aggravated the situation, the industry’s response to adopting conversational AI to help improve collection efforts offers a path to stabilization. As we move into the year’s second half, all eyes will be on how these measures impact the broader landscape of subprime auto lending.

Curious to learn more about how Skit.ai’s Conversational AI can maximize your account penetration? Book a free demo with one of our experts.

Beyond Automation: Top 6 Conversational AI Companies

Posted on by Shrutidhara Kakoti

Since the advent of ChatGPT, Conversational AI has received a significant boost across various industries. Conversational AI is no longer just automating minor tasks; it can now solve complex issues and provide meaningful resolutions to customers. This transformative technology enhances customer interactions by understanding context, emotions, and intent, leading to more personalized and effective communication.

Conversational AI has found applications across various industries, enhancing customer engagement, operational efficiency, and service delivery. In healthcare, Conversational AI facilitates patient interactions through virtual assistants that offer personalized medical advice and manage appointment scheduling. In finance, Conversational AI powers virtual financial advisors, providing real-time investment insights and transaction support. Retail utilizes chatbots for personalized shopping experiences, product recommendations, and customer support. In education, Conversational AI supports virtual tutoring and adapts learning materials to individual student needs.

Many Conversational AI companies in the US are significantly revolutionizing contact center operations. These companies offer solutions that are improving efficiency, ensuring better compliance, and enhancing customer satisfaction by leveraging advanced AI technologies. Let’s take a closer look at the top six conversational AI companies leading the way in the US.

Here are the top 6 conversational AI companies in the United States:

  1. Amazon Lex
  2. Freshworks
  3. Sprinklr
  4. Skit.ai
  5. Yellow.ai
  6. Kore.ai

Amazon Lex

Amazon Lex is a fully managed AI service equipped with advanced natural language models to design, build, test, and deploy conversational AI interfaces within any application using voice and text. Amazon Lex also powers the Amazon Alexa virtual assistant. Released to the developer community in April 2017, Amazon Lex can be used for a variety of conversational AI interfaces, including chatbots for web and mobile apps, as well as interactions for robots, toys, drones, and more. While Amazon Alexa Voice Services allows developers to integrate Alexa into their devices, Amazon Lex provides flexibility for end users to interact with any type of assistant or interface, not just Alexa. As of February 2018, users can define responses for Amazon Lex chatbots directly from the AWS management console.

Freshworks

Freshworks Inc., founded in 2010 in Chennai, India, is a cloud-based software-as-a-service company. It offers cloud-based tools for customer relationship management (CRM), IT service management (ITSM), and e-commerce marketing. One of its key products, the Customer Service Suite, is an all-in-one conversational AI customer support solution that enhances business-customer interactions. The suite enables personalized self-service experiences with conversational AI-powered chatbots, helping businesses optimize operational efficiency and deliver exceptional customer support. The Customer Service Suite equips businesses to anticipate customer needs and deliver unparalleled service experiences by providing a comprehensive view of customer conversations and integrating various tools using conversational AI.

Sprinklr

Sprinklr is an American software company based in New York City that develops a SaaS customer experience management (CXM) platform. The Sprinklr platform integrates various applications for social media marketing, social advertising, content management, collaboration, employee advocacy, customer care, social media research, and social media monitoring.

Sprinklr has integrated AI across four product suites: Sprinklr Service, Sprinklr Social, Sprinklr Marketing, and Sprinklr Insights, along with self-serve offerings. This unified platform, built on a single codebase with an operating system approach, provides customers with the tools they need to deliver exceptional experiences. By enabling seamless collaboration among customer-facing teams, markets, and geographies, Sprinklr offers brands a unified digital edge.

Skit.ai

Skit.ai is the leading Conversational AI company in the accounts receivables industry, enabling collection agencies and creditors to automate collection conversations and accelerate revenue recovery. Skit.ai’s suite of multichannel solutions—featuring voice, text, email, and chat in both English and Spanish, powered by Generative AI—interacts with consumers via their preferred channel, elevating consumer experiences and consequently boosting recoveries. Skit.ai has automated collection calls for many collection agencies in the US and several major banks in India.

Skit.ai is revolutionizing the accounts receivables industry by enabling companies to automate and accelerate consumer interactions at scale using Conversational AI. By integrating existing dialer systems, seamless conversational capabilities powered by Generative AI, and fast campaign analytics, Skit.ai’s suite of multichannel Conversational AI solutions retains context across channels, boosting efficiency and elevating consumer experiences.

Skit.ai has received several awards and recognitions, including the BIG AI Excellence Award 2024, Stevie Gold Winner 2023 for Most Innovative Company by The International Business Awards, and Disruptive Technology of the Year 2022 by CCW. Skit.ai is headquartered in New York City, NY. 

Yellow.ai

Yellow.ai, formerly known as Yellow Messenger, is a multinational company headquartered in San Mateo, California, specializing in customer service automation using Conversational AI. Founded in 2016, the company provides an AI platform designed to automate customer support experiences across chat and voice channels. Supporting more than 135 languages and over 35 channels, Yellow.ai has become a global leader in generative AI-powered enterprise customer service automation. Yellow.ai’s platform helps enterprises achieve exceptional efficiency in customer service while significantly reducing operational costs. Their solutions cater to customer support, employee experience, and sales across BFSI, retail, and healthcare industries. The platform features an enterprise-grade conversational AI system with a no-code builder, making it accessible and adaptable for various business needs.

Kore.ai

Kore.ai develops an enterprise conversational AI and generative AI platform designed to help organizations design, develop, test, and manage chatbots for both internal and customer-facing scenarios. The company’s innovative platform, no-code tools, and solutions deliver comprehensive customer and employee experiences, from automated to human-assisted interactions, and support the creation of generative AI-enabled applications. Kore.ai adopts an open approach, allowing companies to select the LLMs and infrastructure that best suit their business needs and assists customers in navigating their AI strategy. With a strong patent portfolio and recognition as a leader and innovator by top analysts, Kore.ai is headquartered in Orlando and supported by a global network of offices.

DISCLAIMER
This list is based on subjective research and experiences, along with information gathered from various online sources, including web articles and search engine results; it is not intended to imply any specific ranking order and should be used solely as a reference guide.

Curious to learn more about how Skit.ai’s Conversational AI can automate your contact center operations? Book a free demo with one of our experts.

Rising HDHPs and the Cure for RCM Providers: Multichannel Conversational AI

Posted on by Shrutidhara Kakoti

High Deductible Healthcare Plans (HDHPs) have become the preferred insurance option for many Americans primarily due to their lower premiums. They are also a popular health insurance plan offered by private-sector employers. In 2022, more than half of U.S. private-sector workers (53.6%) were enrolled in HDHPs.

However, HDHPs have a higher deductible than traditional insurance plans, meaning individuals must cover more healthcare expenses out of pocket before the insurance company starts contributing. 

In this blog post, we will discuss the rising popularity of HDHPs and their implications for RCM providers and Extended Business Offices (EBOs). Additionally, we will explain why Conversational AI technology is a game-changer for early-out collections.

Why Are High Deductible Healthcare Plans (HDHPs) Becoming Popular?

Rising Health Insurance Costs

With rising health insurance costs and hospital charges, people are opting for HDHPs, which have lower premiums.  The American Medical Association (AMA) reports that healthcare costs are climbing at approximately 4.5% annually. In 2019, healthcare spending in the United States increased by 4.6%, reaching a staggering $3.8 trillion nationwide, equating to an average of $11,582 per person. This increase aligns closely with the rates seen in 2018 (4.7%) and slightly surpasses those of 2017 (4.3%).

Besides the ongoing trend of healthcare costs inching upward, short-term factors have also played a significant role. Many U.S. residents experienced faster-than-average increases in their health insurance costs in 2021, as insurance companies and healthcare providers raised costs post-pandemic.

As a result, over half of all U.S. workers were enrolled in high-deductible health plans (55.7%). Enrollment for HDHPs has risen for the eighth consecutive year in 2023, the highest enrollment rate since 2012. 

HDHPs are Cheaper for Employers

Employers regularly seek strategies to offer stable benefits while reducing costs. This practice enhances their competitiveness in the job market while keeping expenses in check. 

According to a recent Mercer study, larger employers spend an average of $84 per month on High Deductible Healthcare Plans (HDHPs) per employee, compared to $132 per month for traditional Preferred Provider Organization (PPO) plans. This shift towards HDHPs translates to a significant 37% reduction in costs per employee, with greater savings realized by larger companies.

Flexible Coverages

Beyond cost savings, HDHPs offer enhanced flexibility in healthcare coverage. Unlike Health Maintenance Organizations (HMOs), HDHPs typically impose fewer restrictions, granting individuals greater freedom to select their preferred service providers. This increased flexibility removes hurdles from the healthcare decision-making process, empowering individuals to make more informed choices about their healthcare options.

HDHPs = Savings

HDHPs can also provide additional savings opportunities for individuals. HDHP is the sole Health Savings Account (HSA)-eligible health plan that helps with additional savings. With an HDHP, individuals can establish an HSA to benefit from tax-free saving, investing, and spending on healthcare expenses. HSAs offer several advantages, including the ability to carry over funds yearly without expiration. Unlike other types of accounts, HSAs are owned by the individual and can be transferred between jobs and healthcare providers. Furthermore, HSAs serve as an additional retirement account. 

This ownership of HSA funds provides stability amidst the ever-changing healthcare landscape, allowing individuals to retain their accounts even as they transition to different health plans each year.

What Does This Mean For RCM Providers and EBOs?

The increase in demand and adoption of HDHPs has significant implications for Revenue Cycle Management (RCM) providers and External Business Offices (EBOs) especially when collecting self-pay dues in early-out collections. 

Let’s explore how this trend affects early-out collections and alters the revenue cycle for these businesses.

Increased Patient Self-Pay Dues = Delayed Cash Flow

HDHPs typically come with higher deductibles, meaning patients are responsible for a larger portion of their healthcare expenses upfront before insurance coverage kicks in. As a result, patients may delay or struggle to pay their medical bills, leading to a higher volume of outstanding balances in early-out collections.

With this delay, the revenue cycle for RCM providers and EBOs may lengthen as they wait longer to receive patient payments. This impact on the cash inflow can strain liquidity and hinder financial planning efforts.

Challenges in Collecting Payments

RCM providers and EBOs may encounter challenges collecting payments from patients with HDHPs. The increased self-pay responsibilities require increased engagement efforts from RCMs/EBOs to reach patients and collect payments. There is also a higher denial rate for self-pay dues. This requires additional resources, such as spending time resolving billing disputes and answering queries. 

Need to Enhance Patient Communication

RCM providers and EBOs must prioritize communication to ensure patients are aware of their self-pay dues. This may involve explaining insurance coverage, clarifying billing statements, and offering payment plan options to facilitate timely collections.

Focus on Proactive Payment Strategies

RCM providers and EBOs must adopt proactive payment strategies to streamline billing and payment processes in response to the challenges posed by HDHPs. This approach facilitates the retrieval of self-pay obligations and fosters trust among patients.

How Does Conversational AI Help Expedite Early-Out Collections?

Skit.ai’s Multichannel Conversational AI solution can aid RCMs and EBOs by expediting early-out collections. Here’s how:

Bulk Outreach and Multichannel Engagement

Skit.ai’s AI bot can initiate outreach to patients and engage with them in the following days via multiple channels, such as phone calls (Voice AI), text messages, emails, and chatbots, ensuring effective communication and engagement from the outset. It can manage complex, multi-turn conversations with patients across all channels, maintaining context seamlessly. The Voice AI solution engages with patients in human-like conversations. This ensures meaningful engagement with them and, at the same time, offers scalability to RCM providers to reach out to numerous patients in bulk, thus helping mitigate potential payment delays and improving patient satisfaction.

More Than Just a Call; Available at Patient’s Beck and Call

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.

Enhanced Cash Flow

With more outreach, faster query resolution, and seamless payment options (on-call payments and text-based payment links), Skit.ai enables RCM and EBOs to do more early-out collections of self-pay dues.

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. 

Through automation, operational expenses are reduced, revenues are maximized, and overall productivity within the organization is enhanced. Additionally, this results in decreased staffing needs and reduced training costs for RCM providers and EBOs.

Conclusion

The surge in high-deductible healthcare plans (HDHPs) underscores a shift in early-out collections. The rise in patient self-pay dues under HDHPs requires increased engagement efforts and proactive payment strategies to streamline billing processes and enhance revenue cycles. Additionally, adopting Conversational AI solutions offers a promising avenue for overcoming these challenges. 

Conversational AI improves efficiency, enhances cash flow, and reduces costs for RCM providers and EBOs by facilitating bulk outreach, providing comprehensive patient assistance, and automating repetitive tasks.

Curious to learn more about how Conversational AI can help you gain a competitive edge over your competitors? Book a free demo with one of our experts.

SkiTalks: Abhinav Tushar on Machine Learning and Bias in Conversational AI for Collections

Posted on by Shrutidhara Kakoti

Abhinav Tushar, Skit.ai’s Head of Machine Learning, discusses how LLMs are reshaping automated consumer conversations in the collections space and their direct influence on enhancing the overall consumer experience.

What advancements in Conversational AI are you most excited about currently? 

At Skit.ai, we focus on helping businesses derive value from Conversational AI. We’re particularly interested in enhancing LLM capabilities to achieve difficult conversational goals. While LLMs are capable of having high-quality conversations, they still struggle with reliability in multi-turn conversations. We are focused on aligning with the goals of both the users and the businesses we serve.

How have LLMs changed the way we think about Conversational AI?

The current generation of LLMs has solved the problem of handling believable and natural conversations. Despite some factual issues and minor glitches, LLM bots can maintain the flow of a conversation. Apart from this, there are exciting upgrades for spoken conversations, such as the improved ability to model any behavior that can be meaningfully translated into text. This progress aligns with the promises of Artificial General Intelligence (AGI), and it’s exciting to see us move in that direction.

These advancements are prompting a reevaluation of the potential of automation. For a product like ours—goal-oriented bots—we expect a reduction in modeling complexity to increase the extent of automation, even for dialogs that used to be considered the forte of live agents. 

How do you envision the future of Conversational AI over the next few years? 

Over the next few years, we’ll see a focus on extracting value from this technology. While chat and voice bots have been around for quite some time already, the emergence of LLMs has marked the beginning of a brand new chapter, in which we will see more experimentation with conversational modality added as part of many interfaces. At a lower level, we expect multimodal models to dominate, along with a lot of effort going into integrating these virtual assistants with diverse data sources enabling us to further personalize the interactions with users.

What are some best practices that you follow while working with AI systems?

The most important thing to do is establish clear, measurable, goal-oriented metrics. Safety metrics, like the conversational compliance rate, are crucial in our domain. Without an effective system to monitor business value, we risk deploying products that are either harmful or not useful. This requires a thorough understanding of the Machine Learning (ML) model lifecycle, which remains unchanged despite advancements in LLMs, even though the other intermediate tools have evolved.

What sets Skit.ai’s approach to Conversational AI apart from others in the industry?

Our Conversational AI stack is powered by LLMs connected with speech systems in a complete duplex manner to achieve naturalness, which is an industry standard. However, here are a few elements that set us apart from other providers.

Firstly, at Skit.ai, we prioritize compliance and data security. We use guardrails, flow guarantees, red teaming, reinforcement learning, and other techniques to ensure compliance checks are considered at every stage of model development, deployment, and runtime.

Secondly, for us, goal completion often involves multiple conversations with a user, possibly across multiple channels.

Lastly, we support multi-modality and believe in speech-first Conversational AI. Our approach aims to acknowledge and leverage non-vocal cues from conversations, which have historically been overlooked in real-time Conversational AI.

What are some challenges that companies face in extracting value from AI? 

The two most common challenges in my experience have been (a) not thoroughly understanding how business metrics are connected with low-level models and (b) not respecting the model lifecycle once in production. With the rise of LLMs, executives in every company are pressured to incorporate them in some way, often leading to ineffective efforts.  What’s needed is a two-way conversation between understanding your product’s value chain and an LLM’s capabilities. Additionally, as this technology evolves rapidly, it’s crucial to have a clear vision of the future to avoid working on problems that may soon become irrelevant.

How do you handle AI’s shortcomings in terms of fairness and bias?

Most of the shortcomings can be handled with a little extra effort. The type of models used, and the nature of the product being developed carry more significant bias implications than the algorithm itself. We ensure that our product’s usage complies with AI ethics regulations and regional deployment guidelines. At a lower level, we monitor fairness metrics, prevent the misuse of protected attributes by any model, and select fair algorithms wherever we need them. This is a challenging objective, and new learnings often emerge as we go. We strive to lead the way as we address bias.

Can AI in collections enhance compliance with regulations? If so, how?

Yes, absolutely. While ongoing efforts are essential to ensure compliance with existing and upcoming AI regulations in the collections space, we’re confident that overall compliance regulations are on the rise and will continue to improve with AI adoption. This is not surprising. In fact, there are solutions built specifically to handle and monitor human compliance. Humans make mistakes naturally, and that’s one of the reasons why automation scales. At Skit.ai, we enhance collections with AI not only by adding and improving communication channels but also by interconnecting them and learning from data to create a superior, error-free engine.

Curious to learn more about how LLMs can enhance your collections strategy? Book a free demo with one of our experts.

Gain Competitive Advantage as a Small Third-Party Debt Collector

Posted on by Shrutidhara Kakoti

As a small third-party collection agency, it can be challenging to compete with larger firms. Companies often prefer to assign their accounts to larger agencies because they believe they can manage more accounts and achieve higher collection rates.

However, Conversational AI can change that for you and give you a competitive advantage over other collection agencies, including those larger than yours.

With Conversational AI, you achieve the following:

How AI Can Give an Edge to Small Third-Party Collection Agencies

Curious to learn more about how Conversational AI can help you gain a competitive edge over your competitors? Book a free demo with one of our experts.

SkiTalks: Prateek Gupta on Compliance and Go-To-Market Strategies for Conversational AI in Collections

Posted on by Shrutidhara Kakoti

Interview with Prateek Gupta, Director of Sales

What do you see as the most exciting developments or trends in the Conversational AI space right now, and how do you think they will impact go-to-market strategies? 

“Generative AI has enabled bots to accurately discuss a broader range of subjects. Streaming text-to-speech technology has also significantly reduced the delay between analysis and response by bots, making the entire experience human-like. 

Although Artificial General Intelligence (AGI) has room for improvement, Skit.ai is in an excellent position to cater to various use cases in many industries. The immediate impact on the marketing strategy would be an expansion of the Total Addressable Market within the same industry. Expanding to new markets is dependent on relevant regulations, which keep evolving, and our ability to understand the industry-specific nuances so that we can give proper instructions to AI. AGI is expected to overcome many of these hurdles in the future.”

Could you share some insights into how you approach identifying and prioritizing target markets for Conversational AI solutions? 

“For Skit.ai, the current approach centers around finding markets with large volumes of interactions, typically between business institutions and their customers, and with a limited number of use cases. As our technology gets better and faster at fine-tuning Generative AI models to specific use cases, we will drop the requirement of focusing on limited use cases. Any industry that requires a large number of interactions is a feasible market for Skit.ai. Underserved markets are and will be prioritized.”

How do you navigate the challenges of educating potential clients about the value and capabilities of Conversational AI, especially in industries that may be less familiar with the technology? 

“Educating a nascent industry is tough initially; it takes significant effort and time but presents a golden opportunity to become the go-to partner once the solutions start getting accepted. Talking to as many people as possible improves our knowledge of a new industry and gives them a chance to learn about our solution. 

In a typical Skit.ai sales process, conversion ratios are small but build over time. We generally find some early adopters and work hard to help them be successful and turn them into evangelists. We focus on platforms with the right target audience, which has turned out to be an efficient way to pass on the message. 

To expand our reach beyond the limitations of human interactions, educational digital marketing content is beneficial.”

How do you approach pricing and packaging strategies for Conversational AI solutions, considering factors such as market competition and perceived value?

“Delivering value to customers is the only way to achieve long-term success, and Amazon.com is a great example that comes to my mind. Different industries perceive value in different ways. For us at Skit.ai, the trick is to quantify the perceived value in dollars and charge a fraction of that value for our solution. In the current financial market, the second thing we worry about is our gross margins, so we don’t indulge in pricing wars with the competition. We would rather focus on delivering higher value and charging a small fraction of that value while keeping a healthy gross margin.”

Looking ahead, what are the biggest opportunities and challenges for Conversational AI in terms of growth and delivery, and how do you plan to capitalize on them? 

“As and when the cost of Generative AI goes down, price-sensitive markets and geographies will present a tremendous growth opportunity. With these technologies being commonplace, the challenge will be to keep finding differentiation in our solutions.”

What are some of the key challenges you face when implementing Conversational AI solutions while ensuring compliance with regulations? 

“Regulations keep changing and are influenced by various factors. Sometimes, you are just collateral damage. Small and medium players in a niche market generally don’t have much influence on regulations. Therefore, the biggest challenge is to keep up with changing regulations that apply to us but are not intended for us. This also derails progress.”

In your experience, what are some common misconceptions or myths surrounding compliance in Conversational AI, and how do you address them?

“The most recent myth is that ‘Robocalls are Illegal.’ While that makes for a good headline, if you read the fine print it’s clear that you can use artificial voices to make phone calls if you have prior express consent, which has been a requirement since 1991, when the TCPA was introduced. Educating our users is one way to tackle this misconception. This is where Skit.ai’s early adopters turned evangelists became more helpful than ever.”

How do you approach data privacy and security concerns in Conversational AI implementations, especially considering the sensitive nature of conversational data? 

“Skit.ai follows all data privacy standards and has invested heavily in making our systems compliant with GDPR, SOC2, HIPAA, PCI-DSS certifications.”

What role does collaboration play between your team and regulatory bodies or compliance experts in ensuring that Conversational AI solutions meet the necessary standards? 

“We are not directly involved with regulatory authorities, but we engage with compliance experts to ensure that our solutions comply with all aspects of the regulations governing our clients and us. Due to the evolving nature of the regulations, this is not limited to the solution’s development cycle but is an ongoing process.”

How do you anticipate future regulatory changes and adapt your go-to-market strategies and product offerings accordingly?

“At Skit.ai, we engage with compliance experts to foresee regulatory changes and incorporate them into our product. We also have product owners who double up as internal compliance experts because they are the ones who understand the product best and can ensure that it remains true to regulatory standards.”

Looking ahead, what do you see as the biggest opportunities and challenges in the intersection of Conversational AI and compliance, and how do you plan to address them? 

“We believe that our solution does not engage in any activity that current (or future) regulations are targeting. Our multichannel strategy and enhancement of our inbound voice solution will help us address compliance hurdles that may arise in the future.”

How does Skit.ai’s approach to conversational AI set it apart from others in the industry?

“Most other AI vendors view debt collection as just another industry in which interacting with consumers represents a significant requirement.”

Curious to learn more about how Conversational AI can enhance your collections strategy? Book a free demo with one of our experts.

How Multichannel Conversational AI Can Reduce Collection Cost

Posted on by Shrutidhara Kakoti

What is Multichannel Conversational AI in Debt Collection?

Multichannel Conversational AI automates interactions across various communication channels—such as voice, text, chat, and email—to engage with consumers through their preferred mode of communication and assist them in resolving their debt.

This significantly improves the consumer experience throughout the recovery journey. Consumers can seamlessly switch between channels without losing the context of their previous interactions.

The Multichannel Advantage

What benefits have early adopters of Multichannel AI seen in the accounts receivables industry?

Implementing a multichannel strategy has enabled industry-leading organizations to drastically reduce the cost of collections. Thanks to the technology, live agents can focus on more complex, revenue-generating tasks, while AI handles the most repetitive and routine tasks. This strategy boosts agent productivity and decreases agent dependency, solving the staffing and resource challenges many financial services organizations face.

Here are some examples of the overall improvements in collections a Missouri-based collection agency experienced by leveraging Skit.ai’s suite of Multichannel Conversational AI.

Curious to learn more about how Conversational AI can enhance your collections strategy? Book a free demo with one of our experts.

Faster and Efficient Account Penetration with Conversational AI

Posted on by Shrutidhara Kakoti

Are you grappling with the challenge of reaching out to a vast number of accounts with limited staffing resources?

Is the task of engaging meaningfully with each consumer proving to be a daunting feat? 

How Does Skit.ai’s Multichannel Conversational AI Help With Account Penetration?

Rapid Outreach at Scale

One of the most significant hurdles in collections is the sheer volume of accounts that need attention. Traditional methods involve reaching out to thousands of consumers, which is time-consuming and resource-intensive.

Skit.ai enables collection agencies of all sizes to reach thousands of accounts within minutes. You can automate compliant outbound outreach of your consumer portfolio and ensure 100% account penetration.

Skit.ai doesn’t just enhance outreach efforts; it also enables you to connect with consumers during weekends and after work hours when agents are usually unavailable for outreach campaigns, yet consumers are more inclined to pick up the phone and engage. 

Multichannel Engagement

Why give apples to your consumers when they’ve clearly said they want oranges?

Consumers nowadays have diverse preferences when it comes to communication channels. Depending on their demographics and behavior, they will prefer to use different channels.

Skit.ai offers multichannel engagement capabilities, enabling collections agencies to connect with debtors through their preferred mode of communication. 

Whether it’s sending personalized emails, automated SMS reminders, or initiating interactive voice calls, Skit.ai ensures that agencies can engage with debtors on channels they are most likely to respond to. This strategic approach significantly increases the chances of meaningful engagement and debt resolution, ultimately driving higher recovery rates.

Intelligent Insights and Analytics

Skit.ai provides collections agencies with many actionable insights to guide their decision-making process. It uses debtors’ response patterns to recommend best practices, such as when and how to reach out to effectively engage with debtors.

Merely increasing the frequency of contact with debtors doesn’t always translate to higher connection rates. Skit.ai’s software analyzes data and recommends the optimal number of engagement retries (while ensuring compliance with regulatory bodies) to achieve an optimal connection rate. It can also suggest the optimal mode of communication for better engagement.

Still don’t believe us? Hear it from our customers!

Curious to learn more about how Conversational AI can maximize your account penetration? Book a free demo with one of our experts.

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