Application of Machine Learning Model in Fraud Identification: A Comparative Study of CatBoost, XGBoost and LightGBM

Jiaran Liu; Yao Xiao; Li Tan

doi:10.54254/2755-2721/2025.21637

Abstract

In the digital age, credit card fraud seriously affects financial stability and consumer trust, and machine learning technology provides a new way for its detection. In this study, the performance of three machine learning models, CatBoost, XGBoost and LightGBM, in credit card fraud detection is compared and analyzed by using a credit card transaction data set containing more than 1.85 million records. Through hierarchical K-fold cross-validation, with F1 score, accuracy rate and recall rate as evaluation indicators, the results show that the comprehensive performance of CatBoost model is the best, with F1 score of 0.9161, which is excellent in balancing and accurately identifying fraudulent transactions and detecting all fraudulent transactions. At the same time, the top 10 important characteristics that affect the model prediction are determined, such as transaction amount, cardholder age, urban population, etc. These characteristics provide a key basis for constructing and optimizing the fraud detection model. Based on the research results, it is suggested that financial institutions optimize the model with CatBoost as the core, expand the data feature dimension and strengthen real-time monitoring to improve the accuracy and timeliness of fraud detection. This study provides a valuable reference for the field of credit card fraud detection and helps to promote the development of financial security technology.

Keywords: Fraud detection , CatBoost,XGBoost,LightGBM,Unbalanced data

Article Information

Received: 17 February 2025

Published: 28 March 2025

DOI: https://doi.org/10.54254/2755-2721/2025.21637

Article Type: Research Article

Cite this article

Liu,J.;Xiao,Y.;Tan,L. (2025). Application of Machine Learning Model in Fraud Identification: A Comparative Study of CatBoost, XGBoost and LightGBM. Applied and Computational Engineering,119,135-141.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About this Volume

Volume Title: ACE Vol.119

Part of Series: Applied and Computational Engineering

ISSN: 2755-2721 (Print) / 2755-273X (Online)

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[1]Ali A, Abd Razak S, Othman S H, et al. Financial fraud detection based on machine learning: a systematic literature review[J]. Applied Sciences, 2022, 12(19): 9637.

[2]Hernandez Aros L, Bustamante Molano L X, Gutierrez-Portela F, et al. Financial fraud detection through the application of machine learning techniques: a literature review[J]. Humanities and Social Sciences Communications, 2024, 11(1): 1-22.

[3]Alsuwailem A A S, Salem E, Saudagar A K J. Performance of different machine learning algorithms in detecting financial fraud[J]. Computational Economics, 2023, 62(4): 1631-1667.

[4]Obeng S, Iyelolu T V, Akinsulire A A, et al. Utilizing machine learning algorithms to prevent financial fraud and ensure transaction security[J]. World Journal of Advanced Research and Reviews, 2024, 23(1): 1972-1980.

[5]Ashtiani M N, Raahemi B. Intelligent fraud detection in financial statements using machine learning and data mining: a systematic literature review[J]. Ieee Access, 2021, 10: 72504-72525.

[6]Whiting D G, Hansen J V, McDonald J B, et al. Machine learning methods for detecting patterns of management fraud[J]. Computational Intelligence, 2012, 28(4): 505-527.

[7]Song X P, Hu Z H, Du J G, et al. Application of machine learning methods to risk assessment of financial statement fraud: evidence from China[J]. Journal of Forecasting, 2014, 33(8): 611-626.

[8]Prokhorenkova L, Gusev G, Vorobev A, et al. CatBoost: unbiased boosting with categorical features[J]. Advances in neural information processing systems, 2018, 31.

[9]Hancock J T, Khoshgoftaar T M. CatBoost for big data: an interdisciplinary review[J]. Journal of big data, 2020, 7(1): 94.

[10]Chen T, Guestrin C. Xgboost: A scalable tree boosting system[C]//Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 2016: 785-794.

[11]Ke G, Meng Q, Finley T, et al. Lightgbm: A highly efficient gradient boosting decision tree[J]. Advances in neural information processing systems, 2017, 30.

[12]Wang D, Li L, Zhao D. Corporate finance risk prediction based on LightGBM[J]. Information Sciences, 2022, 602: 259-268.