Come meet us at DCS! Two sessions and many people on the ground…

By on August 15th, 2018 in Industry Insights
TrueAccord Blog

This year at DCS we’ll be moderating or presenting in two sessions.

Understanding What Consumers Want
The consumer market is shifting. Where each of us once had one landline at home, we now all have cell phones and lap tops. Communication preferences have shifted tremendously. Join a roundtable to hear from peers about identifying consumer needs and how to respond to them in an ever changing technology and regulatory environment.

Strategic Implementation of Machine Learning and AI in Your Collection Operations
Machine Learning and Artificial Intelligence hold a huge promise for the ARM industry. Are they all they are made out to be? Will AI be the job killer some fear it will be? Hear about strategies and tips for implementing ML and AI in a human decision-heavy market like ARM, how to integrate those technologies to create real leverage, and how to combine ML/AI with your collections staff.

Check out the sessions and email our team at if you want to meet.

Did New York State’s Stringent Rules Contribute to Increased Lawsuits?

By on August 9th, 2018 in Industry Insights
TrueAccord Blog

Crain’s New York writes about a surge in debt collection litigation in recent years. While the article conflates a few things such as regulatory rollback that started after lawsuits started coming back, and new lawsuits with one from 2006, we can’t help but wonder what’s driving the new increase in lawsuits filed in NYS.

One possible factor is the tight regulation over communication methods, introduced by the NYS’s Department of Financial Services. The DFS requires that collectors request express consent to email consumers, even if they initially provided their email to the creditor. NYS stands out in doing so, contrary to other states and what we believe will be the CFPB’s position. This approach to email renders digital communications 20 times less effective even though consumers often say it is their preferred method – much more than phone calls.

Between limited digital communications and the decline in phone call effectiveness, we can’t help but suspect this limited ability to contact consumers is leading lenders to resort to lawsuits. If indeed that is the case, it’s a cautionary tale of unintended consequences. Limiting consumer choices, not allowing access to digital channels, stops any possible innovation in the debt collection space – and that innovation is gravely needed to improve consumer protection and respond to consumer choice.

Subprime Blindness is holding back the next big break in fintech

By on August 7th, 2018 in Industry Insights
TrueAccord Blog

In December 2017 Bloomberg posted this infuriating story about a man who was hounded by phantom debt, and how his crusade took down a Bad Guy (recently sentenced to almost 17 years in prison). It’s a captivating story with a happy ending, but one quote really caught my eye:

Therrien says he paid back the debt promptly. He was offended by the Lakefront woman’s suggestion that he was a deadbeat. “I’m a person who believes in personal friggin’ responsibility,” Therrien tells me. “I signed an agreement. And I fulfilled my obligation.”

This quote demonstrates one of several key misperceptions of consumers in debt. It’s something I like to call “subprime blindness”, a deep seated lack of understanding of and empathy for the consumer’s experience, motivations, and psyche, and it has wide ranging effects on our ability to start, fund, and scale solutions for the debilitating debt problem in developed markets.

Subprime blindness usually takes one of two forms: on one hand the condescending “it would never happen to me” approach, looking down on people in debt. This group thinks of debtors as morally inferior, deficient people choosing to remain in debt. The other is complete disregard of the reason most people are in debt, assuming everyone can afford to pay their debts if they were only afforded a convenient way to do so. Many investors I talk to have a story about debt, usually missed copay or some lingering internet subscription. To the well off it’s clear that they, and everyone they know, would pay if they just got a polite message.

Neither approach is correct. The majority of consumers end up in debt because they lost a job, had a medical emergency, or experienced another significant life event. These are not careless spenders or malicious consumers who couldn’t care less about their debt. They are often trapped and are doing the best they can given dire circumstances. Subprime blindness stigmatizes being in debt and hampers any ability to offer long term solutions that improve financial health and build equity.

This is what TrueAccord is solving. Radically changing debt collection is just step one. Our product relies on a radical—yet simple—alternative to Subprime Blindness: that consumers in debt are experiencing a temporary difficulty, and treating them like valuable customers will not only lead to better debt collection results, but will also help them build equity to eventually exit the cycle of debt.

How we apply empathy to write engaging content

By on July 24th, 2018 in Industry Insights
TrueAccord Blog

By: Vivian Chau with Ohad Samet

People with debt come from all walks of life. Some get into debt after losing their jobs; others fall behind on bills while taking care of ailing family members or dealing with unexpected expenses like a car breakdown. Because there are so many ways someone can be saddled with debt, we, at TrueAccord, have a unique challenge. We need to connect with our customers in times that are often difficult. For our content to be engaging, we need to make our customers feel understood. So, how do we write engaging content?

We start with empathy

The first question we ask ourselves when we write is: Do we understand our customer? We need to know where he or she is in the lifecycle of our communications with them, as well as understand his or her unique situation. Is it the first time the customer’s ever heard about the debt? Maybe he’s confused. Or maybe she’s thinking “Yes, I know about this debt, but I need more time.” We help people understand they have options. By opening our emails, customers can start learning about the choices they have in front of them, and if they choose, start paying online.

The four attributes of empathy

But how, exactly, do we “apply” empathy? We use nursing scholar Theresa Wiseman’s four attributes of empathy as our framework when we write. The first attribute is to be able to see the world as others see it. Can we see the world from our customer’s point of view? Our customers are diverse, from various backgrounds and locales, and so are we. We recruit people from different backgrounds, experiences and life circumstances to work at TrueAccord. Diversity isn’t only a way to tap into our unique talent pools—it’s key to how we understand our customers.

Because of our diverse perspective, we’re non-judgmental. We don’t make judgments about people with debt because judgment is not a part of what we do. It’s irrelevant to our collections strategy. We engage with customers thoughtfully and respectfully to show people their options on how to resolve their debt.  

What sets us apart is our ability to engage with our customers with compassion. In our emails, we include phrases such as “We’re here to support you“ and “We can work together to find an affordable solution for you.” We are motivated to act with compassion because our mission is to empower our customers to make informed financial decisions.

Third, we understand our customer’s feelings. Money is a touchy subject. Debt has an especially negative stigma in our society. While unfortunate, debt is a reality of a society that relies on credit (similar to the common saying “Nothing is certain but death and taxes.”). Many customers write in to express frustration around needing more time and other payment options. For example, a customer has written to us with “I have a plan and will get to this debt as soon as I can, but I have to go in order of priority and you are not at the top.” Emotions can run high, whether that’s expressed through email replies or over the phone through our Engagement Team. While there’s social and emotional stigma associated with debt that’s not necessarily true, that stigma still exists and can contribute to heated emotions.  We acknowledge that.

Fourth, we communicate our understanding of our customer’s feelings. One example is how our emails include phrases like “We know facing an outstanding balance alone can be overwhelming.” We need to write content that is not just engaging but also authentic because we cannot be empathetic without building a level of trust and understanding between ourselves and our customers. This applies to not just our customer communications, but our overall company voice when it relates to our product, our marketing, our PR, et cetera. Everything. That is why we are upfront with our motivation—we want to help people make informed financial decisions.

Sparking conversations

It’s a noisy world. We all get dozens, if not hundreds of emails every day. So how do we put these four attributes together and cut through the noise? By applying this framework of empathy, we don’t need to rely on cheap psychological tricks to get a reaction out of people. Instead, we use our content as an opportunity to begin a conversation. To engage and nurture our relationship with our customers. We are thoughtful and considerate of how we communicate, especially when it comes to a subject that has as much stigma as debt does.

We’ve found that when we engage with our customers with compassion, we get extraordinary results for our clients. And most exciting of all, we see that it is possible to make debt collection a pathway to financial health.

Vivian Chau is TrueAccord ‘s Product Manager for Content Strategy

Video: Implementing AI to Change the Consumer Experience in Banking

By on July 3rd, 2018 in Industry Insights
TrueAccord Blog

Our CEO, Ohad Samet, gave a talk at the Bank Innovation conference. There he discussed the overarching goal of AI and Machine Learning in banking, what it can do for consumers in niches that banks are leaving, and gave a few examples from our day to day at TrueAccord.

You can find the talk here.

Ohad Samet at Bank Innovation 2018

The Pros and Cons of Trying to Sell Into Large Companies

By on June 14th, 2018 in Industry Insights
TrueAccord Blog

Collection agencies that want to woo larger clients better not hold their breaths, because the sales cycle for those types of clients has expanded dramatically, according to one speaker during a webinar hosted yesterday by

Read more insights from our CRO, Joe Gelbard, in this webinar summary.

Getting to Know Sophie Benbenek

By on June 13th, 2018 in Industry Insights
TrueAccord Blog

Sophie Benbenek always planned on a career in the public sector, and would be working on policy or for some non-profit if she had not ended up at TrueAccord. But helping people is still very much in her job description. As head of data science at TrueAccord, Sophie was the lead architect for the decision engine that drives self-service collections at the company. Read on to learn more about Sophie, her love of Marvel Comics, and why she likely finished reading this sooner than you or I did.

Accounts Recovery ran a profile of our Sr. Manager of Data Science, Sophie Benbenek. You can read it here.

How TrueAccord Optimizes Multiple Collection Strategies

By on June 12th, 2018 in Industry Insights
TrueAccord Blog

Companies have many choices as to how they manage their delinquent and charged off debt portfolios. Options range from having an internal collections team, working with third parties, selling their debt to debt buyers to engaging the court system. Every company has its own requirements, so collection strategists must leverage the available options to varying degrees to optimize its resources, protect the brand name, and recover as much as possible while remaining compliant.

TrueAccord achieves extraordinary collection results because it’s designed to fit into any debt collection model. Here’s how we optimize each stage of the collections process.  

Pre-charge Off vs. Post-charge Off

Creditors service their debt in both the pre and post-charge off stage. Some creditors may choose to sell their debt post-charge off, but more sophisticated creditors will often have a strategy with multiple phases.

Pre-charge off focuses on remediation and putting the customer back on track. The collection is on the part of the loan that is late, which is often not the whole amount. Interest and fees may be accruing while most of the collection work is done as first party, under the creditor’s name. Post-charge off is the opposite; most collection is done as a third party and no interest and fees accrue. At this point, many creditors believe the customer relationship has been lost .

First-party vs. Third-party Collections

First party refers to using the creditor’s brand, while third party means using the agency’s. The former is common in pre-charge off collections and the latter in post-charge off.  However, savvy collection strategies make use of both first and third-party collections in both stages.

Historically, pre-charge off is considered to be “soft” collections while post-charge off are recoveries or “hard” collections. As banks and issuers see more repeat customers, they are moving away from “hard” collections, because better customer relationships means more repeat customers. Several issuers use “back on track” plans to give customers in debt an on-ramp to financial services through more secured/specialty programs or products.

Legal and Pre-legal Channels

Depending on the characteristics of the debt, creditors can take legal action by hiring a lawyer to review the accounts and determine which ones to pursue and collect. However, many creditors first want to see how the collections route will succeed before resorting to legal action. This collection period before utilizing legal demands is often referred to as a “pre-legal” strategy.

The TrueAccord Advantage

Our goal with every client is to help build a strategic vision and chart the most effective path to great returns. TrueAccord fits into multiple stages of a sophisticated collection strategy: first party pre-charge off, first or third party post-charge off, pre-legal strategy, and even as a champion challenger to debt sales.

We help determine which accounts to pursue, for how long, and what the next steps should be after accounts have been handled by our system. TrueAccord has a vast library of content and algorithms based on years of real-life collections data that fit into these various strategies to improve returns and increase customer engagement.

For large clients with diverse collection requirements, we leverage multiple strategies based on their existing approach towards their current use of pre- and post-charge off, debt sales, legal, and placement duration. TrueAccord offers a combination of solutions to make the most out of our strategy. Our debt collection automation technology, Heartbeat, can handle the account as a first party pre-charge off, then become a third party post-charge off followed by a call-center agency, or a pre-legal strategy followed by a law firm.

TrueAccord works with every client to optimize collections performance for the long run. We can help you build an intelligent, sustainable and successful collections strategy. For more information, check out our podcast page on SoundCloud.

How to Design the Perfect Pilot with TrueAccord

By on June 5th, 2018 in Industry Insights
TrueAccord Blog

We like to tell our new clients that working with TrueAccord to manage debt collection will be an entirely different experience than they’re used to. Traditional collection agencies are usually fast out of the gate, calling the highest value debtors and pushing for short-term plans or liquidation settlements. The problem with this approach, apart from the lack of learning mechanism, is it sacrifices long term gains from consistent payers for short term settlements. It may move quickly but it also fades quickly and puts a damper on long-term results.

TrueAccord takes a more steady and consistent approach, both in learning and offering payment arrangements that customers stick to. Our decision-making engine, Heartbeat, is based on machine learning and it is the driving force that helps us find connections between different types of debt along common similarities. It learns as it interacts with consumers, and ends up beating our call center-based competition. It does take some time for the machine to learn and tune itself; it starts behind at first but rapidly picks up steam as the decision engine learns more and more and we begin to make adjustments based on the inputs that are unique for each client and portfolio.

Our piloting process is built to help the machine to learn and fine tune its strategy. It is the first step in determining the right debt collection approach for a type of debt As we gain more experience and work with millions of consumers, we leverage our experience working with major banks, issuers and lenders to give our subsequent clients the best results early and consistently based on those accumulated lessons.

Here’s how we build the perfect pilot process.

  1. Gather Client Data

Our new client first gives us the base collections information and content, including the number of accounts, the number of placements and the duration of each. While some clients have giant placements of 10,000-100,000 accounts per month, others have smaller sizes of 1,000-5,000. Ideally, we look for at least three placements of 3-6 consecutive months, with at least  5,000-10,000 accounts per placement. This ensures we’re analyzing enough information to ramp up the algorithm, perfect the experimentation model and enhance performance of future debts.

TrueAccord uses a dynamic, self-service model for collections, which means we try to get the customer to pay on their own without any direct contact from an agent. Only two percent of our customers ever speak to an agent on the phone or via email, and more than 90 percent of customers with balances of over $300 choose payment plans. Since we focus on getting more customers on payment plans with lower breakage rates, the liquidation curve appears slow at first because of the time it takes to optimize customer-plan fit, but it catches up and exceeds the competition’s curve in roughly 45-60 days .

  1. Build the Ideal Initial Model

When we onboard a new client, we look for specific policies and procedures they have, disclosures, content and communications to come up with the right recipe for our pilot, including duration of payment plans and settlement authority. That’s where Heartbeat’s decision-making engine comes into play. Heartbeat is always evolving based on all the data it receives.

If the client has a product that we haven’t worked with before, we gather anecdotal information and do a manual review to see what new features we can add to Heartbeat to optimize collections. We validate the anecdotal information with data by monitoring direct consumer engagement rates. For example, if more than two percent of consumers are still reaching out to our engagement team that’s an indicator that we need to improve the flow to service them manually. We build a feedback loop into the process to continually evaluate if we have the right configuration settings and determine how to modify our strategy.

  1. Experiment, Learn and Optimize

Once the pilot is up and running we continue to execute on a suite of performance enhancing experiments. Our goal is to gain data that comes from real-life testing of each strategy and content. Over time, Heartbeat learns automatically how each scenario will impact liquidation, giving us valuable insight on how the product and process are working for each customer, not just on hard outcomes such as liquidation or conversions.

Here’s a quick example of how we would adjust the pilot based on higher-than-normal engagement rates. On new types of debt, we might not be using email content that is easily understood by each audience. The consumer might not even know why they are in debt in the first place, or to whom they owe the debt (especially with debt buyers who collect on debt that may be old, and using a name that the customer doesn’t immediately recognize), and that’s why they reach out to the engagement team. When we detect a spike in contact rate and run through a manual review, we discover how to refine the messaging and target each consumer better to lower the direct engagement rate. Over time, Heartbeat learns to automatically classify each engagement so there’s no need for a person to take the time and do it.

After testing, the final step is to roll the learnings back into the machine learning models. In the end, it’s all about creating a cadence of placing accounts, monitoring performance, building consistency into the model and maximizing liquidation.

TrueAccord’s highly successful debt collection model all begins with a well-oiled piloting process designed to kick-start the machine learning engine, enhance the consumer collection experience and optimize long-term liquidation rates. To hear more, please tune into our podcast.

How We Created the Heartbeat of TrueAccord

By on May 29th, 2018 in Industry Insights
TrueAccord Blog

The genesis of Heartbeat—the machine learning engine that makes TrueAccord debt collection a reality—is a story that demonstrates our commitment to consumers who hope to take control of their financial future. Heartbeat is how we create a more humane and thoughtful collection experience.

So What Is Heartbeat?

Heartbeat is a fully automated and reactive decision engine that uses a combination of machine learning and data-driven heuristics to determine the optimal way of interacting with each individual debtor. It tells us when we should contact them, how often, through which channel, with what content, and what specific types of offers we should provide. Most importantly, it is the engine that replaces an agent’s phone-based collection activities with a data-driven strategy, ultimately making the same decisions, but automatically, more quickly and with a bigger heart.  

How Was It Built?

We built Heartbeat with three key tenets in mind. The first is compliance: to create a pre-approved boundary for what should and shouldn’t be said to each debtor. The second is performance: how we leverage data-driven heuristics to test our assumptions and continually improve the performance of our debt collection system. And the third is the customer experience: how consumers engage with the product at every phase and how we ensure we’re seeing positive reactions. Put these all together and they constitute the core foundation of Heartbeat.

We Start with Data, Then We Test

The process starts with data, and lots of it. We’ve collected years of historical collections data to help determine the optimal way to communicate with consumers and generate the best collections model. We then set up an experimentation engine to test and refine the process for continuous improvement.

For the testing to be relevant, we ask a few key questions: Is this a problem that we can define well enough to solve and take action? Do we have enough data from different segments of our population to solve the problem for all of our customers, not just some? And does the result add value to the process? Otherwise, it’s not worth putting the time into it.

Once we decide to move forward, we establish a hypothesis (e.g. paydays are the best days to set up recurring payments) based on the intuition of our domain experts who know what an ideal customer journey should be like. We test our hypothesis with an A/B experiment to see if it performs better than our current status quo. The data we collect from these experiments shows us what tactics work best. To ensure we’re optimizing for various audiences,  we re-target the test to new segments until we have enough data to apply the new treatment to the broad audience.

Then Machine Learning Takes Over

The biggest challenge is that data and heuristics are not enough to offer highly personalized treatments at scale. At some point we have to transition, by taking the learning outcomes based on all of our initial data and programming them into a machine learning model. The goal here is to replace human heuristics with an automated decision-making model that continues to learn from multiple samples at scale. A human agent is prone to biases, such as using non-compliant language in their calls when pressed to make their monthly numbers or using the wrong tone based on a previous conversation that may have impacted their mood. A machine learning model doesn’t fall prey to these biases.

The more data we collect, the better the system gets and the more accurately it represents edge cases and special needs. Now with more than 2.5 million consumers and tens of million of interactions, we’re seeing great results and constant improvement. The larger sample sizes also allow us to reach a statistically significant result faster in large experiments, often in only 30 to 60 days.

What’s Next for Heartbeat?

Right now, only two percent of our customers still need to interact with one of our agents. That’s already a pretty impressive number, but we still want to reduce it even further.

We constantly scale the technology behind Heartbeat and improve its intelligent self-service capabilities that feature our three key tenets: better compliance (Heartbeat can navigate the legal restrictions with less risk than a person), better collections performance (50-500% better than our competition), and a better customer experience where consumers are empowered to manage their debt in a way that puts control back in their hands and treats them the way they want to be treated.  

At TrueAccord, we’ve always been committed to providing the best customer experience for a behaviorally complex debt collection process, and Heartbeat is true to its name in working to that objective.