{"id":2499,"date":"2025-06-23T10:37:39","date_gmt":"2025-06-23T09:37:39","guid":{"rendered":"http:\/\/newskbncran.ru\/?page_id=2499"},"modified":"2026-04-13T09:34:50","modified_gmt":"2026-04-13T08:34:50","slug":"27-2-1-en","status":"publish","type":"page","link":"https:\/\/izvestiyakbncran.ru\/index.php\/en\/27-2-1-en\/","title":{"rendered":"27.2.1 en"},"content":{"rendered":"\n

Building a machine learning model for predicting fraudulent transactions<\/strong><\/h1>\n\n\n\n

A.F. Konstantinov, L.P. Dyakonova<\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/p>\n\n\n\n

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Abstract<\/em><\/strong><\/span>.<\/strong> The article presents development of a machine learning model for predicting fraudulent transactions using transactional data from a bank. It discusses the features of encoding categorical variables related to the presence of time in the transactional data to avoid information leakage. Additionally, experiments were conducted on the application of bagging and the creation of additional variables based on their contribution to the final prediction using Shapley values. The quality metrics of the machine learning model are examined and analyzed.<\/p>\n\n\n\n

Keywords<\/em><\/strong>:<\/strong> fraudulent transactions, catboost, encoding categorical variables, catboost_encoder, target_encoder, bagging, variables creation, Shapley values<\/p>\n\n\n\n

For citation<\/strong>.<\/strong> Konstantinov A.F., Dyakonova L.P. Building a machine learning model for predicting fraudulent transactions.  News  of  the  Kabardino-Balkarian  Scientific  Center  of  RAS.<\/em>  <\/strong>2025.  Vol. 27.  No. 2.  Pp. 11\u201322. DOI: 10.35330\/1991-6639-2025-27-2-11-22<\/p>\n\n\n\n

R<\/strong>eferences<\/summary>\n
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  2. Awosika T., Shukla R.M., Pranggono B. Transparency and Privacy: The Role of Explainable  AI  and  Federated  Learning  in  Financial  Fraud  Detection.  IEEE Access. <\/em>2024. Vol. 12. Pp. 64551\u201364560. DOI: 10.1109\/ACCESS.2024.3394528<\/li>\n\n\n\n
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  5. He K., Yang Q., Ji L. et al. Financial Time Series Forecasting with the Deep Learning Ensemble Model. Mathematics<\/em>. 2023. Vol. 11. No. 4. P. 1054. DOI: 10.3390\/math11041054<\/li>\n\n\n\n
  6. Prokhorenkova L., Gusev G., Vorobev A. et al. CatBoost: unbiased boosting with categorical features. NIPS’18: Proceedings of the 32nd International Conference on Neural Information Processing Systems<\/em>. 2018. Pp. 6639\u20136649. DOI: 0.48550\/arXiv.1706.09516<\/li>\n\n\n\n
  7. Micci-Barreca D. A Preprocessing Scheme for High-Cardinality Categorical Attributes in Classification and Prediction Problems. ACM SIGKDD Explorations Newsletter.<\/em> Vol. 3. No. 1. Pp. 27\u201332. DOI: 10.1145\/507533.507538<\/li>\n\n\n\n
  8. Dorogush A.V., Ershov V., Gulin A. CatBoost: gradient boosting with categorical features support. Workshop on ML Systems at NIPS.<\/em> 2017. DOI: 10.48550\/arXiv.1810.11363<\/li>\n\n\n\n
  9. Breiman  L. Bagging predictors. Machine Learning.<\/em> 1996. Vol.  24. No. 2. Pp.  123\u2013140. DOI: 10.1007\/BF00058655<\/li>\n\n\n\n
  10.  Official website Catboost. Common parameters. \u0422\u043e\u0447\u043a\u0430 \u0434\u043e\u0441\u0442\u0443\u043f\u0430: https:\/\/catboost.ai\/en\/docs\/ references\/training-parameters\/common#bagging_temperature (\u0434\u0430\u0442\u0430 \u043e\u0431\u0440\u0430\u0449\u0435\u043d\u0438\u044f: 10 \u044f\u043d\u0432\u0430\u0440\u044f 2025)<\/li>\n\n\n\n
  11. Shapley L. Notes on the n-person game, ii: the value of an n-person game. 1951.<\/li>\n\n\n\n
  12. Official website SHAP library. \u0422\u043e\u0447\u043a\u0430 \u0434\u043e\u0441\u0442\u0443\u043f\u0430: https:\/\/shap.readthedocs.io\/en\/latest\/ example_notebooks\/tabular_examples\/tree_based_models\/Catboost%20tutorial.html (\u0434\u0430\u0442\u0430 \u043e\u0431\u0440\u0430\u0449\u0435\u043d\u0438\u044f: 10 \u044f\u043d\u0432\u0430\u0440\u044f 2025)<\/li>\n\n\n\n
  13. Brier Glenn W. Verification of forecasts expressed in terms of probability. Monthly Weather Review.<\/em> 1950. Vol. 78. No. 1. Pp. 1\u20133. Bibcode:1950MWRv…78….1B. DOI: 10.1175\/1520-0493(1950)078 <0001:VOFEIT> 2.0.CO<\/li>\n\n\n\n
  14. Akiba T., Sano S., Yanase T. et al. Optuna: A Next-generation Hyperparameter Optimization Framework. KDD ’19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining<\/em>. Pp. 2623\u20132631. DOI: 10.1145\/3292500.3330701<\/li>\n<\/ol>\n<\/details>\n\n\n\n
    Information about the authors<\/strong><\/summary>\n
    \n

    Alexey F. Konstantinov<\/strong>, Post-graduate Student, Department of Informatics, Plekhanov Russian University of Economics;<\/p>\n\n\n\n

    115054, Russia, Moscow, 36 Stremyannyy lane;<\/p>\n\n\n\n

    konstantinovaf@gmail.com, ORCID: https:\/\/orcid.org\/0009-0000-9591-3301, <\/strong>SPIN-code: 3088-3121<\/p>\n\n\n\n

    Lyudmila P. Dyakonova<\/strong>, Candidate of Physical and Mathematical Sciences, Associate Professor, Department of Informatics, Plekhanov Russian University of Economics;<\/p>\n\n\n\n

    115054, Russia, Moscow, 36 Stremyannyy lane;<\/p>\n\n\n\n

    Dyakonova.LP@rea.ru, ORCID: https:\/\/orcid.org\/0000-0001-5229-8070,SPIN-code: 2513-8831<\/p>\n<\/div>\n<\/details>\n","protected":false},"excerpt":{"rendered":"

    Building a machine learning model for predicting fraudulent transactions A.F. Konstantinov, L.P. Dyakonova Upload the full text Abstract. The article presents development of a machine learning model for predicting fraudulent transactions using transactional data from a bank. It discusses the features of encoding categorical variables related to the presence of time in the transactional data […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"wp-custom-template-home","meta":{"footnotes":""},"class_list":["post-2499","page","type-page","status-publish","hentry"],"yoast_head":"\n27.2.1 en - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/izvestiyakbncran.ru\/index.php\/en\/27-2-1-en\/\" \/>\n<meta property=\"og:locale\" content=\"ru_RU\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"27.2.1 en - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb\" \/>\n<meta property=\"og:description\" content=\"Building a machine learning model for predicting fraudulent transactions A.F. 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