Deep learning for survival and competing risk modelling

Abstract

The article examines novel machine learning techniques for survival analysis in a credit risk modelling context. Using a large dataset of US mortgages, we evaluate the adequacy of DeepHit, a deep learning-based competing risk model, and random survival forests. The observed results provide strong evidence that both models predict default and prepayment risk more accurately than statistical benchmarks in the form of the Cox proportional hazard model and the Fine and Gray model. The superiority of the machine learning models is robust across different periods including stressed periods. We also find machine learning models do not require larger amounts of training data than the statistical benchmarks. Finally, we extend methods for estimating feature importance scores to deep neural networks for survival analysis and clarify which covariates determine the estimated survival functions of DeepHit. An online companion with additional results is available in Supplementary Information.

Keywords:

• survival analysis
• competing risk model
• deep learning
• mortgage risk

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 Source: http://www.freddiemac.com/research/datasets/sf_loanlevel_dataset.page.

Additional information

Funding

Funded by Deutsche Forschungsgemeinschaft in thescope of IRTG 1792.