[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121183-en":3,"doc-seo-121183-105":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},121183,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Approaches to Risk Analysis in the Financial Sector Based on Machine Learning and Artificial Intelligence Methods","The article studies approaches for improving forecasting quality of machine learning models in finance, with emphasis on risk management in banking and on applied techniques for credit scoring and fraud detection. Explainable AI (XAI) methods are examined for use in financial organizations to address interpretability limitations. To identify effective models, experiments compare eight classification models across five open financial datasets. CatboostClassifier serves as the base model.","Munich Personal RePEc Archive  \nApproaches to risk analysis in the financial sector based on machine learning and artificial intelligence methods  \nDyakonova, Ludmila and Konstantinov, Alexey  \nPlekhanov Russian University of Economics  \n10 December 2024  \nOnline at [https://mpra. ub. uni-muenchen. de/122941/](https://mpra. ub. uni-muenchen. de/122941/)  \n[MPRA Paper No. 122941](MPRA Paper No. 122941) , [posted 16 Dec 2024 14:22 UTC](posted 16 Dec 2024 14:22 UTC)  \nApproaches to risk analysis in the financial sector based on machine learning and artificial  \nintelligence methods  \nDyakonova, Ludmila  \nAssociate Professor, Cand. Sc. (Physics&Mathematics), Associate Professor of the department  \nof Informatics  \nHigher School of Cybertechnologies, Mathematics and Statistics Plekhanov Russian University of Economics, Moscow, Russian Federation Plekhanov Russian University of Economics, 36 Stremyanny Lane, office 5.18 (building 9), Moscow, 117997, [Russian Federation. tel](Russian Federation. tel). +7 (495) 958-24-10  \n[0000-0001-5229-8070]  \n[Dyakonova.lp@rea.ru](Dyakonova.lp@rea.ru)  \nKonstantinov, Alexey  \ngraduate student  \nHigher School of Cybertechnologies, Mathematics and Statistics Plekhanov Russian University of Economics, Moscow, Russian Federation Plekhanov Russian University of Economics, 36 Stremyanny Lane, 5.18 (building 9), Moscow, 117997, [Russian Federation. tel](Russian Federation. tel). +7 (495) 958-24-10  \nAbstract  \nThe article studies approaches to improving the forecasting quality of machine learning models in finance. An overview of studies devoted to the application of machine learning models and artificial intelligence in the banking sector is given, both from the point of view of risk management and considering in more detail the applied methods of credit scoring and fraud detection. Aspects of applying explainable artificial intelligence (XAI) methods in financial organizations are considered. To identify the most effective machine learning models, the authors conducted experiments to compare 8 classification models used in the financial sector. The gradient boosting model CatboostClassifier was chosen as the base model. A comparison was carried out for the results obtained on the CatboostClassifier model with the characteristics of the other models: IsolationForest, feature ranking model using Recursive Feature Elimination (RFE), XAI Shapley values method, positive class weight increase models wrapper model. All models were applied to 5 open financial data sets. 1 dataset contains transaction data of credit card transactions, 3 datasets contain data on retail lending, and 1 dataset contains data on consumer lending. Our calculations revealed slight improvement for the models IsolationForest and wrapper model in comparison with the base CatboostClassifier model in terms of ROC_AUC for loan defaults data.  \nKey words: financial risks, credit scoring, fraud detection, machine learning, explainable artificial intelligence methods, Catboost, SHAP.  \nIntroduction  \nFinancial risks include risks associated with operations in financial markets, as well as the ability of economic entities to fulfill their obligations to counterparties in a timely manner and in full. Banking risks are risks specific to the activities of commercial banks, which imply the occurrence of a negative result in bank operations and have an adverse effect on the bank's capital. In the field of financial institutions, accurate assessment of credit risk is of paramount importance to maintain stability and profitability. In the retail banking business, the most serious  \nrisks are consumer credit risks and operational risks associated with fraudulent transactions. Every year, huge amounts of money are lost worldwide due to credit card fraud. Therefore, financial institutions are forced to constantly improve their fraud detection systems. Detection of financial fraud continues to be an important task for business intelligence technologies. T","cbCaiia5Tv6S69IP","https://ap.wps.com/l/cbCaiia5Tv6S69IP","pdf",398618,1,13,"English","en",105,"# Introduction\n## Motivation and research goal\n## Dataset and model setup\n# Literature Review\n## Prior work on ML/AI in banking risk management\n# Methods and Experiments\n## Model comparison and XAI integration\n# Results and Discussion\n## ROC_AUC-based performance findings","[{\"question\":\"What problem does the study address in financial risk modeling?\",\"answer\":\"The study targets improving forecasting quality in finance while mitigating interpretability gaps in machine learning results used for risk management.\"},{\"question\":\"Which risk-related applications are covered in the paper?\",\"answer\":\"It focuses on credit scoring and fraud detection within banking, including both credit lending and credit card transaction risk.\"},{\"question\":\"How did the authors evaluate which machine learning models are most effective?\",\"answer\":\"They ran experiments comparing eight classification models on five open financial datasets, using CatboostClassifier as the baseline and assessing results such as ROC_AUC.\"}]","Approaches to Risk Analysis in the Financial Sector Based on Machine Learning and Artificial Intelligence Methods | 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