[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121239-en":3,"doc-seo-121239-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},121239,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","A Review of Machine Learning in Banking Risk Management and Possible Research Topics","The review examines how machine learning has been studied within banking risk management and identifies opportunities for future research. It contextualizes recent regulatory changes after the 2008 global financial crisis, explains the role of banking risk management in reducing adverse outcomes, and outlines major bank risk categories such as credit, market, and operational risks. Using analysis of bank reports, it proposes a risk taxonomy and assesses the specific domains where AI/ML methods are applied, highlighting gaps and challenges.","A REVIEW OF MACHINE LEARNING IN BANKING RISK MANAGEMENT AND POSSIBLE RESEARCH  \nTOPICS  \nAnnotation: The 2008 global financial crisis brought to light the fundamental  \nsignificance of bank risk management (GFC). The primary cause of the economic and financial disaster that ensued after the Great Financial Crisis was banks' utter disdain for risk management in the years preceding 2008. Bank culture and structure have changed as a result of the substantial regulatory measures that have since been put in place to address the flaws and deficiencies that were exposed in the financial services industry. The purpose of this article is to examine the degree to which machine learning has been studied in relation to risk management in the banking industry and to suggest possible directions for future research. It ranks bank-specific risks according to an analysis of bank reports and assesses The domains of risk management in banking where machine learning principles have been used.  \nKeywords: Banks, Risk managing, Credit, Cards, Artificial Intelligence.  \nInformation about the authors  \nAliA. Alaidany  \nFuel and Energy Techniques Engineering Department, Shatt Al-Arab University College, Basra, Iraq  \nAli K. Mattar  \nDepartment of Computer Science, Shatt Al-Arab University College, Basra, Iraq  \nHaider A.khudair  \nComputer Engineering Technology Department,Shatt Al-Arab University College, Basra, Iraq  \nMarwah M. Mahdi  \nPhD Candidate, Department of Computer Eng., Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran  \nTibah Firas  \n1. Introduction  \nTo gauge the level of rivalry, central banks frequently employ concentration indices like the Herfindahl-Hirschman index and the concentration ratio. These indexes are usually computed by the National Bank of Serbia using the absolute value of assets. The values ofthe Rosenblatt index, entropy coefficient, Gini coefficient, and Lorenz curve graph from 2015 to 2019 are also included in this study. Significant regulatory reforms in the financial services sector resulted from the 2008 global financial crisis, which brought attention to the significance of bank risk management. The degree of machine learning research in banking risk management is examined in this article. It highlights unexplored topics or challenges, studies, examines, and assesses machine-learning techniques employed in this field, and offers suggestions for additional study. Instead than depending on the body of existing literature, the study offers a risk taxonomy that was created by surveying bank annual reports. Additionally, it evaluates the fields in which machine learning techniques have been studied, banking  \napplications of AI and machine learning, and a developing trend in other economic sectors. Although banking risk management has grown significantly in the last few decades, more developments are urgently needed. Risk managers are essential in today's financial sector, and there are aspirations to improve banking risk management procedures by utilizing machine learning and artificial intelligence.  \nThe use of data learning, machine learning, and artificial intelligence in banking risk management is growing. These technologies have been widely used in credit risk for many years, and credit scoring frequently uses complex classification algorithms like Bayesian classifiers and logistic regression. These techniques are also essential for monitoring, stress testing, and predicting credit risk metrics like PD and LGD. The IFRS 9 projected credit losses impairment model is one area where AI and ML are being used in this industry.  \nRisk Management at Banks  \nThe process by which a bank determines, assesses, and takes action to reduce the likelihood that a negative outcome would result from its operational or investment choices is known as banking risk management.  \nThe bank's management is taking on greater risk in an attempt to increase its owners' profits. Among the risks that banks face include Risks ","cbCaiagZxg0x8WSN","https://ap.wps.com/l/cbCaiagZxg0x8WSN","pdf",735072,1,"English","en",105,"# Introduction\n## Banking risk management concepts\n## Bank risk categories and regulation\n## Machine learning and AI in credit risk\n# Risk Management at Banks\n## Economic vs regulatory capital\n## Credit, market, and operational risks\n## Capital calculation frameworks (Basel)","[{\"question\":\"Why is banking risk management especially important after the 2008 global financial crisis?\",\"answer\":\"The crisis revealed major deficiencies in how banks handled risk before 2008. Subsequent regulatory reforms were introduced to address exposed flaws and improve bank risk management practices.\"},{\"question\":\"Which bank risk categories does the review focus on?\",\"answer\":\"The review discusses interest-rate and market risks, credit risk, off-balance-sheet and technology-related risks, foreign exchange and sovereign risks, liquidity risk, and bankruptcy risk, emphasizing that credit risk typically requires the most capital.\"},{\"question\":\"How has machine learning been used in banking risk management according to the review?\",\"answer\":\"Machine learning principles are applied in areas such as credit scoring and monitoring, including stress testing and predicting credit-risk metrics like PD and LGD, as well as IFRS 9 expected credit losses impairment models.\"}]","A Review of Machine Learning in Banking Risk Management and Possible Research Topics | PDF",1785734505,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"a-review-of-machine-learning-in-banking-risk-management-and-possible-research-topics","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/a-review-of-machine-learning-in-banking-risk-management-and-possible-research-topics/121239/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why is banking risk management especially important after the 2008 global financial crisis?","Question",{"text":74,"@type":75},"The crisis revealed major deficiencies in how banks handled risk before 2008. Subsequent regulatory reforms were introduced to address exposed flaws and improve bank risk management practices.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which bank risk categories does the review focus on?",{"text":79,"@type":75},"The review discusses interest-rate and market risks, credit risk, off-balance-sheet and technology-related risks, foreign exchange and sovereign risks, liquidity risk, and bankruptcy risk, emphasizing that credit risk typically requires the most capital.",{"name":81,"@type":72,"acceptedAnswer":82},"How has machine learning been used in banking risk management according to the review?",{"text":83,"@type":75},"Machine learning principles are applied in areas such as credit scoring and monitoring, including stress testing and predicting credit-risk metrics like PD and LGD, as well as IFRS 9 expected credit losses impairment models.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]