[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118202-en":3,"doc-seo-118202-105":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},118202,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine Learning in Banking Risk Management - Mapping a Decade of Evolution","Banks' risk management evolves continuously in a dynamic market environment, making timely adaptation essential. Traditional techniques face limitations that motivate innovative methods to manage multiple risk categories. This systematic literature review analyzes how machine learning algorithms are used to predict, assess, and mitigate market, operational, liquidity, and other risks. The review covers recent studies and shows an expanding role of machine learning in strengthening risk management strategies, while noting that coverage is stronger for market and operational risk than for liquidity and other risks.","Master Degree Program in Information Management  \nMachine Learning in Banking Risk Management: Mapping a  \nDecade of Evolution  \nValentin Lennart Heß  \nMaster Thesis  \npresented as partial requirement for obtaining the Master Degree in Information Management  \nNOVA Information Management School Instituto Superior de Estatística e Gestão de Informação  \nUniversidade Nova de Lisboa  \nNOVA Information Management School Instituto Superior de Estatística e Gestão de Informação  \nUniversidade Nova de Lisboa  \nMachine Learning in Banking Risk Management: Mapping a Decade of Evolution  \nby  \nValentin Lennart Heß  \nMaster Thesis presented as partial requirement for obtaining the Master’s degree in Information Management, with a specialization in Knowledge Management and Business  \nIntelligence  \nSupervised by  \nBruno Miguel Pinto Damásio, PhD, NOVA Information Management School  \nJanuary, 2024  \nSTATEMENT OF INTEGRITY  \nI hereby declare having conducted this academic work with integrity. I confirm that I have not used plagiarism or any form of undue use of information or falsification of results along the process leading to its elaboration. I further declare that I have fully acknowledged the Rules of Conduct and Code of Honor from the NOVA Information Management School.  \nValentin Heß, Germany, 30th of January 2024  \nABSTRACT  \nBanks' risk management is constantly changing in a dynamic market environment. It is, therefore, necessary to respond appropriately to these changes. Innovative approaches are needed to overcome the limitations of traditional methods. Machine learning algorithms are suitable for dealing with the various risk types banks face. A significant amount of academic literature focuses on applying machine learning in credit risk management. This systematic literature review addresses market, operational, liquidity, and other risk types, analyzing the utilization of machine learning algorithms to predict, assess, and mitigate these risks. Examining a wide range of recent studies, this review highlights the expanding role of machine learning in enhancing risk management strategies. The review has revealed that machine learning is adequately covered in the context of market and operational risk. In particular, the learning ability and predictive capabilities of artificial neural networks and other algorithms are promising for risk management. However, only a few studies address liquidity and other risks that can be treated by applying machine learning algorithms. The findings of this review highlight opportunities, challenges, and further research directions of machine learning to strengthen risk management in banks.  \nKEYWORDS  \nMachine Learning; Bank; Risk Management; Algorithm; Artificial Intelligence  \nSustainable Development Goals (SDG):  \nTABLE OF CONTENTS  \n1. Introduction.................................................................................................................. 1  \n2. Theoretical Background................................................................................................3  \n2.1. Risk Management Foundations.............................................................................3  \n2.1.1. Risk Types .......................................................................................................4  \n2.2. Machine Learning Principles .................................................................................6  \n2.2.1. Models ............................................................................................................7  \n2.3. Study Relevance .................................................................................................. 11  \n3. Methodology .............................................................................................................. 12  \n3.1. Search Process..................................................................................................... 12  \n3.2. Eligibility Criteria.................................","cbCaifCukanrSrb3","https://ap.wps.com/l/cbCaifCukanrSrb3","pdf",1712442,1,48,"English","en",105,"# Introduction\n# Theoretical Background\n## Risk Management Foundations\n## Machine Learning Principles\n# Methodology\n## Search Process\n## Eligibility Criteria\n# Results\n## Quantitative Analysis\n## Qualitative Analysis\n## Market Risk\n## Operational Risk\n## Liquidity Risk\n## Other Risk Types\n# Discussion\n## Literature Synthesis\n## Practical Implications\n## Limitations\n## Further Research\n# Conclusion","[{\"question\":\"Why is updating bank risk management necessary in a dynamic market environment?\",\"answer\":\"Bank risk management must adapt continuously because market conditions and risk factors change over time, requiring appropriate responses to maintain effectiveness.\"},{\"question\":\"Which risk types does the systematic literature review examine in relation to machine learning?\",\"answer\":\"The review analyzes market, operational, liquidity, and other risk types, focusing on how machine learning supports prediction, assessment, and mitigation.\"},{\"question\":\"What do the review findings suggest about the coverage of machine learning across risk types?\",\"answer\":\"Machine learning is well covered for market and operational risk, while only a few studies address liquidity and other risks using machine learning approaches.\"}]","Machine Learning in Banking Risk Management - Mapping a Decade of Evolution | PDF",1785682143,121,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-in-banking-risk-management-mapping-a-decade-of-evolution","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-in-banking-risk-management-mapping-a-decade-of-evolution/118202/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is updating bank risk management necessary in a dynamic market environment?","Question",{"text":75,"@type":76},"Bank risk management must adapt continuously because market conditions and risk factors change over time, requiring appropriate responses to maintain effectiveness.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which risk types does the systematic literature review examine in relation to machine learning?",{"text":80,"@type":76},"The review analyzes market, operational, liquidity, and other risk types, focusing on how machine learning supports prediction, assessment, and mitigation.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the review findings suggest about the coverage of machine learning across risk types?",{"text":84,"@type":76},"Machine learning is well covered for market and operational risk, while only a few studies address liquidity and other risks using machine learning approaches.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]