Checking account activity and credit default risk of enterprises: An application of statistical learning methods

TL;DR

This study demonstrates that transactional account activity data can effectively predict corporate default risk, outperforming traditional financial ratios, and highlights the importance of liquidity and real-time cash flow in default prediction.

Contribution

The paper introduces the use of transactional account activity data combined with advanced machine learning methods to improve default risk prediction for enterprises.

Findings

Transactional data outperforms financial ratios in default prediction

Merged data improves prediction accuracy over individual datasets

Machine learning methods reveal key factors like credit violations and cash flow

Abstract

The existence of asymmetric information has always been a major concern for financial institutions. Financial intermediaries such as commercial banks need to study the quality of potential borrowers in order to make their decision on corporate loans. Classical methods model the default probability by financial ratios using the logistic regression. As one of the major commercial banks in France, we have access to the the account activities of corporate clients. We show that this transactional data outperforms classical financial ratios in predicting the default event. As the new data reflects the real time status of cash flow, this result confirms our intuition that liquidity plays an important role in the phenomenon of default. Moreover, the two data sets are supplementary to each other to a certain extent: the merged data has a better prediction power than each individual data. We have adopted some advanced machine learning methods and analyzed their characteristics. The correct use of these methods helps us to acquire a deeper understanding of the role of central factors in the phenomenon of default, such as credit line violations and cash inflows.