TrueAccord Digitally Serves 20 Million Consumers on Path to Financial Health

By on July 12th, 2022 in Company News, Customer Experience, Machine Learning, Product and Technology

With more than 20 million consumer accounts serviced through intelligent, digital-first collections products, results show better repayment and happier customers than “call to collect” agencies

LENEXA, Kan., July 12, 2022 — TrueAccord Corp, a debt collection company using machine learning-powered digital recovery solutions, today announced that it has served more than 20 million customers in debt with a digital-first experience. TrueAccord’s customer-centric approach and commitment to creating a positive consumer experience is reflected in its 4.7 Google customer satisfaction rating, customer feedback, and an A+ rating with the Better Business Bureau.

TrueAccord’s collection solutions harness machine learning and digital-first communications to deliver a personalized, consumer-friendly experience for those in debt. As is the nature of machine learning, the system dynamically analyzes and refines the approach used for each customer based on their interactions combined with years of previous engagement data in order to deliver the most effective communication treatment. The patented system, HeartBeat, which is now 20 million customer engagement interactions strong since its 2013 inception, continues to optimize with each new customer interaction.

“Machine learning is only as good as its data sources, and with more than 20 million accounts’ worth of engagement data that informs the HeartBeat system, we’re confident that the experiences being delivered are as streamlined and as aligned to consumer preferences as possible,” said Mark Ravanesi, CEO of TrueAccord Corp. “As a mission-driven company, we prioritize creating better experiences for consumers in debt, and based on our high customer satisfaction and repayment rates, it looks like we’re making significant progress.”

Powered by TrueAccord’s industry-leading tech stack, the product suite includes Retain, a client-branded early-stage consumer engagement platform for managing pre-charge off debt, and Recover, a full-service debt collection solution. Key benefits of both products 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. 

While holding customer experience as a priority, TrueAccord products continue to prove more effective than competitors, as evidenced by client case studies showing 25-35% better performance on accounts using Recover when compared to those placed with traditional agencies, and recovering $17 million in delinquent bills with a 44% paid in full rate using Retain.

To learn more about TrueAccord and its digital-first recovery solutions, visit www.TrueAccord.com and follow on Twitter and LinkedIn.

About TrueAccord

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 20 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.

Elevate Your Collection Strategy with Machine Learning and HeartBeat

By on July 7th, 2022 in Industry Insights, Machine Learning, Product and Technology

Debt recovery and collection look quite different in 2022 than it did ten, five, even just a year ago: new channels to reach consumers, larger data sets to analyze, complex regulations that can vary state by state, and so much more.

So when it comes to deciding the best way to engage consumers and effectively recover debt, has your strategy evolved to keep up? Machine learning, artificial intelligence, data science—these terms are thrown around a lot, and for good reason.

But how does it tactically improve the experience for both lenders and members?

Decoding Machine Learning for Debt Collection and Recovery

To help decipher real differences between a machine learning strategy versus the traditional call-and-collect, we have designed a highly visual guide to cut through the jargon and help you understand the basics of machine learning in collections. Decoding Machine Learning for Debt Recovery and Collection provides straightforward definitions, clear diagrams, and bottom line benefits make this eBook your at-a-glance guide to machine learning in debt collection.

Download your complimentary copy of the new eBook Decoding Machine Learning for Debt Recovery and Collection here»

From delivering a better experience in-line with what consumers expect from businesses to streamline communications, machine learning has gone from a “nice to have” to a “must have” for collection efforts.

Upgrade Debt Recovery & Collection With HeartBeat

Although this type of technology is a step in the right direction, it’s only one step forward—your debt collection strategy can go even further with TrueAccord’s patented decision engine, HeartBeat.

Integrating machine learning into your practice is certainly important—but how does this technology know what the best choice is to engage all of your delinquent accounts now and in the future?

Say hello to HeartBeat, our intelligent decision engine, and say goodbye to missed debt recovery opportunities left on the table by basic machine learning models. See exactly how HeartBeat upgrades your collection strategy in our new eBook, Upgrade Debt Recovery & Collection With HeartBeat—the more in-depth companion piece to our visual guide to machine learning (detailed above).

While HeartBeat utilizes machine learning in its decision-making process, it is not limited to it. This decision engine is continuously evaluated for performance, and adjusted to align with the current economic situation, changes in consumer behavior, and updates to compliance rules.

If you are switching from a more traditional outbound approach then a basic machine learning model can provide a short-term lift in recovery rates, but will hit a dead end when it comes to optimizing, adapting, and improving over time. HeartBeat is set up for the long game and recovers more because of it.

Download your complimentary copy of the new eBook Upgrade Debt Recovery & Collection With HeartBeat to learn how to start recovering more»

Elevate Engagement, Recover More

Together, these two companion eBooks, Decoding Machine Learning for Debt Recovery & Collection and Upgrade Debt Recovery & Collection With HeartBeat, serve to be the ideal introduction into machine learning in debt collection and then a deeper look beyond the basics to see what even more advanced technology can do for your recovery operation.

Discover how an intelligent, digital-first collection strategy drives overall improved performance, better member experience, and the more effective recovery of delinquent funds—without implementing more manual processes or adding headcount to your team.

Schedule a consultation today with one of our experts today to learn more about how you can elevate your debt collection practice today.

Bridging the Gap Between Machine Learning and Human Behavior with HeartBeat

By on June 27th, 2022 in Industry Insights, Machine Learning, Product and Technology

When it comes to engaging consumers in debt collection, behavioral science helps us to understand and respond to an individual’s situation, motivations, and contact preferences.

For example, we know that consumers don’t like being called by debt collectors. With that knowledge, behavioral science then helps us determine the optimal way to meet consumers where they are, contact them when they want by using their preferred channels, and lastly sending a message that resonates with them enough to engage.

It starts with a lot of engagement data. At TrueAccord, we’ve been collecting data about how consumers engage with us for nearly 10 years to determine the optimal ways to engage with consumers, and then use that data to generate a special collections experience just for them – from best time, best channel, and best language to engage that individual consumer.

Take content for example, each person is unique and different people respond differently to different communications. They are driven to action by different words and are convinced for different reasons. In writing content, it’s our goal to write content that responds to these individual needs.

There are 2 things we consider when writing content for collection communications: content type and content dimensions.

  • Content type is what we send based on a user’s actions. As an example, following up on a page view—if a consumer is viewing a payment plan, disputing a debt, or thinking about unsubscribing but drops off the page, we will try sending a follow up email or SMS with other plan options, information about the account, a description of how to unsubscribe or dispute for them to view. We will continue to try different content types until we find the right one that engages the consumer.
  • Content dimensions are more established behavioral science frameworks used to ensure that our communications vary in style and tone so that we are speaking to consumers in a unique way that will motivate them to engage. Everyone responds to different motivations so we use a variety of different frameworks until we find the one that connects with the individual

Each piece of content is tagged depending on the content type and dimensions so it can be easily used by HeartBeat, our powerful and intelligent patented-machine learning engine. Good content will lend itself well to automated, data-driven prioritization done by HeartBeat to present customers with the best possible content item at each given time.

How can technology personalize the debt collection experience?

By using technology and behavioral science to determine the best way to communicate with consumers, we are able to personalize each user’s unique experience. Our patented machine learning engine mentioned above, HeartBeat, allows us to do just that. HeartBeat collects engagement data and then, after analyzing multiple solutions, suggests the best possible treatment depending on the individual and their engagement. HeartBeat also uses a real-time feedback loop so the technology can adapt to a consumer’s engagement right as it happens.

Instead of relying on data like age and location, HeartBeat uses engagement data to personalize the communication process. The engagement data is collected every time a consumer engages in a certain way, whether it’s clicking on an email or SMS, visiting a webpage, and/or viewing payment plan options. Our system learns what motivates the consumer and responds with content or payment options that will resonate with them.

For example, if the consumer clicks on an email that uses likable content mentioning “short term cash flow,” our system may determine to send a friendly follow up email letting them know that they can set up a payment plan that starts on a later date when they’ll have the ability to make a payment. We know what motivates an individual may change from day to day depending on their circumstances, so we treat them based on their active engagement and behavior with our system rather than construct a specific profile for each consumer and treat them based on that basic account profile.

By combining behavioral science and machine learning, the best-possible payment options are offered to customers based on their debt situation, previous communication, and engagement data. Whether their actions show that they would benefit from a long-term payment plan, or if it shows that they’d prefer to pay in full, HeartBeat will suggest the best option for that customer. The power of using behavioral science and machine learning is anticipating the needs and preferences of our customers and using that to help them as seamlessly as possible.

Overall, there is no one way of communicating that will work for everyone across all situations, and tailoring communication and collection strategy to align with consumer preferences is better for both the consumer, lenders, and our business. That’s why building the bridge between machine learning and human behavior is essential.

Discover how HeartBeat can help humanize your collection process in our new in-depth eBook, Upgrade Debt Recovery & Collection With HeartBeat, available for download here»

Learn more about behavioral science from TrueAccord’s User Experience Director, Shannon Brown in a new interview in Collector Magazine here»

Technology in Collection: 21 Must-Know Buzzwords

By on May 24th, 2022 in Industry Insights, Machine Learning, Product and Technology

Your Guide to Key Terms for Today’s Debt Recovery Strategy

Reaching consumers today requires a more sophisticated process than simply dialing the phone or sending a generic email, especially when it comes to debt recovery and collection. But reviewing potential strategies can often leave you lost in a sea full of acronyms and buzzwords. Between terms like AI, machine learning, and data science, it can be difficult to keep up with the different definitions—and understand how they impact your business and bottom line.

To help keep this word salad straight, we’ve compiled a glossary of helpful terms, definitions, and examples to help differentiate them:

  • Accounts per employee (APE), account to collector ratio (ACR): The number of delinquent accounts that can be serviced by an individual recovery agent – often used to measure cost effectiveness.
  • Artificial Intelligence (AI): AI is a blanket term describing a range of computer science capabilities designed to perform tasks typically associated with human beings. Machine learning (ML) is a subset of AI. Through AI, processes like debt collection can become more efficient by developing better outreach and deployment strategies.
  • Big Data: This term means larger, more complex data sets . Big data can save collectors a lot of time by using many variables for analytics-based customer segmentation, insert, insert..
  • Coverage: The percentage of users for whom organizations have digital contact information, such as email addresses or phone numbers.
  • Customer Retention Rate: Measures the total number of customers that a company keeps over time. It’s usually a percentage of a company’s current customers and their loyalty over that time frame.
  • Data Science: ‍A cross-discipline combination of computer science, statistics, modeling, and AI that focuses on utilizing as much as it can from data-rich environments. Data science (which includes machine learning and AI) requires massive amounts of data from various sources (customer features such as debt information or engagement activity) in order to build the models to make intelligent business decisions.
  • Deep Learning (DL): A subset of machine learning. Deep learning controls many AI applications and services and improves automation, performing analytical tasks with human intervention.
  • Delinquency rate: The total dollars that are in delinquency (starting as soon as a borrower misses as a payment on a loan) as a percentage of total outstanding loans.
  • Deliverability: The percentage of digital messages that are actually reaching consumers (e.g., as opposed to ending up in email spam filters).
  • Digital engagement metrics: A range of KPIs that capture how effectively digital channels are reaching and engaging consumers.
  • Digital opt-in: The percentage of users who have indicated their preference to receive digital communications in a particular channel.
  • Efficiency: Measures a company’s ability to use its resources efficiently. These metrics or ratios are at times viewed as measures of management effectiveness.
  • Machine Learning (ML): Technology that uses algorithmic modeling techniques to observe patterns and trends, reassessing the best approach to achieve a goal, and adapting behavior accordingly. It continuously, automatically learns and improves at a massive scale as more data is observed. With the help of machine learning, companies can make sense of all their data and take on new approaches to debt collection processes from better customer experience to more efficient delinquent fund recovery.
  • Net loss rate: The total percentage of loan dollars that get charged off (written off as a loss).
  • Open rate, clickthrough rate: The percentage of users who are actually opening and clicking digital communications.
  • Predictive Analytics: Predicting outcomes is one specific application of machine learning. It allows companies to predict which accounts are more likely to pay sooner and allows them to better plan operations accordingly.
  • Promise to pay kept rate: The percentage of delinquent accounts that maintain a stated commitment to pay.
  • Promise to pay rate: The percentage of delinquent accounts that make a verbal or digital commitment to pay.
  • Right party contact rate: The rate at which a collections team is able to establish contact with the consumer associated with a delinquent account.
  • Roll rate: The percentage of delinquent dollars that “roll” from one delinquency bucket (e.g., 60 days past due) to the next (e.g., 90 days past due) over a given timeframe.
  • SMS: An acronym that stands for “Short Message Service” referring to text messages on cellular devices.

For more information on how to get started integrating innovative technologies into your debt recovery strategy, schedule a consultation today.

TrueAccord Announces Results Confirming Effectiveness of Digital-First Retain Product for Early-Stage Delinquencies

By on January 25th, 2022 in Company News, Machine Learning, Product and Technology

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.

About TrueAccord

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.

How TrueAccord Embraces Machine Learning to Create Positive Consumer Experiences in Debt Collections

By on December 23rd, 2021 in Industry Insights, Machine Learning, Product and Technology
TrueAccord Blog

By Laura Marino

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 have machine 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

Building a Digital-First, Third-Party Collections Solution with Snap Finance

By on August 13th, 2021 in Industry Insights, Machine Learning, Product and Technology

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.

Beyond Coding: Using AI to Improve the Healthcare Revenue Cycle

By on July 8th, 2021 in Industry Insights, Machine Learning
TrueAccord Blog

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.

Pitfalls on the Path to Digital Debt Collection

By on August 25th, 2020 in Industry Insights, Machine Learning, Product and Technology, User Experience

Banks are accelerating their adoption of new digital debt collection tools in anticipation of a “tidal wave of consumer debt issues” when government stimulus programs end and financial institutions stop offering forbearance and loan deferral options.

That’s the premise of a new article in American Banker highlighting a variety of technology-powered strategies banks are using to make debt resolution more automated, conversational, and empathetic. These approaches range from the convenient (more flexible self-service payment options) to the high-tech (robotic process automation). 

The American Banker article highlights promising signs of progress, particularly for industry players that have not always been known for digital adoption. KeyBank, for example, is in the process of rolling out a self-service digital payment portal designed to offer banking customers privacy and flexibility in resolving payments. And Alabama-based Regions is implementing digital messaging and intelligent interactive voice response (IVR).

At the same time, the article shines a light on the massive challenges facing any financial institution looking to implement intelligent digital debt collection at scale. Here are three common hurdles on the path to digital debt collection maturity – and why they matter:

Challenge #1: “One-size-fits-all” approaches

The challenge: In its overview of Regions, the article makes reference to a single conciliatory messaging tone used in all outreach to delinquent customers. 

Why it matters: Consumers differ vastly in their preferences and responsiveness to digital touchpoints. For example, one consumer might respond to a friendly message delivered by SMS, while another might respond best to a straightforward message delivered by email. As a result, a one-size fits all approach falls short of realizing the potential – in both performance uplift and customer experience – of true one-to-one personalization.  

The TrueAccord approach: HeartBeat, TrueAccord’s patented machine learning platform, mines through tens of millions of data points to optimize digital outreach on the individual level within a programmed set of compliance rules  – and continues learning the more data it analyzes.  

Challenge #2: Narrow, channel-specific use of machine learning 

The challenge: Another challenge that banks face in scaling their use of intelligence – including artificial intelligence (AI) – is the limited deployment of algorithms and optimization within a single kind of channel, such as in a call center environment. The article profiles a collections and business process outsourcing company, for example, that developed an AI-based virtual assistant that can handle most inbound phone calls.

Why it matters: Machine learning and artificial intelligence (AI) are powerful tools for restoring intimacy and relevance to customer relationships at scale. At their most useful, these tools should be deployed to personalize the customer’s full experience with a bank – not just the limited interaction on one channel. 

TrueAccord’s Approach: HeartBeat captures a continuous data feedback loop and optimizes for each customer touchpoint across a variety of digital channels, ensuring that each customer is being reached on the channel that is most relevant for her.  

Challenge #3: Building a truly comprehensive and flexible self-serve portal

The challenge: Constructing a digital portal that drives consumer adoption and usage takes major work. To truly match the convenience of online banking, digital tools must also allow consumers to adjust the length and installment amount on a payment plan, defer a payment, dispute all or a portion of their debt, apply for a hardship pause on their debt, and much more.

Why it matters: Research suggests that customers want to be able to self-serve. But doing so requires the full, flexible range of interaction options that would be available to them through traditional analog channels.

TrueAccord’s Approach: Through a robust and flexible digital platform, TrueAccord offers  a best-in-class self-serve experience: over 95% of users resolve their accounts without ever directly communicating with an agent. 

Ultimately, digital debt collection technologies offer banks the ability to build lasting relationships with their customers. As Kimberly Snipes, consumer chief information officer at KeyBank puts it in the American Banker article: “We want our customers to say, I hate that I had that situation, but I felt like my bank was working with me, not against me.”

Being aware of the challenges on the path to digital debt collection – and having a plan in place to address them proactively – can help financial institutions ensure that they’re set up for long-term success. 

About TrueAccord

TrueAccord is reinventing the relationship between creditors and lenders with a machine learning-driven, digital approach to debt collection. Our technology personalizes outreach to each customer across digital channels, continuously optimizing for performance while delivering a customer experience that builds long-term brand loyalty. Schedule a demo today to learn more. 

How is machine learning driven by experimentation?

By on March 6th, 2020 in Machine Learning, Product and Technology
microscopes in a laboratory

Building scalable technology requires constant evaluation and improvement. Experimenting is defined by trying new things and creating effective changes that help teams to make informed decisions around product development. Trying new things creates momentum, and organizations that are driven by experimentation turn that momentum into growth.

Machine learning and artificial intelligence support large-scale, concurrent experimentation that helps these technologies to improve upon themselves. With the right tools in place, you can test a variety of scenarios simultaneously.

For example, we use our systems to track changes in the collection process and better understand how our digital collections efforts can be improved. Since digital-first channels offer thorough tracking and analysis, including real-time tracking on our website, we can learn in short cycles and continuously improve our product. 

This kind of frequent experimentation helps to avoid making product development decisions based on untested hunches. Instead, you can test your instincts, measure them carefully, and invest energy where it matters.

Machine learning drives the experimentation engine

Aggregating historical data and processing it using machine learning algorithms and artificial intelligence helps you to understand their effectiveness. Regardless of how intelligent your learning algorithms may be, waiting to test and expand your knowledge base before marching blindly ahead can make or break the success of your product.

To launch an experiment, we follow these steps: 

  1. Start with a hypothesis that you want to test
  2. Assign a dedicated team to manage the experiment
  3. Monitor the performance of the test as it is guided by machine learning
  4. Iterate

B2B companies can benefit from partnering directly with clients to customize experiments for their unique product lines in order to make experimentation-based optimization an ongoing process for both new and existing business. Keep in mind that the goal of product optimization is not always jumping to the finish line. 

Understanding how your product works ultimately offers you and your customers more value, but it’s easy to become distracted by positive outcomes. Effective, scalable products require intentional design; if you’ve accomplished a goal, but the path there was accidental, taking a few steps back to review that progress and test it can help you to get a clearer picture and grow the way you want. 

Below are two sample experiments we conducted to optimize our machine learning algorithms. 

Experiment #1: Aligning Payments to Income

Issue

The number one reason payment plans fail is consumers don’t have enough money on their card or in their bank account. 

Hypothesis

If you align debt payments with paydays, consumers are more likely to have funds available, and payment plan breakage is reduced. 

Experiment

We tested three scenarios: a control, one where we defaulted to payments on Fridays, and one where consumers used a date-picker to align with their payments with their payday. After testing and analysis, we determined that the date-picker approach was the most effective as measured by decreased payment plan breakage without negatively impacting conversion rates.

By understanding which payment plan system was the most effective, we were able to provide our AI content that offered these plans as options to more consumers and integrate the knowledge back into our systems and track those improvements at a larger scale!

Experiment #2: Longer payment plans can re-engage consumers

Issue

Customers dropped off their payment plans and stopped replying to our communications.

Hypothesis

Customers can be enticed to sign up for a new plan if offered longer payment plan terms. 

Experiment

We identified a select group of non-responsive consumers that had broken from their payment plans and sent them additional text messages and emails. These additional messages offered longer payment plan terms than the plans they broke off from.

Ultimately, we found that offering longer payment plans, even with reference to the consumer’s specific life situations didn’t lead to an increase in sign-ups. The offers that we sent had high open and click rates but did not convert. This indicated that we were on the right track but needed to iterate and come up with another hypothesis to test.

This experiment was especially important because it illustrates that not every hypothesis is proven to be correct, and that’s okay! Experimentation processes take time, and the more information you can gather, the better your results will be in the future.

We’re able to simultaneously update our product and continue experimenting, thanks to algorithms called contextual or multi-armed bandits. Here’s what you need to know about these algorithms and how they help!

Building the newest, most innovative products feels exciting, but building without carefully determined direction can be reckless and dangerous. By regularly evaluating the effectiveness of machine learning algorithms, you can make conscious updates that lead to scalable change, and experimentation paves the way for consistent product improvement.