Case Study: Major Bank Improve Risk Profiling boosts lending risk assessment with Unsupervised

Unsupervised Helps Major Bank Improve Risk Profiling

Major Bank Improve Risk Profiling Company, a major American bank, needed a better way to assess lending risk across its large and complex data environment. The bank relied on proprietary models and credit scores, but struggled to analyze information across silos and validate assumptions about how factors like credit changes affected risk.

Unsupervised helped the bank set up within its secure data ecosystem and use its AI platform to blend data across sources and uncover patterns in weeks. With Unsupervised, the bank found that some assumptions around ROAA, Tier 1 Capital Ratios, and loan types did not hold in all segments, helping improve risk modeling and uncover new opportunities to refine lending decisions and competitive advantage.