Deep Learning Credit Risk Modeling

Abstract

This article demonstrates how deep learning can be used to price and calibrate models of credit risk. Deep neural networks can learn structural and reduced-form models with high degrees of accuracy. For complex credit risk models with no closed-form solutions available, deep learning offers a conceptually simple and more efficient alternative solution. This article proposes an approach that combines deep learning with the unscented Kalman filter to calibrate credit risk models based on historical data; this strategy attains an in-sample R-squared of 98.5% for the reduced-form model and 95% for the structural model.

TOPICS: Credit risk management, big data/machine learning, quantitative methods, statistical methods

Key Findings

• ▪ Neural networks can approximate solutions to credit risk models, precisely capturing the relationship between model inputs and credit spreads.

• ▪ Compared to standard techniques, the approximate solutions are more computationally efficient.

• ▪ Neural networks can be used to accurately calibrate structural and reduced-form models of credit risk.