CFPB’ s Concerns With Black-Box Credit Models
The Consumer Financial Protection Bureau( CFPB) issued a circular on May 26, 2022, detailing their stance on black-box credit models. Although these models are fast, effective and usually helpful, the models may also contain unseen biases or other discriminatory tendencies due to the way data is mined and the history of bias in the American financial world.
“ Federal consumer financial protection laws and adverse action requirements should be enforced, regardless of the technology used by creditors, and that creditors cannot justify noncompliance with the Equal Credit Opportunity Act( ECOA) based on the mere fact that the technology they use to evaluate credit applications is too complicated, too opaque in its decision-making, or too new.” CFPB
In recent years, the explosion of digital financial services has been a double-edged sword for both consumers and lenders. The sudden influx of new customers as online banking gained traction meant financial institutions everywhere needed a fast, reliable and theoretically sound model to get customers through intake procedures and into their systems. But how was that to be done with so many consumers from so many areas, all wanting something different?
The Answer: Black-Box Credit Models and Algorithms
Black-box credit models were designed to be effective and efficient, by taking enormous amounts of consumer data and history, and basing decisions off the predictive analysis of that data. Given algorithms are designed to make decisions faster and better than human beings, financial institutions faced with situations where a lot of data needed to be sorted through resorted to complex algorithms.
Typically speaking, a sufficient and adequate machine learning system will take as much data as it can, start making predications or analyses, and then will
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