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.

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