MANAGING credit risk today feels like navigating a maze with moving walls. Lenders must account for a dizzying array of financial and nonfinancial factors that include interest rates, consumer behavior, geopolitical shocks and even climate trends while grappling with economic data that often points to conflicting directions.
Add to this the rise of alternative data and artificial intelligence , which promise sharper and granular insights but bring their own complexities. Understanding these challenges, applications and practices is key to seeing how the industry is taming the unknown: how AI and alternative data redefine credit risk. Let us unpack the hurdles in building robust models, how AI and new data are reshaping credit decisions, and what is standard for testing resilience.
A 2022 study made by the Bank Policy Institute about the role of ML and alternative data in expanding access to credit showed that ML models using alternative data cut rejection rates for underserved groups without spiking defaults — a win for financial inclusion. But it is not flawless. AI's 'black box' nature where decisions are hard to explain raises eyebrows, especially with independent model validators and regulators as well.
The Unknown Turning Chaos To Clarity
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