[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119337-en":3,"doc-seo-119337-105":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},119337,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Evolution of Machine Learning in Financial Risk Management - A Survey","Financial risk management is central to day-to-day financial decisions, reducing exposure while supporting profit maximization. Because risk management relies heavily on data, machine learning offers strong opportunities to improve modeling and decision quality. Over time, ML adoption in this domain has evolved toward greater model complexity and a wider set of solvable tasks. This survey organizes the field in three steps: risk taxonomy and method foundations, real-world applications across representative approaches, and a synthesis of current challenges with directions for further improvement.","Evolution of machine learning in financial risk management: A survey  \nKuan-ILu  \nActuarial Science, Department of Applied Probability and Statistics, 93117 University of California, Santa Barbara, United States  \nAbstract. Financial risk management plays a crucial role in daily financial decision-making, aiming to mitigate risk and maximize profit. Given its reliance on data, financial risk management can greatly benefit from the application of machine learning tools. Over the years, we've observed a clear trend in the evolution of these applications, marked by increasing model complexity and a broader range of manageable tasks. This paper contributes to the field in three key dimensions: First, we provide a clear taxonomy of risks and an introduction to relevant machine learning methods to establish a foundation and identify the targeted issues. Next, we explore real-world data applications, discussing the pros and cons of three methods, from the earliest to the most recent. Finally, based on the observed results, we highlight current challenges and limitations and propose potential directions for improvement.  \n1 Introduction  \nMachine Learning (ML) has long played a crucial role in the era of artificial intelligence. From prediction tasks to signal and image processing, and more recently to breakthroughs in natural language processing, the impact and potential of ML have become increasingly evident. Its applications in Financial Risk Management (FRM) are no exception, with its effectiveness well-validated [1] . Whether forecasting future Value at Risk (VaR) trends based on historical data, predicting customer default rates, or managing financial portfolios, ML proves especially valuable in the financial sector, where data is abundant and the need for improved performance drives the adoption of data-driven approaches.  \nMany previous surveys have offered comprehensive and unified overviews of ML applications in FRM tasks [2, 3] . However, this paper approaches the issue from a different angle and aims to provide an overview of the evolution of applications in a chronological order. We illustrate the progression from simply presuming a time series model and then refine it with Bayesian inference, to using more complicated structures like neural networks to mitigate the inherent complexity of financial data, to elevating the task form metric prediction to decision making with the use of reinforcement learning (RL) . Given the highstakes nature of FRM, even small mistakes or false assumptions can result in significant  \nCorresponding author: [1k_lu@ucsb.edu](1k_lu@ucsb.edu)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nfinancial losses. Consequently, stakeholders often prefer explainable models, which allow for clearer interpretation of the model's logic, as discussed in [4] . However, despite this preference for simpler, transparent methods, more complex black-box models, which have demonstrated significantly greater effectiveness, seem inevitable for accurate predictions and the full utilization of ML in FRM. This paper explores this transformation and supports it with evidence from related works.  \nIn this paper, we begin by breaking down the broader term \"Financial Risk Management\"into several classifications. This taxonomy helps clarify the purpose of each ML model and assess the versatility of each method based on the types of risk it can address. By using this taxonomy, we can better define the scope of the problem and more easily understand the characteristics of the targeted task. Additionally, we can share knowledge directly with those facing the same types of risks. Next, we provide a brief background on the ML methods discussed in this overview, ranging from the simplest Bayesian Inference to the more complex Neural","cbCaicg9b3BsU0nz","https://ap.wps.com/l/cbCaicg9b3BsU0nz","pdf",461336,1,11,"English","en",105,"# Introduction\n## Financial Risk Management and Machine Learning\n## Purpose and Organization of the Survey\n# Classifications of financial risks and ML methods\n## Financial Risks\n## ML methods overview","[{\"question\":\"How does machine learning support financial risk management in practical decision-making?\",\"answer\":\"Machine learning leverages abundant financial data to improve forecasting, default prediction, and portfolio assessment, helping organizations achieve better performance in risk-related tasks.\"},{\"question\":\"What structure does the survey use to present the evolution of ML in financial risk management?\",\"answer\":\"It presents the topic chronologically, moving from time-series modeling with Bayesian inference to more complex structures like neural networks, and then to reinforcement learning that shifts from value prediction toward decision-making.\"},{\"question\":\"Why does the survey emphasize explainability and what tension does it highlight?\",\"answer\":\"Stakeholders often prefer explainable models for clearer interpretation, yet black-box models can deliver substantially better predictive effectiveness. The survey examines this trade-off and supports it with evidence from related works.\"}]","Evolution of Machine Learning in Financial Risk Management - A Survey | PDF",1785723772,28,{"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},"evolution-of-machine-learning-in-financial-risk-management-a-survey","",{"@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/evolution-of-machine-learning-in-financial-risk-management-a-survey/119337/",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","2026-08-03",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},"How does machine learning support financial risk management in practical decision-making?","Question",{"text":75,"@type":76},"Machine learning leverages abundant financial data to improve forecasting, default prediction, and portfolio assessment, helping organizations achieve better performance in risk-related tasks.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What structure does the survey use to present the evolution of ML in financial risk management?",{"text":80,"@type":76},"It presents the topic chronologically, moving from time-series modeling with Bayesian inference to more complex structures like neural networks, and then to reinforcement learning that shifts from value prediction toward decision-making.",{"name":82,"@type":73,"acceptedAnswer":83},"Why does the survey emphasize explainability and what tension does it highlight?",{"text":84,"@type":76},"Stakeholders often prefer explainable models for clearer interpretation, yet black-box models can deliver substantially better predictive effectiveness. 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