Leveraging Language Models for Credit Risk
Using BERT to extract risk signals from P2P loan descriptions
This research introduces a novel approach to credit risk assessment in peer-to-peer lending by analyzing borrowers' loan descriptions through Large Language Models.
- Creates a text-based risk indicator from loan descriptions to reduce information asymmetry
- Utilizes BERT to capture contextual nuances in borrower narratives
- Enhances traditional credit scoring models with natural language insights
- Demonstrates how AI can improve financial security through better default prediction
By transforming unstructured text into actionable risk metrics, this approach offers lenders additional tools to evaluate creditworthiness beyond traditional numerical indicators, ultimately strengthening security in P2P lending markets.