[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123167-en":3,"doc-seo-123167-105":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},123167,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Data-driven credit risk monitoring - Leveraging machine learning in risk management","This review traces the evolution of credit risk monitoring from traditional qualitative judgments by loan officers to the adoption of machine learning and big data analytics. It explains how ML enables the detection of subtle, hard-to-observe patterns and supports more accurate, efficient, and dynamic credit risk predictions using models such as random forests, gradient boosting, and decision trees. The analysis also evaluates implementation challenges, including legacy system integration, data quality, and regulatory compliance, while emphasizing transparency requirements and the use of forward-looking macroeconomic indicators.","OPEN ACCESS  \nFinance & Accounting Research Journal P-ISSN: 2708-633X, E-ISSN: 2708-6348  \nVolume 6, Issue 8, P.No. 1416-1435, August 2024 DOI: 10.51594/farj.v6i8 .1399  \nFair East Publishers [Journal Homepage: ](Journal Homepage: www.fepbl.com/index.php/farj)[www.fepbl.com/index.php/farj](Journal Homepage: www.fepbl.com/index.php/farj)  \nData-driven credit risk monitoring: Leveraging machine learning  \nin risk management  \nStanley Chidozie Umeorah 1, Adesola Oluwatosin Adelaja2, Bibitayo Ebunlomo Abikoye3, Oluwatoyin Funmilayo Ayodele2, & Yewande Mariam Ogunsuji4  \n1University of Michigan, MI, USA.  \n2University of Virginia, VA, USA.  \n3Central Bank of Nigeria, Banking Supervision Department, Abuja, Nigeria.  \n4 Sahara Group, Ikoyi, Lagos, Nigeria  \n*Corresponding Author: Stanley Chidozie Umeorah [Corresponding Author Email: ](Corresponding Author Email: ustanley@umich.edu)[ustanley@umich.edu](Corresponding Author Email: ustanley@umich.edu)  \nArticle Received: 01-03-24 Accepted: 10-06-24 Published: 11-08-24  \nLicensing Details: Author retains the right of this article. The article is distributed under the terms of  \nthe Creative Commons Attribution-Non Commercial 4.0 License  \n([http://www.creativecommons.org/licences/by-nc/4.0/](http://www.creativecommons.org/licences/by-nc/4.0/)) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the Journal open access page.  \nABSTRACT  \nThis review explores the evolution of credit risk monitoring, tracing its journey from traditional qualitative assessments to the current integration of machine learning (ML) . It highlights how the integration of ML and big data has introduced unprecedented capabilities for analyzing extensive datasets and detecting subtle patterns beyond human capacity. These advanced technologies enable more accurate, efficient and dynamic credit risk predictions through techniques such as random forests, gradient boosting and decision trees. The transformative potential of these methodologies in credit risk assessment was critically examined, addressing challenges such as legacy system integration, data quality and regulatory compliance. It emphasizes the importance of incorporating forward-looking macroeconomic indicators to comply with applicable financial reporting standards and regulatory requirements. Furthermore, it highlights the necessity of ensuring model transparency to maintain trust and compliance. By leveraging the power of big data and ML, the study shows that financial institutions can achieve more precise and proactive risk assessments, enhancing decision-making processes and mitigating potential risks. This comprehensive review provides valuable insights for stakeholders, guiding the  \nimplementation of advanced analytical techniques to improve credit risk management. Ultimately, it underscores the potential for a more robust, efficient and stable financial system through the strategic application of ML and big data analytics.  \nKeywords: Credit Risk Management, Financial Services, Machine Learning, Big Data, Financial Transformation.  \nINTRODUCTION  \nCredit risk monitoring has undergone significant evolution over the years, driven by advancesin technological innovations, regulatory developments and financial crises. Traditional credit risk assessment relied heavily on qualitative methods and the personal judgment of loan officers, often involving manual financial statement analysis and assessments of the borrower's character and credit history, which were prone to inconsistency and bias. The traditional credit monitoring process typically begins after loan disbursement to identify early warning signals and track changes to the borrower's financial health. This might include monitoring for missed payments, defaults on other loans, significant income drops, or anyother red flags that could signal potential repayment issues. In the mid-20th centur","cbCaieaEhh7iTan5","https://ap.wps.com/l/cbCaieaEhh7iTan5","pdf",847475,1,20,"English","en",105,"# Abstract\n# Introduction\n## From qualitative monitoring to statistical credit scoring\n## Integration of digital data and analytics\n## Recent shift toward machine learning and AI","[{\"question\":\"How has credit risk monitoring evolved over time?\",\"answer\":\"It evolved from qualitative assessments and manual judgment to statistical credit scoring models, then toward technology-enabled analytics using richer data sources, and finally to machine learning and AI-driven monitoring.\"},{\"question\":\"What role does machine learning play in credit risk predictions?\",\"answer\":\"Machine learning analyzes large datasets beyond human capacity, uncovering subtle patterns and correlations and improving prediction accuracy as more data is processed.\"},{\"question\":\"What challenges does adopting ML for credit risk monitoring face?\",\"answer\":\"Key challenges include integrating with legacy systems, ensuring data quality, meeting regulatory compliance requirements, and maintaining model transparency to preserve trust.\"}]","Data-driven credit risk monitoring - 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