Machine learning has quietly revolutionized credit risk management over the past decade but most institutions are only scratching the surface.
While credit scoring was one of the earliest applications of predictive analytics, today's ML capabilities extend far beyond traditional scorecards. From real-time early warning systems that detect portfolio stress months before financial metrics deteriorate, to LLM-powered sentiment analysis that processes thousands of news articles and filings in seconds, the technology is reshaping how banks monitor and manage credit exposure.
In this white paper, Capco's Paris team examines:
- How machine learning evolved from 1970s logistic regression to today's sophisticated ensemble models
- Real-world applications in credit scoring, early warning systems, and risk monitoring
- Two concrete LLM use cases already working in production
- Critical challenges: data quality, interpretability, hallucinations, regulatory compliance
- Practical frameworks for navigating EU regulatory approval
Based on our experience implementing ML solutions with European banks, we share what actually works, and what doesn't when deploying AI in credit risk.
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