Application of Advanced Machine Learning Methods in Financial Fraud Detection
Keywords:
machine learning, financial fraud, neural network, decision tree, gradient boosting.Abstract
The identification and prevention of financial fraud represent one of the most critical challenges facing organizations and financial institutions, with significant implications for the health and stability of economic systems. The present study investigates the application of advanced machine learning methods in detecting financial fraud, aiming to evaluate and compare the performance of several machine learning models in this domain. In this research, decision tree, artificial neural networks, support vector machines, gradient boosting, and random forest methods were applied to financial data from companies listed on the Tehran Stock Exchange. After conducting the necessary preprocessing steps, the models were trained. The results indicate that the gradient boosting model, with an accuracy of 95%, a sensitivity of 92%, and an F1-score of 0.94, delivered the best performance in detecting financial fraud. Additionally, artificial neural networks and decision tree models demonstrated acceptable performance with accuracies of 90% and 80%, respectively. Unlike previous studies that typically focused on a limited set of algorithms, this study adopts a comprehensive and multi-model approach, enabling a precise and practical comparison of various algorithms’ performance. The use of real financial data and rigorous preprocessing enhanced the validity and generalizability of the findings. The results of this study suggest that employing advanced machine learning methods—particularly the gradient boosting algorithm—can significantly enhance the accuracy and sensitivity of financial fraud detection compared to traditional methods, and can serve as an effective and practical solution in fraud detection systems. This multi-model and application-oriented approach highlights the study’s main innovation in improving the precision and efficiency of financial fraud detection using state-of-the-art machine learning technologies, and constitutes a meaningful step toward strengthening security and trust in financial systems.
References
S. Mehrani and A. Rahimipour, "Optimizing the Beneish Fraud Model in Predicting Financial Statement Restatements Using a Combination of Neural Networks and Genetic Algorithms," Journal of Accounting and Management Auditing, vol. 54, pp. 73-87, 2025.
H. Lak, "Application of Artificial Intelligence and Machine Learning in Continuous Auditing and Detecting Financial Fraud," 2024.
A. A. S. Alsuwailem, E. Salem, and A. K. J. Saudagar, "Performance of different machine learning algorithms in detecting financial fraud," Computational Economics, vol. 62, no. 4, pp. 1631-1667, 2023, doi: 10.1007/s10614-022-10314-x.
E. A. Minastireanu and G. Mesnita, "An Analysis of the Most Used Machine Learning Algorithms for Online Fraud Detection," Informatica Economica, vol. 23, no. 1, 2019, doi: 10.12948/issn14531305/23.1.2019.01.
M. N. Nezami, F. M. Nodeh, and S. Khordyar, "Modeling Bankruptcy Prediction with Emphasis on Modern Measurement Methods Using Neural Networks and Support Vector Machines," Journal of Accounting and Management Auditing, vol. 55, pp. 265-278, 2025.
M. Asadi and A. Rad, "Fraud Detection in Banking Transactions Using Hyperparameter Optimization of the Support Vector Machine Algorithm," 2023.
Z. Zhao and T. Bai, "Financial fraud detection and prediction in listed companies using SMOTE and machine learning algorithms," Entropy, vol. 24, no. 8, p. 1157, 2022, doi: 10.3390/e24081157.
M. Ali, S. A. Mirarab Baygi, and N. Farjian, "Presenting a Model for Predicting Financial Bankruptcy Risk in Listed and Over-the-Counter Companies Using Machine Learning Algorithms," Capital Market Analysis Journal, vol. 2, pp. 79-99, 2022.
M. Mahmoudi and M. Shahrokh, "Machine Learning in Fraud Detection," 2024.
H. Kamrani and B. Abedini, "Developing a Model for Detecting Financial Statement Fraud Using Artificial Neural Networks and Support Vector Machines in Companies Listed on the Tehran Stock Exchange," Journal of Accounting and Management Auditing, vol. 41, pp. 285-314, 2022.
W. Duan, N. Hu, and F. Xue, "The information content of financial statement fraud risk: An ensemble learning approach," Decision Support Systems, vol. 182, p. 114231, 2024, doi: 10.1016/j.dss.2024.114231.
S. Alizadeh Fard, "Presenting a Novel Framework Based on Collective Learning for Detecting Fraud in Financial Data," 2023.
S. Ghorbani, "Analysis and Explanation of Financial Accounting Theory Based on the Conceptual Framework of the Financial Accounting Standards Board," Journal of Accounting and Management Auditing, vol. 53, pp. 37-53, 2025.
M. S. Anari and M. K. Yazdi, "Application of Data Envelopment Analysis and Machine Learning in Detecting Accounting Fraud," 2024.
Y. Chen and Z. Wu, "Financial fraud detection of listed companies in China: A machine learning approach," Sustainability, vol. 15, no. 1, p. 105, 2022, doi: 10.3390/su15010105.
Y. Alghofaili, A. Albattah, and M. A. Rassam, "A financial fraud detection model based on LSTM deep learning technique," Journal of Applied Security Research, vol. 15, no. 4, pp. 498-516, 2020, doi: 10.1080/19361610.2020.1815491.
T. Sadeghi and A. Nodehi, "Fraud Detection in Bank Cards Based on Image Processing Using Machine Learning Algorithms," 2023.
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Copyright (c) 2025 Zahra Menatpour (Author); Gholamreza Farsad Amanollahi
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
