When COVID hit in 2020, one Fortune 500 company needed to find an effective way to communicate and collect from the rising overdue accounts, with many of their customers falling into financial hardships. While the company had previously relied on old-school communication tactics like direct mail and an in-house call center to reach customers who had delinquent accounts, they knew a better solution was needed.
The company had already observed firsthand a rise in customers’ preference for digital communications between mobile apps and online bill pay options, making it clear that this was the best route to go. Rather than build from scratch in-house—which would’ve been costly and time-consuming—the company evaluated third-party options before choosing TrueAccord and implementing Retain, the client-labeled early-stage collections solution.
Retain’s digital outreach strategy made a significant impact on customer engagement and resolution beyond just payments with improvements across their paid in full rate, overall collections rate, average amount collected daily, and more. And with the help of HeartBeat, TrueAccord’s powerful machine learning decision engine, they were able to observe behavior data and optimize digital touch points and engagement in real-time. In just a few weeks, this digital collections approach caused a major transformation that only continued to improve.
With more than 1 million consumer accounts now managed through the intelligent, client-branded product, results show 40% more effective than leading “call and collect” vendors
LENEXA, Kan., Jan. 25, 2022 — TrueAccord Corporation, a debt collection company offering machine learning-powered digital recovery solutions, today announced results following the September 2021 rollout of Retain, the client-branded product that addresses early-stage recovery challenges for organizations with customers with delinquent accounts. TrueAccord Retain is now being used by creditors to manage more than 1 million consumer accounts and has shown to be 40 percent more effective at repayment than traditional “call and collect” debt collection vendors.
TrueAccord Retain, which harnesses digital technology and machine learning to deliver a personalized, effective early-stage recovery strategy, significantly outperformed three traditional “call and collect” agencies across several of an anonymous client’s portfolios. Relative to the best-performing “call and collect” vendor for each product portfolio, TrueAccord Retain drove a 24 percent improvement in roll rate, a 28 percent improvement in early-stage gross flow through rate and a 40 percent improvement in late-stage gross flow through rate*.
“With more than 1 million consumer accounts now being managed through Retain, we’re able to see the robust results of the product on improving early-stage delinquencies for our clients,” said Mark Ravanesi, CEO of TrueAccord Corp. “The results of our client’s evaluation were unambiguous: Retain’s machine learning-powered, digital-first approach resonated with consumers and drove significant growth for the early-stage recovery business. With a lingering worker shortage, especially in the call center space, we expect these performance numbers to continue to grow as more consumers are brought into the Retain ecosystem in 2022.”
Powered by TrueAccord’s industry-leading tech stack, key benefits of Retain include a simple, intuitive and effortless-to-use digital platform leading to great user experience, constant A/B testing and optimization to reduce friction and boost conversion rate, infinite scalability, and second-to-none channel deliverability. Retain implements e-commerce-based innovations like the focus on digital experience and outreach, machine learning-based personalization, and deliverability at massive scale for early-stage use.
To learn more about TrueAccord and its digital-first recovery solutions, visit www.TrueAccord.com and follow @TrueAccord on Twitter and LinkedIn.
*This data comes from an anonymous client’s evaluation of performance of different delinquency approaches side-by-side. The client randomly assigned credit and retail card accounts to TrueAccord Retain and the other vendors. Key success metrics included roll rate, or the percentage of dollars that became progressively delinquent, and gross flow through rate, or the percentage of dollars that flowed from one delinquency category across multiple subsequent categories.
TrueAccord is the intelligent, digital-first collection and recovery company that leaders across industries trust to drive breakthrough results while delivering a superior consumer experience. TrueAccord pioneered the industry’s only adaptive intelligence: a patented machine learning engine, powered by engagement data from over 16 million consumer journeys, that dynamically personalizes every facet of the consumer experience – from channel to message to plan type and more – in real-time. Combined with code-based compliance and a self-serve digital experience, TrueAccord delivers liquidation and recovery rates 50-80% higher than industry benchmarks. The TrueAccord product suite includes Retain, an early-stage recovery solution, and Recover, a full-service debt collection platform.
TrueAccord’s Chief Product Officer, Laura Marino, was recently featured in the New Standard in Debt Collection panel as part of the Beyond Digital: The Next Era in Collections summit. As a civil engineer turned product management executive, Laura has a unique viewpoint on the evolution of machine learning in software across a variety of industries. In this blog post, Laura shares her perspective on machine learning at TrueAccord and in collections, in general.
At TrueAccord, we know that consumers prefer digital channels and self-service. We also know that just providing the digital channels is not enough. To truly engage with consumers we need to help them throughout the journey. This is where machine learning comes in.
What is machine learning?
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. In the context of collections, and specifically in the context of our consumer-centric approach to collections, machine learning is a wonderful tool to personalize the experience for each consumer, effectively engage with each of them, and ultimately help resolve their debt.
There has been so much hype around machine learning, but often companies that claim to do ML are really using fixed rules or heuristics (if a consumer does X, then do Y) without including any of the automatic learning and improvement. Or they may be using ML for a very specific, very limited scope – like automating some consumer support responses. The reason that leveraging ML is so difficult for something as complex as collections and recovery is that it requires a lot of expertise in data science and behavioral science, it requires a lot of user research, and it requires a lot of data. This is not something that a company can decide to start doing overnight as an add-on.
How does TrueAccord apply machine learning to debt collection?
TrueAccord is leveraging machine learning and behavioral science throughout the entire journey, from initial engagement all the way to resolution. We were built specifically around the hypothesis that focusing on machine learning-driven, digital-first experiences was the way to transform debt collections. We have been doing this since 2013, and we have orders of magnitude more data than anyone else. Just to give you an idea: we send millions of emails per day, and hundreds of thousands of text messages per week and our ML engine learns from every open, every click, every action on our website, and every interaction with our call center agents. Because of all of this, we have something that is very hard for anyone to imitate.
Unlike traditional collections, we do not use demographic data like age, zip code, or creditworthiness to personalize the experience. Instead, we use engagement data about how the consumer responds at every step in the process.
We have handled debts for over 24 million consumers and we have collected data about each individual interaction with those consumers. That wealth of data, combined with our ongoing user research is behind the ability of Heartbeat (our fully automated and reactive decision engine) to personalize the experience for each consumer. We’ve seen this data-driven machine learning customer-centric approach lead to increased customer satisfaction, better repayment rates, and lower complaint rates.
Machine learning is used to personalize and optimize every step of the customer journey. The first thing we need to do is to effectively engage with the consumer. For that we have several models:
Cadence optimizer: determines the right cadence to communicate with each consumer about their debt. Specifically, it determines which day to send the next communication. We don’t have a fixed rule that says “send an email every x days.” Our decision engine decides it dynamically based on the type of debt, the consumer behavior, and where they are in the process.
Send time optimizer: determines when during that day, communication should go out. A working mother who is busy with her kids in the morning and in the evening is more likely to check her messages in the middle of the day during her lunch break. A construction worker has a very early start to their day, may prefer to check messages at the end of the day. We want our consumers to receive our communications during their preferred times so that they are at the top of their inbox and not buried under 50 other emails. Reaching people at the right time of day has a big impact. Due to our send time optimizer, we saw a 23% increase in liquidation for certain types of debts.
Email content rater: we also want to make sure that the tone of our communication is one that will best resonate with a specific consumer. For each piece of content we send out, our content team has created multiple versions with different voices, ranging from very empathetic to more ‘to the point’ because different people respond to different styles. Heartbeat chooses which one to send based on what it has learned from the behavior of each consumer.
After engaging the consumer with the right cadence, timing, and content we want to make sure that they commit to a payment plan and stick to it until their debt is resolved. For that, we havemachine learning models that determine the best combination of discount and length of payment plans to offer to each consumer. The options that the consumer sees when they get to the payment plan page are tailored to them based on what Heartbeat believes will work best. The consumers can build their own plan but, if we can proactively offer options that work, we make it easier.
We also have a ‘payment plan breakage model’ that helps us identify consumers who are at risk of not making a payment so that we can proactively reach out to them and give them options. With this we were able to increase the resolution rate among customers at risk by 35%.
What do customers think about TrueAccord’s model?
We have a lot of very positive feedback from our consumers which I attribute very much to our machine learning capabilities. It is one of the things that I think is so exciting for everybody who works at TrueAccord. We consistently get messages saying, “Thank you for making it so easy. Thank you for allowing me to do it via digital channels without having to talk to anybody.” And then when people call with questions, our call center knows that they’re there to help. People definitely respond very positively to the approach we’re taking to collections.
This content originally appeared as part of the Beyond Digital: The Next Era in Collections summit. Watch the entire summit here.
In 2013, TrueAccord was founded with the hypothesis that AI driven digital collection was the way to transform the industry. Eight years later, we are still confident in the transformational nature of our hypothesis but are still surprised how few other companies in our industry have fully embraced digital-first debt collection.
The digital revolution has been ongoing for some time now. The word “digital” itself has evolved from a high-tech term that few understood to one that is now regularly accepted as part of our everyday lives – both personally and professionally. As the digital world continues to accelerate the way in which we do everything – from paying for things to driving cars to debt collection – it’s not enough anymore to just invest in digital. Focused strategies and understandings of more complex technologies are mandatory to getting the most out of what the digital economy has to offer.
At TrueAccord, to create powerful moments that actually help consumers, not only pay off debt, but become more financially stable and confident, we need to think bigger by putting them first. In honor of the launch of our newest product, Retain, TrueAccord hosted the Beyond Digital: The Next Era in Collections summit, which is now available in its entirety on-demand. Stay tuned for more on each of the individual sessions.
Here’s the lineup from the Beyond Digital summit:
Ohad Samet, Co-founder & CEO, One True Holding Company
Understanding Consumers in Debt in 2021 (and Beyond)
Mark Ravanesi, CEO, TrueAccord
Jacob Kong, Chief Product Officer, Experian
Jan Hansson, VP, Debt Collection, Klarna
What Debt Collection Leaders Can learn From the Masters of E-Commerce
Naama Bloom, CMO, TrueAccord
Sunil Kaki, EVP, Beachbody & OpenFit
The New Standard of Excellence in Debt Collection: Creating World-Class Consumer Experiences Via Machine Learning
The intelligent, client-branded product for delinquent accounts uses a patented, machine learning-driven decision engine to engage customers and boost recovery rates
Lenexa, KS – Sept. 16, 2021 – TrueAccord Corporation, which offers intelligent digital recovery and communication products and services, today launched Retain, a new, client-branded product that addresses early-stage collection challenges for lenders and other organizations with customers with past-due delinquent accounts. Using the company’s patented, machine learning-based and self-optimizing decision engine, Retain uses engagement data from individual interactions to optimize the consumer experience while increasing recovery for clients. The client-branded product enables clients to improve collections, maintain customer relationships, and offer solutions to their customers that improve financial fitness.
Powered by TrueAccord’s industry-leading tech stack, key benefits of Retain include a simple, intuitive and effortless-to-use digital platform leading to great user experience, constant A/B testing and optimization to reduce friction and boost conversion rate, infinite scalability, and second-to-none channel deliverability. Retain implements ecommerce-based innovations like the focus on digital experience and outreach, machine learning-based personalization, and deliverability at massive scale for early-stage use.
“After seeing success with our late-stage collection solution, Recover, we identified an opportunity to apply the same customer-centric approach to early-stage collections. Our data and machine learning-driven engine proved unmatched for late-stage recoveries. Clients asked us to expand our product suite to address early-stage delinquency while keeping their customers in their brand ecosystem, and we were happy to oblige,” said Mark Ravanesi, CEO of TrueAccord Corp.
Retain prioritizes customer engagement and preference, which is critical to preserve the lender-borrower relationship, with custom communications, timing and channels and a self-serve payment platform that empowers customers to easily manage their accounts. Unlike traditional call-to-collect early-stage collections, which require increased outbound call center volumes, Retain engages users more effectively and efficiently with a digital-first approach and can reduce costs by transforming call centers into productive inbound operations.
“Retain takes all the innovative customer engagement processes we’ve built and adds a brand-focused retention toolkit for our clients to easily plug and play to engage with their delinquent customers,” added Ohad Samet, co-founder and CEO of One True Holding Company, TrueAccord’s parent company. “Retain adds to our product and service offerings designed to improve the experience for consumers in debt and actually help them find a path toward a better financial future.”
For more product information or to request a demo, please visit the product page at www.trueaccord.com.
TrueAccord is the intelligent, digital-first collection and recovery company that leaders across industries trust to drive breakthrough results while delivering a superior consumer experience. TrueAccord pioneered the industry’s only adaptive intelligence: a patented machine learning engine, powered by engagement data from over 16 million consumer journeys, that dynamically personalizes every facet of the consumer experience – from channel to message to plan type and more – in real-time. Combined with code-based compliance and a self-serve digital experience, TrueAccord delivers liquidation and recovery rates 50-80% higher than industry benchmarks. The TrueAccord product suite includes Retain, an early-stage collection solution, and Recover, a full-service post-charge off recovery platform.
About One True Holding Company
One True Holding Company is a technology company providing business- and consumer-facing solutions in the consumer debt space. Subsidiaries include TrueAccord, which offers machine learning-based, digital- and mobile-first servicing for debt in collections and recoveries, and True Life Solutions, which offers a SaaS platform that consumers can use to contact collectors and creditors digitally.
In 2019, Todd Johnsen, Snap Finance’s Senior Manager of Collections Vendors, was charged with doing something that had never been done at Snap before: developing a third-party collections program. According to Johnsen, “At that time, the only recovery program for charge-off accounts was a call-and-collect settlement at tax season. I knew we could do better, but we’d have to start from scratch.” Johnsen also found a large amount of backlog accounts that had never been worked by a collections agency, as well as the need for a forward flow third-party recovery program.
Johnsen and team surveyed their options: they looked at both traditional agencies (predominantly making outbound calls) and digital-first collections solution providers, like TrueAccord. Johnsen was particularly interested in how digital-first providers like TrueAccord used machine learning to optimize their relationships with consumers via digital channels like email, SMS, and push notifications.
“My thought process was — we work with subprime consumers who may have bad associations with debt collection,” said Johnsen. “This audience may have already had experiences with incessant collection phone calls, and they are used to avoiding them. I wanted to find an agency that was doing things differently. I knew that TrueAccord was using technology and digital channels in a way that other providers weren’t.”
While Johnsen was curious about working with a digital-first agency like TrueAccord, he wasn’t ready to go all-in immediately. The Snap Finance team decided to engage both a traditional agency and TrueAccord and compare the results. In evaluating the competing partners, key considerations included liquidation rate performance, security and compliance, and optimization efficiency. The result? TrueAccord delivered better results across all measured parameters.
“The reality of the results really knocked me out,” said Johnsen. “What we saw was almost 25-35% better performance on the accounts that we placed with TrueAccord, compared to the accounts we placed with traditional agencies. It was a real eye-opener. In fact, TrueAccord is number one in every tier I have them in. We’ve seen nothing but huge benefits as a result of that individual, digital-first interaction that TrueAccord tailors to each consumer.”
To learn more about TrueAccord’s work with Snap Finance, read the full case study.
Generally, when talking about artificial intelligence (AI) in regards to medical collections, we hear about how it has automated the once-painstaking process of medical coding for billing. But why stop there? With all of its capabilities, AI has much more impressive and patient-facing applications when used to improve customer experience, especially in the healthcare industry which is increasingly digital-first and self-serve. In this post, we’ll explore how AI and machine learning can supercharge the healthcare revenue cycle by catering to consumer preferences, turning billing and collections into a seamless, efficient experience for both patients and providers.
But first: why is it necessary—and even urgent—to improve healthcare revenue management? The answer is patient expectations. Patients now expect the same type of personalized, easy-to-use experience they’ve grown accustomed to receiving from other industries, including banking, airline and retail industries. Patients are now “digital-first” and look for an end-to-end experience that allows them to handle medical-related issues on their own, often from their mobile devices. Patients can already schedule appointments, request prescription refills, receive test results, and even contact their healthcare provider directly through digital platforms. The application of digitization through AI and machine learning to other touchpoints in the patient journey, all the way through billing and collections, can improve customer experience and thereby their overall interactions and relationships with their healthcare providers.
First, digitization powered by AI and machine learning can replace manual and paper processes to speed up the recovery timeline. A 2020 report by InstaMed, a J.P. Morgan company, found that patient collections take more than a month for 63% of healthcare providers. This figure isn’t surprising when 81% of providers still leverage paper and manual processes for collections, while 75% of consumers want to receive eStatements for medical bills. The traditional method of collections does not align with consumer preferences, with more than half (54%) of consumers surveyed saying they prefer electronic communications (emails, text messages, in-app messages and live chats) for medical bills. And a majority of consumers (65%) preferred paying those medical bills digitally as well – whether online through their doctor’s or health plan’s website, their bank’s bill-pay portal or mobile apps – instead of manually. Using AI and machine learning to match the consumer’s communication and payment preferences can drastically improve the time needed to engage and collect from patients.
Second, AI-powered systems can personalize the billing and collections process and offer intuitive payment solutions for patients to achieve the best possible recovery rates. According to the InstaMed report, collecting patient financial responsibility in a timely manner was especially challenging for large patient balances, with 49% of surveyed providers reporting that they cannot collect bills of more than $400 in 30 days. Especially with multiple billers on different payment cycles, it can be difficult for a patient to set up a payment plan with terms they can successfully meet. AI can improve this experience by identifying the most efficient time, place and manner to communicate with a patient about their financial responsibility and go a step further in presenting personalized, affordable payment options.
Third, AI can be used to interface directly with clients where they are and minimize the need for waiting on hold for the next available representative, creating a more seamless, humane process and a better customer experience. AI-enabled chatbots can answer basic questions, while automation can help provide information on why claims were denied and other status updates. Empathetic customer service is important in the healthcare industry and customized customer self-service can reduce frustration for the patient and the number of service agents needed for the provider.
At TrueAccord, we use AI and machine learning to build digital debt collection solutions for billers that put customers first. By implementing behavioral analytics to predict consumer communication preferences and machine learning to create smart, intuitive processes that increase likelihood of patient repayment, TrueAccord products stay a step ahead to ensure a successful revenue cycle where both patients and providers win. To safeguard personal patient information, TrueAccord’s policies and procedures are designed to comply with all HIPAA-related requirements (Health Insurance Portability and Accountability Act), including documenting the use of protected health information (PHI) and the physical, technical, and administrative safeguards implemented to protect PHI. Learn more about how we use AI and machine learning to provide a personalized collections experience at scale here.
TrueAccord is a machine-learning and Al-driven 3rd-party debt collection company that is reinventing debt collection. We make debt collection empathetic and customer-focused and deliver a great user experience.
Our digital-first approach to debt collection creates a cycle of collections growth:
1. Improve the perception of the industry
2. Provide a personalized experience
3. Build brand equity and collect