Applicability of Large Corporate Credit Models to Small Business Risk Assessment

TL;DR

This paper investigates whether deep learning-based large corporate credit risk models can be effectively applied to assess small business credit risk, addressing a significant underserved market with limited data.

Contribution

It evaluates the transferability of large corporate credit models to small business risk assessment, highlighting potential adaptations needed for limited data environments.

Findings

Large corporate models show promise but require modifications for small business data.

Deep learning models can improve small business credit risk prediction accuracy.

Potential for expanding credit access for small businesses using adapted models.

Abstract

There is a massive underserved market for small business lending in the US with the Federal Reserve estimating over \$650B in unmet annual financing needs. Assessing the credit risk of a small business is key to making good decisions whether to lend and at what terms. Large corporations have a well-established credit assessment ecosystem, but small businesses suffer from limited publicly available data and few (if any) credit analysts who cover them closely. We explore the applicability of (DL-based) large corporate credit risk models to small business credit rating.