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Predicting Fraud in Financial Payment Services through Optimized Hyper-Parameter-Tuned XGBoost Model

Author

Listed:
  • Surjeet Dalal

    (Department of Computer Science and Engineering, Amity University Haryana, Gurugram 122413, Haryana, India)

  • Bijeta Seth

    (Department of Computer Science and Engineering, B. M. Institute of Engineering & Technology, Sonipat 131001, Haryana, India)

  • Magdalena Radulescu

    (Department of Finance, Accounting and Economics, University of Pitesti, 110001 Pitesti, Arges, Romania
    Institute of Doctoral Studies, Lucian Blaga University of Sibiu, 550136 Sibiu, Sibiu, Romania)

  • Carmen Secara

    (Department of Management and Business Administration, University of Pitesti, 110001 Pitesti, Arges, Romania)

  • Claudia Tolea

    (Department of Management and Business Administration, University of Pitesti, 110001 Pitesti, Arges, Romania)

Abstract

Online transactions, medical services, financial transactions, and banking all have their share of fraudulent activity. The annual revenue generated by fraud exceeds $1 trillion. Even while fraud is dangerous for organizations, it may be uncovered with the help of intelligent solutions such as rules engines and machine learning. In this research, we introduce a unique hybrid technique for identifying financial payment fraud by combining nature-inspired-based Hyperparameter tuning with several supervised classifier models, as implemented in a modified version of the XGBoost Algorithm. At the outset, we split out a sample of the full financial payment dataset to use as a test set. We use 70% of the data for training and 30% for testing. Records that are known to be illegitimate or fraudulent are predicted, while those that raise suspicion are further investigated using a number of machine learning algorithms. The models are trained and validated using the 10-fold cross-validation technique. Several tests using a dataset of actual financial transactions are used to demonstrate the effectiveness of the proposed approach.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:24:p:4679-:d:999120
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    References listed on IDEAS

    as
    1. Sharmila Subudhi & Suvasini Panigrahi, 2020. "Two-Stage Automobile Insurance Fraud Detection by Using Optimized Fuzzy C-Means Clustering and Supervised Learning," International Journal of Information Security and Privacy (IJISP), IGI Global, vol. 14(3), pages 18-37, July.
    2. Peterson K. Ozili, 2018. "Impact of digital finance on financial inclusion and stability," Borsa Istanbul Review, Research and Business Development Department, Borsa Istanbul, vol. 18(4), pages 329-340, December.
    3. Ozili, Peterson Kitakogelu, 2018. "Impact of Digital Finance on Financial Inclusion and Stability," MPRA Paper 84771, University Library of Munich, Germany.
    4. K.S.Reddy, 2019. "Pot the ball? Sovereign wealth funds’ outward FDI in times of global financial market turbulence: A yield institutions-based view," Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, vol. 19(4), pages 129-139.
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    Cited by:

    1. 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.
    2. Seyed Farshid Ghorashi & Maziyar Bahri & Atousa Goodarzi, 2024. "Developing and comparing machine learning approaches for predicting insurance penetration rates based on each country," Letters in Spatial and Resource Sciences, Springer, vol. 17(1), pages 1-29, December.
    3. Ludivia Hernandez Aros & Luisa Ximena Bustamante Molano & Fernando Gutierrez-Portela & John Johver Moreno Hernandez & Mario Samuel Rodríguez Barrero, 2024. "Financial fraud detection through the application of machine learning techniques: a literature review," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-22, December.

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