[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118528-en":3,"doc-seo-118528-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118528,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Improving credit risk assessment in P2P lending with explainable machine learning survival analysis","Explainable machine learning survival analysis is adapted to credit risk assessment to identify drivers of default timing and to produce transparent risk ratings. Using a comprehensive dataset from the Estonian P2P platform Bondora (over 350,000 loans, 112 features, €915 million total volume), the study estimates loan risk with classical and boosted Cox models, then evaluates risk-stratified categories via default rates, returns, and Kaplan–Meier curves. The boosted model outperforms classical Cox and the platform’s ratings. Shapley Additive Explanations reveal nonlinear effects and interactions, offering interpretable risk-factor insights for more precise, transparent lending decisions.","Bone-Winkel, Gero Friedrich; Reichenbach, Felix  \nArticle — Published Version  \nImproving credit risk assessment in P2P lending with explainable machine learning survival analysis  \nDigital Finance  \nProvided in Cooperation with:  \nSpringer Nature  \nSuggested Citation: Bone-Winkel, Gero Friedrich; Reichenbach, Felix (2024) : Improving credit risk assessment in P2P lending with explainable machine learning survival analysis, Digital Finance, ISSN 2524-6186, Springer International Publishing, Cham, Vol. 6, Iss. 3, pp. 501-542, [https://doi.org/10.1007/s42521-024-001](https://doi.org/10.1007/s42521-024-001)14-3  \nThis Version is available at:  \n[https://hdl.handle.net/10419/316976](https://hdl.handle.net/10419/316976)  \nStandard-Nutzungsbedingungen:  \nDie Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.  \nSie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.  \nSofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte.  \nTerms of use:  \nDocuments in EconStor maybe saved and copied foryour personal and scholarly purposes.  \nYou are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public.  \nIf the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence.  \n[http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)  \nDigital Finance (2024) 6:501–542  \n[https://doi.org/10.1007/s42521-024-001](https://doi.org/10.1007/s42521-024-001)14-3  \nORIGINAL ARTICLE  \nImproving credit risk assessment in P2P lending with explainable machine learning survival analysis  \nGero Friedrich Bone‑Winkel1 · Felix Reichenbach2  \nReceived: 4 March 2024 / Accepted: 17 May 2024 / Published online: 12 June 2024 © The Author(s) 2024  \nAbstract  \nRecent 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% anda 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 cred","cbCaijsIEr0qAWYz","https://ap.wps.com/l/cbCaijsIEr0qAWYz","pdf",7463821,1,43,"English","en",105,"# Abstract\n# Introduction\n# Methodology\n## Data and study design\n## Cox model estimation and risk stratification\n## Kaplan–Meier curves and performance metrics\n## Explainability with Shapley Additive Explanations (SHAP)\n# Results\n## Model comparison and risk-rating performance\n## Interpreted risk factors and interactions\n# Conclusion","[{\"question\":\"What data and lending platform are used in the study?\",\"answer\":\"The study uses a dataset from the Estonian P2P lending platform Bondora, containing over 350,000 loans, 112 features, and a total loan volume of about €915 million.\"},{\"question\":\"Which modeling approaches are compared for credit risk assessment?\",\"answer\":\"Classical (linear) Cox models and machine learning boosted Cox models are applied to estimate loan risk, followed by risk stratification for rating categories.\"},{\"question\":\"How does the study explain the machine learning model’s outputs?\",\"answer\":\"It applies Shapley Additive Explanations (SHAP) to identify feature contributions and uncover nonlinear relationships and interactions affecting default risk.\"}]","Improving credit risk assessment in P2P lending with explainable machine learning survival analysis | 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data and lending platform are used in the study?","Question",{"text":75,"@type":76},"The study uses a dataset from the Estonian P2P lending platform Bondora, containing over 350,000 loans, 112 features, and a total loan volume of about €915 million.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which modeling approaches are compared for credit risk assessment?",{"text":80,"@type":76},"Classical (linear) Cox models and machine learning boosted Cox models are applied to estimate loan risk, followed by risk stratification for rating categories.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the study explain the machine learning model’s outputs?",{"text":84,"@type":76},"It applies Shapley Additive Explanations (SHAP) to identify feature contributions and uncover nonlinear relationships and interactions affecting default 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