Debt Collectors Want To Use AI Chatbots To Hustle People For Money

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The collections industry is pushing GPT-4 as a dystopian new way to make borrowers pay up, replicating the debt system’s long history of racial bias.

An industry historically known for harassing and intimidating people down on their luck is replacing human debt collectors with AI-powered ones, in a move that one company says will "change debt collections forever."

AI tools will take the industry into “a new era of debt collections,” according to New York and Bangalore-based A digital voice agent—the new incarnation of a robocall, using AI chatbots and text-to-speech capabilities for dynamic, responsive conversations—could make millions of outbound calls in just a few days, the company claimed, contacting and requesting payment from a collection agency’s entire portfolio of debtors at a far lower cost than human staff.

Human agents could use AI at every stage of the collection process, the company’s blog claims, delivering “instant scalability” through “end-to-end automation,” which of course would boost productivity and lower costs. On the other hand, for someone on the other end of the call, the chance to speak with an actual human during the process becomes ever more distant. did not respond to Motherboard’s questions about using AI for debt collection. Increasingly though, software services marketed to debt collectors are starting to incorporate machine learning and even generative AI, with the promise of optimizing the recovery of funds from debtors. And at a time when AI hype is booming and debts are at an all-time high, these uses are only likely to grow.

The prospect of automated AI systems making phone calls to distressed people adds another dystopian element to an industry that has long targeted poor and marginalized people. Debt collection and enforcement is far more likely to occur in Black communities than white ones, and research has shown that predatory debt and interest rates exacerbate poverty by keeping people trapped in a never-ending cycle. 

In recent years, borrowers in the US have been piling on debt. In the fourth quarter of 2022, household debt rose to a record $16.9 trillion according to the New York Federal Reserve, accompanied by an increase in delinquency rates on larger debt obligations like mortgages and auto loans. Outstanding credit card balances are at record levels, too. The pandemic generated a huge boom in online spending, and besides traditional credit cards, younger spenders were also hooked by fintech startups pushing new finance products, like the extremely popular “buy now, pay later” model of Klarna, Sezzle, Quadpay and the like.

So debt is mounting, and with interest rates up, more and more people are missing payments. That means more outstanding debts being passed on to collection, giving the industry a chance to sprinkle some AI onto the age-old process of prodding, coaxing, and pressuring people to pay up.

For an insight into how this works, we need look no further than the sales copy of companies that make debt collection software. Here, products are described in a mix of generic corp-speak and dystopian portent: SmartAction, another conversational AI product like Skit, has a debt collection offering that claims to help with “alleviating the negative feelings customers might experience with a human during an uncomfortable process”—because they’ll surely be more comfortable trying to negotiate payments with a robot instead. 

Meanwhile, Latitude ”resolves gaps in functionality while reducing the pressure on your agents and increasing recovery rates”; Katabat provides ”full omni-channel orchestration, true machine learning” and a “powerful collection strategy engine”; and TrueAccord runs an ”industry-leading recovery and collections platform powered by machine learning and a consumer-friendly digital experience.” TrueAccord also boasts of offering more empathetic debt collections experiences, which naturally are achieved through the hallmarks of compassion: “experimentation in A/B testing consumer research, and machine learning.”