[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117996-en":3,"doc-seo-117996-105":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},117996,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Financial Risk Assessment using Machine Learning Engineering (FRAME) - Scenario based Quantitative Analysis under Uncertainty","Risk management in banking is evolving as operational and strategic uncertainties increase, making measurable handling of uncertainty essential for sustainable financial performance. This paper presents Financial Risk Assessment using Machine Learning Engineering (FRAME), an AI/ML-based framework with two key contributions: ML-driven risk quantification and granularity that enables customized multi-factor analysis across multiple abstraction levels. The approach supports decision makers by mapping risk severity from qualitative origins to quantitative estimates within banking applications, improving assessment clarity and objectivity.","ISSN 2563-7568  \nFinancial Risk Assessment using Machine Learning Engineering (FRAME): Scenario based Quantitative Analysis under Uncertainty  \nKrishna Mohan Kovur1,2*, Medha Gedela2, Arjun M Rao2  \n1 University of Alberta, Edmonton, Canada  \n2Banking Labs Inc, Toronto, Canada  \nAbstract  \nRisk management functions, under uncertainty, in the Banking Industry have been changing and will continue to change with the recent advancements and innovations. Embracing uncertainty and working with measurable risk becomes critical, therefore quantitative risk severity assessment is critical for sustainable financial excellence. In this paper, the authors propose Financial Risk Assessment using Machine Learning Engineering (FRAME) based on artificial intelligence (AI) and machine learning (ML), which has two significant contributions. Firstly, adoption of machine learning models for banking towards risk quantification and secondly, granularity that emphases on customized logic via multi-factor analysis modeling at different levels of abstraction connecting machine learning models. These contributions will help Financial Institutions (Fis) that will gain the most benefits and opportunities. In a nutshell, the framework analysis presented in this paper is intended as a step towards building a framework of risk modeling from qualitative to quantitative, viewed at different levels of abstraction to access risk severity in the banking applications.  \nKey Words: Risk assessment; Quantitative analysis; Granularity; Machine learning; Banking; Analytic Hierarchy Process (AHP)  \n\n| *Corresponding Author: Krishna Mohan Kovur, University of Alberta, Edmonton, Canada; E-mail:\u003Cbr>[Krishnamohan.Kovur@bankinglabs.com](Krishnamohan.Kovur@bankinglabs.com) |  |\n| --- | --- |\n| Received Date: November 28, 2023, Accepted Date: December 12, 2023, Published Date: December 15, 2023 |  |\n| Citation: Kovur KM, Gedela Medha, Rao AM. Financial Risk Assessment using Machine Learning Engineering (FRAME): Scenario based Quantitative Analysis under Uncertainty. Int J Auto AI Mach Learn. 2023;3(1):1-13 . |  |\n|  | This open-access article is distributed under the terms of the Creative Commons Attribution Non-Commercial License (CC BY-NC) ([http://creativecommons.org/licenses/by-nc/4.0/](http://creativecommons.org/licenses/by-nc/4.0/)), which permits reuse, distribution and reproduction of the article, provided that the original work is properly cited, and the reuse is restricted to non-commercial purposes. |\n\n1  \nInt J Auto AI Mach Learn, Vol 3, Issue 1, December 2023  \nISSN 2563-7568  \n1. Introduction  \nIn recent banking trends, operational and strategic uncertainties have significantly increased for number reasons including failing to understand high risk scenarios under uncertainty and an ever-changing organizational dynamic that makes it difficult for control mechanisms aligning the speed and agility. To address new risks emerging with changing business requirements under uncertainty, the paper introduces a new quantitative risk management framework in banking via machine learning in conjunction with multi-factor analysis. It is therefore necessary to develop a risk management model that prioritizes uncertainties and quantifies their degree. Our paper introduces the concept of granularity (or granularity) for understanding the depth of uncertainty through related risk severities and their associated risk influencing variables. As such, to provide a detailed analysis of the application problem via granularity (varying levels of abstraction), we need a robust methodology for risk quantification for performing a detailed analysis. In our proposed risk model framework, risk severity is quantified at different levels of abstraction, facilitating decision makers' estimationsand evaluations of the concerned application decision problem. The purpose of this paper is therefore to the first multi-criteria (multi-factored) based risk framework that has been applied to risk mana","cbCaioIPO4vvhtIx","https://ap.wps.com/l/cbCaioIPO4vvhtIx","pdf",954062,1,13,"English","en",105,"# Introduction\n## Quantitative risk management framework and granularity\n# Background\n## Banking risk management","[{\"question\":\"What problem does FRAME address in banking risk management?\",\"answer\":\"FRAME targets risk management under uncertainty by emphasizing measurable severity assessment and understanding high-risk scenarios where uncertainty and organizational dynamics complicate control alignment.\"},{\"question\":\"What are the two main contributions of FRAME?\",\"answer\":\"First, the framework adopts machine learning models to quantify risk. Second, it introduces granularity to customize logic through multi-factor modeling across different abstraction levels.\"},{\"question\":\"How does granularity support decision-making in the framework?\",\"answer\":\"Granularity quantifies risk severity at multiple levels of abstraction, allowing decision makers to estimate and evaluate application decision problems and compare influencing factors.\"}]","Financial Risk Assessment using Machine Learning Engineering (FRAME) - Scenario based Quantitative Analysis under Uncertainty | PDF",1785680684,33,{"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},"financial-risk-assessment-using-machine-learning-engineering-frame-scenario-based-quantitative-analysis-under-uncertainty","",{"@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/financial-risk-assessment-using-machine-learning-engineering-frame-scenario-based-quantitative-analysis-under-uncertainty/117996/",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},"What problem does FRAME address in banking risk management?","Question",{"text":75,"@type":76},"FRAME targets risk management under uncertainty by emphasizing measurable severity assessment and understanding high-risk scenarios where uncertainty and organizational dynamics complicate control alignment.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What are the two main contributions of FRAME?",{"text":80,"@type":76},"First, the framework adopts machine learning models to quantify risk. 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