Category: Product and Technology

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.

Sila Offers Customers Digital Debt Collection Services through New TrueAccord Partnership

By on February 7th, 2022 in Company News, Product and Technology
TrueAccord Blog

PORTLAND, Ore., (February 7, 2022) – Sila Inc., a fintech software platform that provides payment infrastructure as a service, today announced that it partnered with TrueAccord, the leading debt collection company offering intelligent, digital-first collection and recovery solutions, to make it easier for Sila’s customers to use TrueAccord’s products and services. How to deal with delinquent and defaulted accounts is a key element that fintechs need to have in place as part of their overall management of funds. Using a patented machine learning engine and engagement data from millions of customers, TrueAccord delivers a personalized, self-serve experience that drives consumer engagement and industry-leading results. Meeting consumer preference for digital-first services and to cut through the noise and empower customer self-service and inbound communication, TrueAccord uses a range of channels including email, SMS, voicemail drop, and more.

Since its inception, Sila has been laser-focused on providing industry-leading API solutions. As importantly, Sila has been steadily growing its partner network to augment its offering by anticipating additional functionality that Sila customers will need to successfully build their businesses. With the recent addition of TrueAccord, Sila is on path to have agreements with over 40 specialist service providers signed by the end of this quarter.

“Sila is proud to welcome TrueAccord as a partner. We know that our customers will benefit from this key addition to our partner network and from a closer relationship between our two organizations,” said Shamir Karkal, CEO and co-founder, Sila Inc. “Like many of our fintech customers, TrueAccord was founded by an individual who had a sub-optimal experience with a traditional financial institution and decided to do something about it. That’s a mindset that is very close to our own because we started Sila around the idea to provide payment services that allow entrepreneurs to build the new financial world they have in mind.”

“We have worked with more than 16 million consumers on their journey to pay off their debts, and we use that data and feedback to understand how and when to best engage consumers to facilitate repayment. By allowing consumers to create flexible payment plans and by offering modern, digital-first communication channels, we are changing the landscape of debt collection from hostile and harassing to empathetic and helpful,” said Mark Ravanesi, CEO of TrueAccord Corp. “We are looking forward to bringing to bear our significant expertise for the benefit of Sila’s customers and consumers.”

About Sila

Sila is a fintech software platform that provides payment infrastructure as a service, a business-critical element for all companies that need to integrate with the US banking system and blockchain quickly, securely, and in compliance with applicable US regulation. Sila offers Banking, Digital Wallet & ACH Payments APIs for Software Teams. The firm was recognized as a ‘2021 best place to work in financial technology’. Sila is headquartered in Portland, Oregon. For more information go to www.silamoney.com

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.To learn more, go to http://www.trueaccord.com.

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.

TrueAccord Offers Buy Now, Pay Later Clients the Opportunity to Improve Repayment Success

By on July 6th, 2021 in Industry Insights, Product and Technology
TrueAccord Blog

If you aren’t familiar with Buy Now, Pay Later (BNPL) yet, it’s a safe bet that you will be soon. The service, which allows consumers to split a purchase into several payments over a set period of time, has been popular in other countries and has been gaining traction in the U.S. 

To quantify the growth in BNPL use, a February 2021 survey conducted by The Strawhecker Group (TSG) of more than 1,500 U.S. consumers found that nearly two in five (39%) had used a BNPL service and predicted that BNPL volume will double by 2025. A separate March 2021 survey by The Ascent found that 56% of U.S. consumers have used a BNPL service, a nearly 50% increase from July 2020. 

This type of payment plan, offered by BNPL companies, has clearly caught on with consumers, leading to rapid growth (in some cases 200% or more year-over-year in 2020) for the main players.

So why do consumers love this offering? The question should be, why wouldn’t they? 

  1. The service performs like a short-term credit card, with generally no interest or fees due, unless the consumer misses or is late on a payment. 
  2. It’s simple and convenient to use when shopping online, with payment offers prominently displayed at checkout. 

It’s easy to see why consumers would opt for a more flexible, pay-over-time purchase option. After trying BNPL, users tend to like it and become repeat customers – TSG’s research found that nine out of ten people who have used BNPL found it reliable and that 85% of consumers plan to continue using it. TSG’s research also confirmed that the option to buy now and pay later tends to make people spend more than they would otherwise, potentially outside of their budget. BNPL will continue to be an attractive payment option for consumers, especially if it’s eventually integrated into in-person retail transactions, and the need for consumer education will grow. 

As a digital debt collection company, TrueAccord helps clients collect on unpaid debts, but is equally committed to helping consumers achieve long-term financial fitness and stability. TrueAccord works with many BNPL customers who for one reason or another did not meet the terms of the payment plan and ended up in collections by helping them understand their debt, offering flexible repayment options and educating on smart borrowing and spending. TrueAccord aims to usher BNPL consumers through and out of debt while delivering the best possible experience, and it’s that collaboration that will lead to better business for BNPL providers and better financial outcomes for consumers.

If you’re interested in learning about BNPL service providers and our work, check out our recent webinars co-hosted with Klarna and Affirm.

Klarna’s Digital Debt Collection Journey: Outsourcing Without Sacrificing the Consumer Experience

By on June 15th, 2021 in Product and Technology

Klarna, the highest-valued private fintech in Europe, is on a mission to make shopping simple, safe and smooth, for both consumers and retailers, through its suite of payment products and services. From its inception in 2005, Klarna has not compromised on providing a seamless consumer experience — even when it comes to consumers in collection. 

With a high standard for customer experience and in an effort to integrate collections seamlessly with their product, Klarna initially opted to keep collections in-house. For five years the company had great results with in-house collections, but as Klarna expanded to new markets and added new products, scaling in-house collections while maintaining a best-in-class customer experience strained the company’s resources and became less feasible. 

This led Klarna to begin considering a third-party collection partner. By this time, the collections industry had evolved. New players like TrueAccord were building digital-first collection solutions that vastly improved the customer experience via personalized outreach, flexible payment plans, and a self-optimizing, machine learning-driven performance engine.

It’s easy to underestimate the expertise involved in building an effective, compliant digital debt collection engine, and partnering with the right collection solutions provider would free up valuable internal resources. Klarna’s priority was to focus on their core business and engage an expert partner who would be able to build a world-class collection operation for them — one that would only enhance their consumer experience while not sacrificing brand image. 

“We look at collections partners the same way we look at hiring team members: we only want to work with the absolute best. We wanted to partner with a company that truly takes care of consumers,” said Jan Hansson, VP Debt Collection, Klarna. 

Other key considerations to moving away from in-house collection included, data science expertise, engineering talent, compliance resourcing and industry knowledge. After doing their due diligence, Klarna decided to partner with TrueAccord as a collection solution provider. TrueAccord stood out from competitors in two important ways: customer centricity and digital and multichannel capabilities.

By partnering with TrueAccord, Klarna was able to increase liquidation rates and achieve better holistic results, with retention rate a key indicator. Moving to a partnership with TrueAccord from in-house collection also allowed Klarna to free up valuable internal resources and refocus on their key business functions. Klarna is now expanding their engagement with TrueAccord to include more accounts and looks forward to growing the partnership even more in the future. 

“We are so proud to work with TrueAccord,” said Sebastian Siemiatkowski, co-founder and CEO, Klarna. “Putting technology to use for the people instead of against the people is the next generation of tech.

To learn more about TrueAccord’s work with Klarna, read the full case study or check out our recent webinar, “Digital Debt Collections 101 with Klarna”.

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 to offer better products in collections: An interview with Parker Lyons

By on June 17th, 2020 in Industry Interviews, Product and Technology
people drawing a plan on a whiteboard

New products in debt collection seek to solve decades-old problems with traditional collection strategies. Products and services seek to improve the collections process and aim to improve contact rates, liquidation rates, brand perception, and ease of access.

When you combine agile product development with its application in collections, you’ll see new solutions to old problems. I recently spoke with Parker Lyons, TrueAccord’s Product Director, about his personal product philosophy, how he and his team are approaching product development, and the challenges faced by new product offerings in the space.

Profile photo of Parker Lyons
Parker Lyons, Product Director

Hi Parker! Thanks for joining me. I wanted to kick us off with an introduction. You’re fairly new to the collections industry. Can you walk me through what brought you to product development in debt collection?

Sure! When I finished college I started out in advertising for consumer packaged goods. I was living in Colorado at the time, so I was working on some ads for Coors Light and Polaris snowmobiles. I spent a few more years in advertising, but I ended up taking an interest in energy and renewables. I saw companies in the space with really impactful missions and the growth potential, so I went to school to get my Master’s [Degree] in Environmental Studies. 

I ended up making my way out to California and started in solar. I met some people at Spruce Finance who are now working at TrueAccord, and had the chance to see the work TrueAccord is doing. I always had a very “Tony Soprano” view of debt collectors, but TrueAccord is something different. It’s a different type of mission, and we’re really helping people get back on their feet. 

We’re happy to have you! How do you translate your product experience over from such a different industry?

Most recently I was with a company called BlueWave Solar that was a community solar business. Our product allowed consumers to subscribe to a percentage of a solar farm and apply the savings generated from that farm to their utility bill. 

So we really were a servicing company. We had clients who were big banks and energy companies whose assets might differ from debt collectors managing portfolios, but the goal was the same: they wanted to keep accounts and cash flow moving.

And as you’ve started to consider meeting that goal for clients in debt collection, what are some things you’ve learned about the industry? Do you see consistent issues that you think need to be addressed?

Traditional debt collection platforms are using reliable systems. Call-and-collect methods have worked for a long time, but performance is waning and people aren’t picking up the phone anymore. A lot of the appeal though is that it’s a relatively simple model to get moving. You hire agents, you train them, and they start calling. 

On the other side of that, we see some resistance to new technology, and I think that people are worried about it being too complex. So that falls on us. We have to meet clients where they are and focus on making integration easy. We have to maintain simplicity even though machine learning and digital tools can be very complex. 

How do you go about making your product more easily digestible then? Where do you start when you’re trying to solve that problem? 

It starts with knowing your user. Who are they? What’s their problem? You have to have a deep understanding of what makes them tick and their pain points because you then have to ask yourself “How do we solve that problem for them in a way that no one else can, or cheaper than someone else can?” In product management, we say that you’re responsible for creating a product that is valuable, usable, feasible, and viable. With those things in mind, you can turn your potential client’s issues into your value proposition and the capabilities of your company.

In product management, we say that you’re responsible for creating a product that is valuable, usable, feasible, and viable. With those things in mind, you can turn your potential client’s issues into your value proposition and the capabilities of your company.

As an example, we’re expanding with TrueAccord Retain, our product for first-party pre-charge-off solutions. We’ve scaled our capabilities with artificial intelligence and machine learning in late-stage collections, and early-stage is a natural extension of our growth and service value. In the age of COVID though, we’re seeing an increasing need for early-stage.

The major pain point we’ve seen is that it’s expensive to spin up and scale massive call centers quickly. We have a proven tech stack that can address the need to start quickly. Now it’s a matter of evaluating and understanding the unique challenges of collecting early-stage debts.

Are there projects outside of Retain that we’re currently working on that you’re allowed to share?

Our team is constantly asking “How do we bake all of our learnings into best practices?” One of our biggest projects right now is improving our own internal efficiencies. Everything that we’ve built so far has worked, but we need to—with higher account volume and higher growth rates—automate more of our own processes and move away from manual practices.

If you’re looking for more details on TrueAccord’s growing technology, here’s a conversation with our CEO, Ohad Samet all about our evolving offerings.

Another important piece of that is ensuring that processes are thoroughly documented. These growing pains are expected when an organization is growing quickly, and the more we grow, the more diverse our client base will get. We have to build on a foundation now that can accommodate that diversity consistently.

I’m also reflecting on what we know about our current users. We have to figure out the changes we hope to deliver for existing and future clients. Building a clear roadmap for that is huge which is why we’re working so closely on improving our internal organization. Improving our internal planning directly improves our product offering and client performance. 

That’s really exciting to hear. I know how much the startup world prides itself on its ability to pivot quickly, but creating a more defined system makes that system scalable. We’re all incredibly excited to see what comes next!

Are you ready to start scaling your collections solution quickly? Talk to our team and get up and running fast!

Code-driven compliance is the future of debt collection

By on June 12th, 2020 in Compliance, Product and Technology

Compliance regulations in the debt collection industry are built to protect consumers in debt from potentially predatory practices and ensure an equitable collections experience. For debt collection agencies, this often requires building out entire departments dedicated to keeping the agency in line with ever-changing debt collection laws and regulations. These teams are committed to reducing risk wherever possible.

One risk that is built into traditional debt collection practices is the potential for human error in a contact center environment. Digital debt collection platforms, however, offer code-driven compliance solutions that range from supporting existing agents to operating largely without the need for agent intervention.

Digital compliance solutions

Agent support

Operations managers throughout the collections industry cite high turnover rates in contact centers as a major challenge. While the exact number changes drastically depending on who you ask, contact centers may see annual agent turnover rates as high as 100%, but properly training contact center agents takes time (at TrueAccord our training process spans a full six weeks). High turnover in a space that requires thorough training means that newer agents may make mistakes when navigating important and complex regulations.

Some of this concern can be alleviated through the introduction of a curated content management system that provides prompts. These systems can be built with pre-written responses that adhere to compliance guidelines that improve agent compliance performance. While this may help to reduce the risk, the consumer experience is less than ideal.

Code-driven digital-first debt collection 

Digital-first debt collection agencies and other debt collection software tools provide systems that allow for close control over what actions are taken and what messages are sent to consumers. These messages are carefully crafted by a dedicated content team, reviewed by a team of legal and compliance experts, and are easily accessible for auditing purposes. They are also then managed by the digital system once they are implemented. 

Most importantly, these messages are then integrated into a digital, consumer-driven payment experience. More advanced systems use artificial intelligence and machine learning to customize a unique customer experience that is optimized for engagement and liquidation.

Compliant content creation

Pre-approved consumer-facing content

Building a digital debt collection system starts with creating compliant and adaptable content. Every email, text message, and landing page in a digital ecosystem is created by a team of dedicated content writers who draft and experiment with different approaches to encourage customer engagement. The guidelines used to draft these messages are shaped by collections laws, policies, and regulations. 

Are you interested in learning more about the content creation process? Here’s an interview with one of our content managers all about engaging with empathy.

Teams can also draft content that meets the needs of individual clients with specific brand considerations. Once the content is drafted, it is processed and reviewed by a team of compliance experts prior to being added to a content repository that the digital system can draw from.

Scalable compliance review process

The next step is to have a team of legal and compliance experts from within the debt collection agency review the content to ensure its adherence to the same regulations. Based on the client’s preferred level of involvement and resources, such a review process may also include a compliance team within the client’s organization. This process lays the foundation for compliant communication down the line.

Easily audited communication history

The content auditing process comes further down the line, but it is important to build that foundation early for the same reason stated above. Traditional call-and-collect debt collection agencies may record voice calls and even provide automated transcriptions of these calls. Unfortunately, these processes are not perfect because auditing activities can only review sample cases. Digital systems are able to accommodate a full audit-specific interface.

At TrueAccord, 96% of consumers resolve their accounts without communicating with an agent, so the vast majority of communications that exist are entirely automated and recorded. Compliance staff can easily search for individual accounts to review and evaluate all collections activity across multiple channels. Digital systems overall offer improved data retention and tracking to provide a clear picture of performance. 

Because the system saves this data, it’s easy to investigate how it responded to a particular message, as well as why it made a specific decision. When these communications are controlled by code, decisions are easy to trace and replicate.

How do these steps lay the foundation for a scalable digital compliance system?

Once content is in place, and there is an established process for reviewing it, digital debt collection platforms can connect to consumers. At TrueAccord, our machine learning engine, Heartbeat, is able to draw from our content library and improve communications with a consumer over time. Digital systems reach out to consumers when and how they prefer and these communication decisions are driven by data, not by individual agent decisions or potential biases.

Digital systems reach out to consumers when and how they prefer and these communication decisions are driven by data, not by individual agent decisions or potential biases.

Digital debt collection systems rooted in machine learning are dynamic. The content they choose to use for an individual consumer is determined not only by historical data but how a consumer responded (or did not respond) to previous communications. Every single message in the system is vetted to meet compliance standards, and the review process is always ongoing to maintain those same standards.

At any point in the customer lifecycle, a consumer can opt-out of communications by replying to a text message or by clicking a link in an email that lets them easily unsubscribe from future communications using that channel. Each email and payment page also provides a link for consumers to request debt verification via a few simple online steps.

Coded compliance continues to scale

As the system scales and communicates with more consumers in this way, it’s able to continually enforce compliance without needing to be retrained because it is built to be compliant from the ground up. Built-in compliance checkers can prevent the use of contact methods that the consumer has unsubscribed from or ensure they do not receive a payment offer that the creditor has not approved. 

Any compliance updates—such as new rules from the Consumer Financial Protection Bureau’s proposed rules—can be implemented securely and quickly at a company-wide scale rather than retraining on an agent by agent basis. 

An improved, more secure consumer experience

Collections regulations and laws are largely driven by a need to protect consumers from bad actors in the industry. Digital debt collection empowers consumers to manage their accounts at their own pace and communicate using their preferred communication channels.

By evaluating content before it is ever sent and programming a platform that delivers unambiguous content you can reduce confusion and improve the user experience. Clear, compliant messaging enables consumers to resolve their accounts through self-service without added support. This leads to a dramatic reduction in consumer complaints, and in TrueAccord’s case, many positive online reviews. 

A code-driven future for debt collection

Code-driven compliance offers predictable, pre-approved, and consistent collections methods. Coupling digital platforms with machine learning creates a system that improves over time and optimizes for a better user experience, guided by consumer preferences and shaped by compliance guidelines. This minimizes the need for agents to manage an account from start to finish and instead allows them to focus on more complex customer cases.

New technology is often seen as a risky investment, but digital debt collection systems offer more compliance security and more transparency—for consumers and creditors—than traditional debt collection agencies. Digital debt collection solutions not only evolve to meet consumer needs, but they can also continually adapt to changing regulations and quickly meet compliance requirements. 

Do you want to see the power of a code-driven compliance platform in action? Reach out to our team today to see what this looks like at TrueAccord.