The Results Are In:TrueAccord Consumer Satisfaction Survey

By on July 17th, 2017 in Company News, Industry Insights, Product and Technology

Today, 80 million consumers are in debt. They are often not treated well by collectors, and subjected to harassment, intimidation and an overall bad user experience that does not encourage or empower resolution. According to a recent CFPB survey 1 in 4 consumers felt threatened by collectors, 3 in 4 consumers reported that a collector did not honor a request to cease contact, over ⅓ reported being contacted at inconvenient times, and 40% of consumers reported they were contacted 4+ times per week. These results are quite disheartening, and demonstrate that the traditional debt collection agencies have not adopted user centric practices and behaviors, nor have they integrated technology into the process to adapt to changing consumer needs. They are stuck making large volumes of phone calls to uninterested consumers who end up complaining.

When we set out to survey our consumers about their experience with TrueAccord we weren’t quite sure what to expect, or if they would even respond. On one hand, we believe our data driven, consumer centric, digital first experience is reinventing the debt collection process and will replace legacy agencies, and consumer will appreciate that. On the other, we are still talking about debt collection, and most likely a lot of these consumers have experienced multiple negative collections experience and have low expectations of the process. They aren’t likely to recommend a debt collector, and as we’ve seen above, are highly likely to have had a bad experience.

Overall satisfaction

What we found was both exciting and inspiring, 80% of respondents were satisfied with their experience with TrueAccord. It’s an unprecedented number in an industry that, for decades, only attracted negative attention. TrueAccord is building a product and brand focused on delivering great user experiences and helping consumers rebuild financial health, and consumers are reacting to that. Traditional agencies’ behaviors have been impacting liquidation, hurting brand reputation and causing a lot of compliance risk. Yet they haven’t changed their ways. We show that working differently is possible – and will yield better results.  

Tone

81% of consumers stated that the tone and personalized offers in our messages were appropriate for their individual needs. Our content is personalized, and tailored to empower and motivate consumers to want to pay off the debt, combined with the ability to offer a wide selection of custom payment plans. Consumers’ needs are served and they are treated like customers. Our clients understand that debt collection is part of a natural consumer life cycle;at one point or another, most of us will encounter debt collectors, but unfortunately traditional agencies lack the technology and best practices to deliver good user experiences, leaving consumers feeling frustrated, angry and wronged. This does not have to be the case.

User experience

80% of our users had an overall positive experience with TrueAccord and recognized TrueAccord as different and better than other agencies. A large proportion of the other 20% resolved their debt by disputing it, so even though they may not feel great about their experience, they were able to dispute and discharge a debt electronically and with minimum hassle. It’s exciting to see that consumers see our brand the way we see ourselves, as innovators focused on great user experiences. We believe helping people get out of debt has positive impact for everyone involved, even (and sometimes more so) if getting out of debt means it can’t be collected.

What consumers had to say:

You were easy to work with and the payment plan worked for me. Even when I had to make a small change, it was no problem. I’m glad to have the debt behind me. I appreciate the email correspondence as opposed to numerous phone calls.”

“They worked with me and I needed that.”

“It is always a pleasant experience dealing with True Accord.”

“Wish you could handle all my debts.”

“I love the fact that TrueAccord was kind and polite! I wanted to pay my debt but needed a plan that wouldn’t leave me over spent or struggling every month. TrueAccord was happy to accept the payment plan I requested. Thank you!”

“TrueAccord provided me a way to be true to my word.”

“The agents are all very friendly and accommodating. It doesn’t feel like you are dealing with a collection agency.”

“The best collection agency ever!”

 

I’m Excited to Join the CFPB’s Consumer Advisory Board.

By on July 7th, 2017 in Company News
TrueAccord Blog

I’m honored and excited to have been appointed to the CFPB’s Consumer Advisory Board. With this appointment, the CFPB is sending a strong message about how it views technology’s role in shaping the future of consumer finance in general, and debt collection in particular. I’m proud to be able to represent the industry’s point of view while making sure we usher in a new era of great user experience and technology innovation.

When we founded TrueAccord in 2013, we set set a goal for ourselves – to go to Washington and influence policy making in the debt collection space. Ever since then, we engaged with the CFPB in various ways: quarterly meetings through Project Catalyst, participating in the SBREFA panel for the proposed debt collection rule, and even potential data exchange. We view policy making that enables better debt collections as our mission, and this appointment is just another step in the process.

This appointment isn’t about me, when I attend these meetings I will represent the industry, TrueAccord, its team and our consumers. I will take as very seriously, like we all take our mission. This could not have happened without the TrueAccord team’s hard work and laser focus on making a difference.

Default Rates Are Going Up As Bad Collection Practices Continue to Ignore Debtors

By on June 28th, 2017 in Compliance, Industry Insights
TrueAccord Blog

The US economy has taken a turn for the better in the past year. Unemployment has plummeted, the Federal Reserve is raising rates, and the stock market is soaring. However, for the past two quarters, several issuers reported an increase in charge off rates. While banks may be changing their underwriting standards to encourage growth, there is another contributing factor: a fundamental shift in the way consumers live and work, one that the credit card industry has failed to adjust to.

2008-2009 was a turning point for the US economy. Millions of jobs were lost across all industries, without much hope of recovery. College grads joined a crippled job market, and felt like they needed to “hustle” and find alternative means to sustain themselves. Uber, founded in 2009, created an opportunity as standard-bearer of the gig economy and many others have followed suit. At the same time, social media became prevalent as Facebook went international in 2007. These processes created  new consumers – the millennial cohort. Millennials are on the move, working several unsteady jobs, managing their own time and relying heavily on social media and digital communications. They use traditional financial solutions like credit cards, but the dominance of mobile and digital in their life is driving their preferences for communications and interactions with people and businesses. However, if they default, they are effectively sent back to the stone age, where in time to a world that knows nothing about them, and does little to service them effectively. When a system that “always worked” faces a new type of consumer behavior, it breaks – and leads to increased defaults and losses.

Consumers expect a better user experience – even in collections

As digital, always connected users, millennials expect their bank – or the bank’s collection vendor – to fit their lifestyle and preferences. Unfortunately, the debt collection and recovery industry hasn’t changed in decades. There has been little investment in moving away from phone calls and letters to a more digital and technology driven process,  that can deliver a better user experience for those in debt.

Contact through digital channels is table stakes for the digital consumer. Many have never  visited a bank branch and most will not answer a call from an unidentified number, or respond to a letter. According to Accenture’s “Banking Customer 2020”, 58% of consumers use their mobile device when seeking support from their bank, 53% report going to their online banking center at least once per year to sort an issue; 78% report doing so to make a payment. More than half of the population has adopted  digital channels to manage their lives, and will not respond to cold calls and letters in nondescript white envelopes. Call center-based collection approaches fail to get these consumers on the phone, and debts make their way to charge off without any meaningful engagement from the consumer.

Once contacted, millennials expect clear communications. The common disclosures used in debt collection, for example, feel onerous and obscure – causing them to disengage (the CFPB recognized that and is planning a survey regarding disclosures). The dispute process, asking for more information about their debt, is onerous and slow. Consumers need, and deserve, communication that drives them to action rather than intimidates and coerces them. Collectors are pressured to cold call and create instant rapport with unwilling debtors – and they are failing this task in growing numbers.

Finally, consumers need flexible payment options that fit their work schedules. As Robert Reich notes, while 1099 workers may make slightly higher hourly salary when working, their hours are irregular and difficult to schedule. This means irregular paychecks that can vary in size and resulting disposable income. A consumer might be able to pay $100 this pay period, $150 next time and only $50 the following one. Traditional approaches fail to adjust to these realities, focusing on steady payment plans that these consumers cannot always keep up with.

Machine Learning Based Liquidation Looks Different – and That’s a Good Thing

By on June 26th, 2017 in Industry Insights, Machine Learning, Product and Technology
TrueAccord Blog

TrueAccord serves major issuers, debt buyers and lenders across the US. We compete with traditional collection agencies and beat them: TrueAccord collects more than 1.5 times the competition in a typical 90 day placement period. We use a machine learning based system, HeartBeat, that replaces the traditional call-heavy model with digital first communications that compliment consumer behavior and mode of communication, but that makes our liquidation curves look different from traditional agencies.

Traditional liquidation curves

Traditional liquidation curves typically shoot up in the first 30-45 days, followed by a plateau around 60-80 day, with a possible bump towards the end of the placement window. This pattern is driven by several factors.

Routine: agents receive fresh accounts and are eager to call them. They fire up dialers and quickly reach consumers who can either pay or be lightly pressured to pay. After a few weeks of calls, agents are tired of calling the same consumers. They heard what they think are excuses, have driven all the easy payments they could drive, and are ready for new accounts. “Old” accounts, as old as 30 days, get a worse treatment. Collection managers know this, and try to trick collectors into thinking they got fresh accounts by pulling the accounts out from the system and re-entering them. This rarely works. Collectors lose focus and with it, performance.

Net present value: settlements are better than payment plans for collectors – they mean more money now, versus a payment plan that may fail, and require reminders and additional work by the collector. Collectors opt for more settlements earlier, if they can get the consumer on the line. Under the pressure of a call, the consumer may commit to a payment plan. In this case the collector prefers as high a monthly payment as possible, since they assume the payment plan will fail early. The consumers, struggling with irregular cash flow and a large payment they shouldn’t have committed to, fail payment plans at a staggering rate: as much as 50% of payment plans fail.

Remorse: consumers who agree to settlements or plans often feel remorse after getting off the call, and tend to charge back on payments they made. Chargeback rates in the debt collection industry are so high (rates as high as 2% are not rare), that most payment providers won’t work with collection companies.

The initial bump in liquidation is often enough to beat other phone based agencies. Since all agencies use the same methods, a slight advantage in selecting the right accounts to call first can get an agency ahead of its unsophisticated peers.

The TrueAccord liquidation curve

In contrast to the traditional curve, TrueAccord’s liquidation curve is somewhat linear. It often starts lower than the traditional agency, but continues to rise through the placement period until it crosses and exceeds its competitors. That inflection point can happen as late as day 80 (before the algorithms have been tuned, early in a pilot) and as early as day 15 (once the algorithms have learned how to handle a new product). The difference is driven by several factors.

Data driven treatment at scale: TrueAccord’s system is machine learning based and digital first. Since it starts with an email, it can initiate contact with all consumers easily, without having to call them often – and consumers are much more likely to respond to digital communications than to a phone call: while Right Party Contact rates often hover around 4-5%, email open rates on TrueAccord’s platform reach 65-70% and click through rates reach 30-35%. Once it sends its first email, it uses real time tracking of consumer responses to tailor its next steps. The system relies on hundreds of millions of historic contact attempts to optimize its contact strategy. If the consumer doesn’t reply, the system can automatically switch between channels (from email to text, call, letter, and so on) to reach the consumer. It also uses data to figure out what time of the day to contact the consumer that will yield the best response rates, call centers are limited to just making phone calls, which often consumers ignore because they are busy or just don’t pick up calls from unknown numbers. Since a machine doesn’t get bored, it continues contact attempts (3 a week on average) until it is told to stop. Targeted, consistent communications at scale mean that more consumers will interact with our system compared to a call center.

Optimizing for liquidation: a data driven system can use historical data to understand what best fits consumer needs and leads to better liquidation. It doesn’t need to push for early settlements because its automation lets it serve each consumer according to their needs – making custom tailored plans viable. Consumers get easier payment terms that fit their needs, and end up paying more. We convinced several of our clients to move from a default payment plan length of 6 months to 12 months. Contrary to call center based intuition, these longer plans get more consumers to sign up and don’t cannibalize settlements, in turn leading to an increase in liquidation. The machine learning system can service these plans at scale and reduce failure rates: TrueAccord payment plans complete as much as 89% of the time (as low as 11% breakage). By the time payment plans for traditional collectors fail their second payment, TrueAccord’s liquidation rates start soaring.

Best in class user experience: consumers don’t like phone calls or letters. They prefer 24/7, personalized, easy to use services – and collections aren’t any different. Using our system they can customize and sign up for settlements, payment plans, or ask for debt verification. Having access to their account information, and a sense of control over payment options, consumers don’t feel pressured or remorseful after paying. TrueAccord’s chargeback rates are next to not existent.

Bottom line

Machine learning based debt collection is different in many ways that benefit creditors and consumers. Our liquidation curve tells the story of how our system behaves differently than call center based collections – serving consumers at scale, using their preferred communication channel, and while tailoring payment solutions that work for them.

Personalized Digital Experiences Drive Engagement and Liquidity for Consumers

By on June 15th, 2017 in Industry Insights
TrueAccord Blog

Debt collection has existed for as long as consumers have been taking loans. For the past few decades, collectors have been building call center businesses – hundreds and thousands of calling agents, using automated dialers to contact indebted consumers, compensated with commission once they reach their collection goals. Consumers are often harassed by overzealous collectors looking to meet their goals, calling as much as 6 times per day. It’s a stressful environment focused on one thing – get the money or get out.

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Fintech Companies Are Learning to Work with Regulators

By on April 24th, 2017 in Compliance, Industry Insights
TrueAccord Blog

This article, written by our In House Counsel Adam Gottlieb, first appeared in the RMA Insights Magazine

The word “startup” conjures images of stereotypical open offices, complete with ping pong tables, standing desks, and people in hoodies feverishly hammering at keyboards. Startups are often associated with high risk, scrappiness, and the ability to break things and move fast–all a stark contrast to the bureaucratic and highly-regulated environment that most debt buyers and collectors operate in. Yet, as startups begin venturing into the area of financial technology, they have had to adjust to new operating principles and new stakeholders, with the government chief among them.

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How Tax Season Affects Debt Collection – and TrueAccord

By on March 29th, 2017 in Industry Insights
TrueAccord Blog

By Roger Lai, TrueAccord’s Head of Analytics.

Tax Season in Debt Collection

Tax season is to debt collection as holiday season is to retail. According to the National Retail Federation, of the 66% of consumers who are expecting a tax refund this year, 35.5% plan to spend their refund on paying down debt. For this reason, mid-February through May is considered the most productive time of the year for debt collection by many in the industry.

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Live from LendIt: TrueAccord on AI in FinTech

By on March 20th, 2017 in Industry Insights
TrueAccord Blog

In case you missed it, our CEO Ohad Samet spoke in a panel at the LendIt Conference about the use of artificial intelligence in FinTech.

Joined by industry leaders in a propelling talk, this video is not to be missed.

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Using phone in a digital world. A Data Science story.

By on March 16th, 2017 in Data Science, Debt Collection, Machine Learning, Product and Technology
TrueAccord Blog

Contributors: Vladimir Iglovikov, Sophie Benbenek, and Richard Yeung

It is Wednesday afternoon and the Data Science team at TrueAccord is arguing vociferously. The white board is covered in unintelligible hand writing and fancy looking diagrams. We’re in the middle of a heated debate about something the collections industry has had a fairly developed playbook on for decades: how to use the phone for collections.

Why are we so passionately discussing something so basic? As it turns out, phone is a deceptively deep topic when you are re-inventing recoveries and placing phone in the context of a multi-channel strategy.


 

Solving Attribution of Impact

The complexity of phone within a multi-channel strategy is revealed when you ask a simple question: “What was the impact of this phone call to Bob?”

In a world with only one channel, this question is easy. We call a thousand people and measure what percentage of them pay. But in a multi-channel setting where these people are also getting emails, SMS and letters, there is an attribution problem. If Bob pays after the phone call, we do not know if he would have paid without the phone call.

To complicate matters further, our experiments have shown that phone has two components of impact:

  1. The direct effect — the payments that happen on the call.
  2. The halo effect — the remaining impact of phone; for example seeing a missed call from us and going back to an email from us to click and pay.

To solve the attribution problem and capture both components of impact, we define the concept of incremental benefit as:


 

Intuitively, the incremental benefit of a phone call is the additional expected value from that customer due to the phone call. For example, assume Bob has a 5% chance of paying his $100 debt. If we know that by calling him, the probability of him paying increases to 7%, then the incremental benefit is $2 (100 * (0.07 – 0.05)).

 

How we calculate incremental benefit

Consider the incremental benefit equation in the last section. It requires us to predict the probability of Bob paying for each scenario where we call him and do not call him.

Hence we created models that predict the probability of a customer paying. These models take as inputs everything we know about the customer, including:

  • Debt features: debt amount, days since charge-off, client, prior agencies worked, etc
  • Behavioral features: entire email history, entire pageview history, interactions with agents, phone history, etc
  • Temporal features: time of the day, day of the week, day of the month, etc

The output of the model is the probability of payment by the customer given all of this information. We then have the same model output two predictions: probability of payment with the current event history, and probability of payment if we add one more outbound phone call to the event history.

Back to our example of Bob, the model would output the probabilities of 7% and 5% chance of paying with and without an additional phone call respectively.

This diagram is a simplification that omits many variables and the actual architecture of our models

 

Optimal Call Allocation

The last step of the problem is choosing who to call, and when. The topic of timing optimization deserves its own write-up, so we will close with discussing who we call.

Without loss of generality, assume that we would only ever call a customer once. The diagram below has the percentage of customers called on the x-axis. And the y-axis is in dollars with 2 curves:

  • Incremental Benefit — this curve shows the marginal incremental benefit of calling the customer with the next highest IB
  • Avg cost — this horizontal curve shows the average cost of an outbound call

 

There are two very interesting points to discuss:

  • Profit max — calling everyone to the left of the intersection of incremental benefit and avg cost is the allocation that maximizes profit. Every one of these calls brings in more revenue than cost.
  • Conversion max — notice that incremental benefit dips below zero. This is especially true when you remove the assumption that we only call each customer once. The point that maximizes conversion for the client is to call everyone to the left of where incremental benefit intersects with zero.

Our default strategy is to call all customers to the left of the profit maximizing intercept. Interestingly, an intuitive investigation of the types of customers selected reveals customers at two extremes: we end up calling both very high value customers that have shown a lot of intent to pay (e.g. dropped off from signup after selecting a payment plan) and customers where email has been ineffectual (e.g. keeps opening emails with no clicks or no email opens.)

 

Conclusion

The world has become increasingly digital, and a multi-channel strategy is the right response. Bringing the traditional tool of phone, as just one channel within this strategy, forced us to rethink a lot of assumptions and see where the problem led us. We began by replacing the traditional “propensity to pay” phone metric with incremental benefit, found ways to predict this value, and implemented a phone allocation strategy that maximizes profits for the business.

Live from LendIt: TrueAccord on Breaking Banks

By on March 13th, 2017 in Industry Insights
TrueAccord Blog

This week, Breaking Banks host Brett King chatted about the ideas of debt rehabilitation with Ohad Samet, CEO of  TrueAccord, and how machine learning and AI can help people fix their credit situations.

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