[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-290500-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-290500-en":131},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","sustainability-related-risks-in-credit-risk-modeling-evidence-limitations-and-the-role-of-machine-learning-290500","Sustainability Related Risks in Credit Risk Modeling - Evidence, Limitations, and the Role of Machine Learning","","Climate change and broader sustainability pressures increasingly affect firms’ solvency, asset values, and credit loss distributions, yet their integration into credit risk modeling remains fragmented. This paper evaluates how sustainability-related factors are incorporated into credit risk assessment and whether machine learning can meaningfully support that integration. It performs two parallel PRISMA-based systematic literature reviews covering empirical sustainability evidence and machine-learning applications, inputs, approaches, and limitations.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/sustainability-related-risks-in-credit-risk-modeling-evidence-limitations-and-the-role-of-machine-learning-290500/290500/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/sustainability-related-risks-in-credit-risk-modeling-evidence-limitations-and-the-role-of-machine-learning-290500/290500.png","ImageObject",300,407,{"name":42,"@type":43},"Noah","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-29","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",14,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"How does the paper assess sustainability integration in credit risk modeling?","Question",{"text":63,"@type":64},"It uses two parallel PRISMA-guided systematic literature reviews: one synthesizing empirical evidence on sustainability factors and credit risk metrics, and another reviewing machine learning applications in credit risk with reported limitations.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"Which sustainability signals show the most consistent relationships with credit risk?",{"text":68,"@type":64},"Social and governance indicators show the most consistent relationships, while environmental indicators exhibit timing- and sector-dependent effects.",{"name":70,"@type":61,"acceptedAnswer":71},"What key limitations do the paper identify for conventional credit models when adding climate and sustainability risks?",{"text":72,"@type":64},"Conventional models rely heavily on historically calibrated, stable relationships, while sustainability and climate risks act through forward-looking, rare but severe events, strong spatial and network correlations, and data often missing from standard financial datasets.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},290500,1789641850,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,120,123,127],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":112,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":113,"slug":114},8,30,"research-report",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":25,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":25,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":25,"category_name":129,"show_sort_weight":97,"slug":130},19,"General","general",{"code":4,"msg":82,"data":132},{"doc_id":79,"user_id":133,"nickname":42,"user_avatar":134,"doc_module":4,"category_id":112,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":140,"language":141,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":12,"update_tm":80,"read_time":145,"preview_page_count":4},8796095462418,"https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780","Sustainability Related Risks in Credit Risk Modeling: Evidence, Limitations, and the Role of Machine Learning  \nManuele Massei∗ Wouter van Heeswijk∗ Xiaohong Huang∗ Laura Spierdijk∗  \nAbstract  \nClimate change and broader sustainability pressures increasingly affect firms’ solvency, asset values, and credit loss distributions, yet their integration into credit risk modeling remains fragmented. This paper examines how sustainability-related factors are incorporated into credit risk assessment and evaluates whether machine learning methods can meaningfully support this integration. We conduct two parallel systematic literature reviews following PRISMA guidelines. The first synthesizes empirical evidence on the effects of environmental, social and governance information, transition risk, and physical climate risk on credit risk metrics. The second reviews machine learning applications in credit risk, focusing on data inputs, modeling approaches and reported limitations. Across the sustainabilityoriented literature, sustainability factors are empirically associated with credit risk outcomes, but effects are heterogeneous across risk channels, sectors, time horizons, and measurement choices. Social and governance indicators show the most consistent relationships with credit risk, while environmental indicators exhibit timing-and sector-dependent effects. Transition and physical climate risks introduce strong scenario dependence, spatial concentration, and forward-looking dynamics that challenge historically calibrated credit models. In parallel, the machine learning literature demonstrates substantial advances in modeling non-linearities, relational dependencies, and unstructured data, but largely abstracts from sustainability and climate-related exposures and remains predominantly backward-looking. By jointly interpreting both literatures, the paper identifies four structural gaps limiting sustainable credit risk modeling and assesses the extent to which machine learning can mitigate them. The findings suggest that machine learning can enhance data integration and relational modeling, while relying on strong data foundations, explicit scenario design, and robust model governance.  \nKeywords: Credit Risk, Sustainability, Climate Risk, Machine Learning, Systematic Review  \n1 Introduction  \nClimate change and broader sustainability pressures increasingly affect firms’ solvency, collateral values, and long term financial resilience. Rising temperatures, more frequent extreme events, and intensified regulatory and social scrutiny modify firms’ operating environments and alter the distribution of credit losses across borrowers and portfolios.s. Supervisory authorities now explicitly recognize these links. The Basel Committee on Banking Supervision requires  \n∗ University of Twente, Faculty of Behavioral, Management and Social Sciences, The Netherlands. Corresponding author email: [manuele.massei@utwente.nl](manuele.massei@utwente.nl)  \nbanks to identify and manage climate related drivers within their credit risk frameworks (Basel Committee on Banking Supervision, 2022) . The European Banking Authority calls for the incorporation of environmental, social and governance risks beyond standard planning horizons (European Banking Authority, 2021b), while the European Central Bank documents persistent shortcomings in banks’ ability to quantify physical and transition risks due to data and modeling limitations (European Central Bank, 2020) . These developments establish sustainability and climate risks as material components of credit risk assessment.  \nDespite this regulatory recognition, integrating sustainability into credit risk modeling remains methodologically challenging. Conventional credit risk models were developed in settings where historical default behavior, conditionally independent exposures, and stable macro financial relationships provided a reliable basis for inference. While correlation and stress testing have long been reco","cbCaidjGuBq1NXTp","https://ap.wps.com/l/cbCaidjGuBq1NXTp","pdf",2862597,47,"English","# Abstract\n# 1 Introduction\n## Regulatory recognition of climate links\n## Methodological challenges for integrating sustainability\n## Growth of non-financial data and machine learning opportunities","[{\"question\":\"How does the paper assess sustainability integration in credit risk modeling?\",\"answer\":\"It uses two parallel PRISMA-guided systematic literature reviews: one synthesizing empirical evidence on sustainability factors and credit risk metrics, and another reviewing machine learning applications in credit risk with reported limitations.\"},{\"question\":\"Which sustainability signals show the most consistent relationships with credit risk?\",\"answer\":\"Social and governance indicators show the most consistent relationships, while environmental indicators exhibit timing- and sector-dependent effects.\"},{\"question\":\"What key limitations do the paper identify for conventional credit models when adding climate and sustainability risks?\",\"answer\":\"Conventional models rely heavily on historically calibrated, stable relationships, while sustainability and climate risks act through forward-looking, rare but severe events, strong spatial and network correlations, and data often missing from standard financial datasets.\"}]","Sustainability Related Risks in Credit Risk Modeling - Evidence, Limitations, and the Role of Machine Learning | PDF",118]