A Hybrid Deep Learning Approach for Financial Fraud Detection in Enterprise Management Systems
DOI:
https://doi.org/10.71143/7cve9q24Keywords:
Financial fraud detection, Deep Learning, Hybrid model, Enterprise management, CNN, LSTMAbstract
The issue of financial fraud is one of the greatest concerns for organizations, especially in enterprise management systems where very large financial transactions are carried out. The conventional fraud detection methods are unable to assist in detecting the sophisticated fraud activities due to their complexity and volumes of data. In the paper, the idea to use the hybrid deep learning methodology is proposed in the form of convolutional neural networks (CNN) and long short-term memory (LSTM) combinations in financial fraud detection in enterprise management systems. The method combines the use of CNN for feature extraction with LSTM to determine the sequence of data in financial transactions. By combining the two models of deep learning, there is hope that the methodology will display a more conveyed outcome in the detection of fraud, as both aspects, feature learning and temporal sequence prediction, would be favorable. The performance of this hybrid model in the work was evaluated on a financial fraud data set, and its results were compared to the conventional machine learning models in accuracy and efficiency measures. The results show that the hybrid deep learning algorithm is highly superior to the current methods insofar as detection accuracy, false positives, and processing time are concerned. The paper will expand towards its end by commenting about the implications that the proposed model has on the improvement of fraud detection systems in enterprise management, controversial issues, and possible areas of research in the future.
Downloads
References
Bello, O. A., & Olufemi, K. (2024). Artificial intelligence in fraud prevention: Exploring techniques and applications challenges and opportunities. Computer Science & IT Research Journal, 5(6), 1505. https://doi.org/10.51594/csitrj.v5i6.1252
Choi, D., & Lee, K. (2018). An Artificial Intelligence Approach to Financial Fraud Detection under IoT Environment: A Survey and Implementation. Security and Communication Networks, 2018, 1. https://doi.org/10.1155/2018/5483472
Nicholls, J., Kuppa, A., & Le‐Khac, N. (2021). Financial Cybercrime: A Comprehensive Survey of Deep Learning Approaches to Tackle the Evolving Financial Crime Landscape. IEEE Access, 9, 163965. https://doi.org/10.1109/access.2021.3134076
Pan, E. (2024). Machine Learning in Financial Transaction Fraud Detection and Prevention. Transactions on Economics Business and Management Research, 5, 243. https://doi.org/10.62051/16r3aa10
Thomas, J. G., Mudur, S. P., & Shiri, N. (2019). Detecting Anomalous Behaviour from Textual Content in Financial Records. IEEE/WIC/ACM International Conference on Web Intelligence, 373. https://doi.org/10.1145/3350546.3352550
Zhang, Z., Zhou, X., Zhang, X., Wang, L., & Wang, P. (2018). A Model Based on Convolutional Neural Network for Online Transaction Fraud Detection. Security and Communication Networks, 2018, 1. https://doi.org/10.1155/2018/5680264
Bhusal, D., Shin, H. J., Shewale, A. A., Veerabhadran, M. K., Clifford, M., Rampazzi, S., & Rastogi, N. (2023). SoK: Modeling Explainability in Security Analytics for Interpretability, Trustworthiness, and Usability. Proceedings of the 17th International Conference on Availability, Reliability and Security, 1. https://doi.org/10.1145/3600160.3600193
Collaris, D., Vink, L. M., & Wijk, J. J. van. (2018). Instance-Level Explanations for Fraud Detection: A Case Study. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1806.07129
Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Specter, M., & Kagal, L. (2018). Explaining Explanations: An Overview of Interpretability of Machine Learning. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1806.00069
Samek, W., & Müller, K. (2019). Towards Explainable Artificial Intelligence. In Lecture notes in computer science (p. 5). Springer Science+Business Media. https://doi.org/10.1007/978-3-030-28954-6_1
Samek, W., Wiegand, T., & Müller, K. (2017). Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1708.08296
Wu, T.-Y., & Wang, Y.-T. (2021). Locally Interpretable One-Class Anomaly Detection for Credit Card Fraud Detection. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2108.02501
Yeo, W. J., Heever, W. van der, Mao, R., Cambria, E., Satapathy, R., & Mengaldo, G. (2023). A Comprehensive Review on Financial Explainable AI [Review of A Comprehensive Review on Financial Explainable AI]. arXiv (Cornell University). Cornell University. https://doi.org/10.48550/arxiv.2309.11960
Adhikari, P., Hamal, P., & Baidoo, F. (2024). Artificial Intelligence in fraud detection: Revolutionizing financial security. International Journal of Science and Research Archive, 13(1), 1457. https://doi.org/10.30574/ijsra.2024.13.1.1860
Kumar, D., Taylor, G. W., & Wong, A. (2017). Opening the Black Box of Financial AI with CLEAR-Trade: A CLass-Enhanced Attentive Response Approach for Explaining and Visualizing Deep Learning-Driven Stock Market Prediction. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1709.01574
Kyaw, P. H., Gutiérrez, J., & Ghobakhlou, A. (2024). A Systematic Review of Deep Learning Techniques for Phishing Email Detection [Review of A Systematic Review of Deep Learning Techniques for Phishing Email Detection]. Electronics, 13(19), 3823. Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/electronics13193823
Luo, B., Zhen, Z., Wang, Q., Ke, A., Lu, S., & He, B. (2023). AI-powered Fraud Detection in Decentralized Finance: A Project Life Cycle Perspective. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2308.15992
Kerwin, K. R., & Bastian, N. D. (2020). Stacked generalizations in imbalanced fraud data sets using resampling methods. The Journal of Defense Modeling and Simulation Applications Methodology Technology, 18(3), 175. https://doi.org/10.1177/1548512920962219
Tran, T. A. (2022). On some studies of Fraud Detection Pipeline and related issues from the scope of Ensemble Learning and Graph-based Learning. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2205.04626
Sharma, V., Tripathi, A. K., & Mittal, H. (2022). Technological revolutions in smart farming: Current trends, challenges & future directions.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Research and Review in Applied Science, Humanities, and Technology
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
