Improving credit risk assessment in P2P lending with explainable machine learning survival analysis

Gero Friedrich Bone-Winkel and Felix Reichenbach ()
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Gero Friedrich Bone-Winkel: Technische Universität Berlin
Felix Reichenbach: Technische Universität Berlin

Digital Finance, 2024, vol. 6, issue 3, No 6, 542 pages

Abstract: Abstract Recent research using explainable machine learning survival analysis demonstrated its ability to identify new risk factors in the medical field. In this study, we adapted this methodology to credit risk assessment. We used a comprehensive dataset from the Estonian P2P lending platform Bondora, consisting of over 350,000 loans and 112 features with a loan volume of 915 million euros. First, we applied classical (linear) and machine learning (extreme gradient-boosted) Cox models to estimate the risk of these loans and then risk-rated them using risk stratification. For each rating category we calculated default rates, rates of return, and plotted Kaplan–Meier curves. These performance criteria revealed that the boosted Cox model outperformed both the classical Cox model and the platform’s rating. For instance, the boosted model’s highest rating category had an annual excess return of 18% and a lower default rate compared to the platform’s best rating. Second, we explained the machine learning model’s output using Shapley Additive Explanations. This analysis revealed novel nonlinear relationships (e.g., higher risk for borrowers over age 55) and interaction effects (e.g., between age and housing situation) that provide promising avenues for future research. The machine-learning model also found feature contributions aligning with existing research, such as lower default risk associated with older borrowers, females, individuals with mortgages, or those with higher education. Overall, our results reveal that explainable machine learning survival analysis excels at risk rating, profit scoring, and risk factor analysis, facilitating more precise and transparent credit risk assessments.

Keywords: P2P lending; Explainable AI; Cox model; Credit risk; SHAP; Survival analysis (search for similar items in EconPapers)
JEL-codes: G10 G21 G32 G33 G51 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s42521-024-00114-3

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