The new, high-performance models allow banks to define lending (and capital) parameters more precisely and thus sharpen their ability to approve creditworthy customers and reject proposals from customers who either are not creditworthy or cannot afford further debt. Banks (and fintech companies) that have put such new models in place have already increased revenue, reduced credit-loss rates, and made significant efficiency gains thanks to more precise and automated decisioning. While traditional models have struggled to handle the changing customer circumstances, forcing banks to resort to Band-Aid solutions (for example, expert adjustments of default rates at portfolio-segment levels).
Four best practices when designing new or upgrading existing credit-decisioning models include: implementing modular architecture, expanding data sources, mining data for credit signals, and leveraging business expertise. More sophisticated and automated credit-decisioning models that can incorporate a wide variety of traditional and nontraditional data from inside and outside the organization are necessary as banks continue to digitize their enterprises.
This article was originally published on McKinsey.com on December 2, 2021 and is reprinted here by permission.
