AI in Lending and Credit Scoring: Opportunities and Risks

1Introduction

In its November 2024 report on the financial stability implications of AI, the Financial Stability Board highlights credit assessment as a key use case. Firms increasingly use machine learning alongside traditional scoring to process large or unstructured data during pre-approval.

2Discussion

This can particularly help applicants with thin or no credit files, while helping lenders predict default risk more accurately.

The FSB also flags four vulnerabilities: third-party dependencies, market correlations when many firms use similar models and data, cyber risk, and model risk and data governance. It calls on authorities to strengthen monitoring and to use AI-powered tools in supervision.

3Conclusion

A practical point: many firms currently prefer simpler, interpretable models and “co-pilot” tools that support rather than replace human decisions.