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The Imbalanced Classification of Fraudulent Bank Transactions Using Machine Learning

Author

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  • Alexey Ruchay

    (Department of Information Security, South Ural State University (National Research University), Chelyabinsk 454080, Russia
    Department of Mathematics, Chelyabinsk State University, Chelyabinsk 454001, Russia)

  • Elena Feldman

    (Department of Mathematics, Chelyabinsk State University, Chelyabinsk 454001, Russia)

  • Dmitriy Cherbadzhi

    (Department of Mathematics, Chelyabinsk State University, Chelyabinsk 454001, Russia)

  • Alexander Sokolov

    (Department of Information Security, South Ural State University (National Research University), Chelyabinsk 454080, Russia)

Abstract

This article studies the development of a reliable AI model to detect fraudulent bank transactions, including money laundering, and illegal activities with goods and services. The proposed machine learning model uses the CreditCardFraud dataset and utilizes multiple algorithms with different parameters. The results are evaluated using Accuracy, Precision, Recall, F1 score, and IBA. We have increased the reliability of the imbalanced classification of fraudulent credit card transactions in comparison to the best known results by using the Tomek links resampling algorithm of the imbalanced CreditCardFraud dataset. The reliability of the results, using the proposed model based on the TPOT and RandomForest algorithms, has been confirmed by using 10-fold cross-validation. It is shown that on the dataset the accuracy of the proposed model detecting fraudulent bank transactions reaches 99.99%.

Suggested Citation

  • Alexey Ruchay & Elena Feldman & Dmitriy Cherbadzhi & Alexander Sokolov, 2023. "The Imbalanced Classification of Fraudulent Bank Transactions Using Machine Learning," Mathematics, MDPI, vol. 11(13), pages 1-15, June.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:13:p:2862-:d:1179718
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    References listed on IDEAS

    as
    1. Ayed Alwadain & Rao Faizan Ali & Amgad Muneer, 2023. "Estimating Financial Fraud through Transaction-Level Features and Machine Learning," Mathematics, MDPI, vol. 11(5), pages 1-15, February.
    2. Tzu-Hsuan Lin & Jehn-Ruey Jiang, 2021. "Credit Card Fraud Detection with Autoencoder and Probabilistic Random Forest," Mathematics, MDPI, vol. 9(21), pages 1-16, October.
    3. Marina Pavlovna Khrestina & Dmitry Ivanovich Dorofeev & Polina Andreevna Kachurina & Timur Rinatovich Usubaliev & Aleksey Sergeevich Dobrotvorskiy, 2017. "Development of Algorithms for Searching, Analyzing and Detecting Fraudulent Activities in the Financial Sphere," European Research Studies Journal, European Research Studies Journal, vol. 0(4B), pages 484-498.
    4. Surjeet Dalal & Bijeta Seth & Magdalena Radulescu & Carmen Secara & Claudia Tolea, 2022. "Predicting Fraud in Financial Payment Services through Optimized Hyper-Parameter-Tuned XGBoost Model," Mathematics, MDPI, vol. 10(24), pages 1-17, December.
    5. Singh, Kishore & Best, Peter, 2019. "Anti-Money Laundering: Using data visualization to identify suspicious activity," International Journal of Accounting Information Systems, Elsevier, vol. 34(C), pages 1-1.
    6. Emanuel Mineda Carneiro & Carlos Henrique Quartucci Forster & Lineu Fernando Stege Mialaret & Luiz Alberto Vieira Dias & Adilson Marques da Cunha, 2022. "High-Cardinality Categorical Attributes and Credit Card Fraud Detection," Mathematics, MDPI, vol. 10(20), pages 1-23, October.
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