Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/316976 
Year of Publication: 
2024
Citation: 
[Journal:] Digital Finance [ISSN:] 2524-6186 [Volume:] 6 [Issue:] 3 [Publisher:] Springer International Publishing [Place:] Cham [Year:] 2024 [Pages:] 501-542
Publisher: 
Springer International Publishing, Cham
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.
Subjects: 
P2P lending
Explainable AI
Cox model
Credit risk
SHAP
Survival analysis
Persistent Identifier of the first edition: 
Additional Information: 
G10;G21;G32;G33;G51
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version
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