Deep Learning-Based Financial Fraud Detection with Temporal and Feature-Level Adaptation

Weilun Tsai

doi:10.54097/j8gvbk95

Authors

Weilun Tsai

DOI:

https://doi.org/10.54097/j8gvbk95

Keywords:

Deep Learning, Financial Fraud Detection, Temporal Modeling, Feature Adaptation, LSTM Networks, Adaptive Systems, Real-Time Processing, Financial Security

Abstract

Financial fraud detection systems face significant challenges in adapting to evolving fraud patterns while maintaining high detection accuracy and minimizing false positives in dynamic financial environments. Traditional approaches rely on static models that cannot effectively capture temporal dependencies or adapt feature representations to changing fraud behaviors. The challenge lies in developing systems that can simultaneously model temporal transaction patterns and dynamically adapt feature representations to detect emerging fraud techniques while maintaining computational efficiency for real-time financial applications. This study proposes a novel Temporal and Feature-Level Adaptive Deep Learning (TFADL) framework that integrates temporal sequence modeling with dynamic feature adaptation mechanisms for enhanced financial fraud detection. The framework employs Long Short-Term Memory (LSTM) networks to capture temporal transaction patterns while utilizing adaptive feature selection and representation learning techniques to continuously adjust to evolving fraud behaviors. The integrated approach enables real-time fraud detection with continuously updated feature representations that respond to changing fraud patterns and transaction dynamics. Experimental evaluation using comprehensive financial fraud datasets demonstrates that the proposed framework achieves 44% improvement in fraud detection accuracy compared to traditional deep learning approaches. The TFADL method results in 39% better detection of novel fraud patterns and 35% reduction in false positive rates while maintaining processing speeds suitable for real-time financial transaction monitoring. The framework successfully combines temporal modeling with adaptive feature learning to provide 42% improvement in detection of sophisticated fraud schemes that exhibit complex temporal and feature-level characteristics.

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References

[1] Xing, S., Wang, Y., & Liu, W. (2025). Self-Adapting CPU Scheduling for Mixed Database Workloads via Hierarchical Deep Reinforcement Learning. Symmetry, 17(7), 1109.

[2] Wang, M., Zhang, X., Yang, Y., & Wang, J. (2025). Explainable Machine Learning in Risk Management: Balancing Accuracy and Interpretability. Journal of Financial Risk Management, 14(3), 185-198.

[3] Aziz, L. A. R., & Andriansyah, Y. (2023). The role artificial intelligence in modern banking: an exploration of AI-driven approaches for enhanced fraud prevention, risk management, and regulatory compliance. Reviews of Contemporary Business Analytics, 6(1), 110-132.

[4] Mai, N., & Cao, W. (2025). Personalized Learning and Adaptive Systems: AI-Driven Educational Innovation and Student Outcome Enhancement. International Journal of Education and Humanities.

[5] Olushola, A., & Mart, J. (2024). Fraud detection using machine learning. ScienceOpen Preprints.

[6] Bello, O. A., Folorunso, A., Onwuchekwa, J., Ejiofor, O. E., Budale, F. Z., & Egwuonwu, M. N. (2023). Analysing the impact of advanced analytics on fraud detection: a machine learning perspective. European Journal of Computer Science and Information Technology, 11(6), 103-126.

[7] Yaseen, A. (2020). Uncovering evidence of attacker behavior on the network. ResearchBerg Review of Science and Technology, 3(1), 131-154.

[8] Ghimire, S. (2023). Timetrail: Unveiling financial fraud patterns through temporal correlation analysis. arXiv preprint arXiv:2308.14215.

[9] Bello, O. A., Ogundipe, A., Mohammed, D., Adebola, F., & Alonge, O. A. (2023). AI-driven approaches for real-time fraud detection in US financial transactions: Challenges and opportunities. European Journal of Computer Science and Information Technology, 11(6), 84-102.

[10] Duane, J., Morgan, A., & Carter, E. (2025). A Review of Financial Data Analysis Techniques for Unstructured Data in the Deep Learning Era: Methods, Challenges, and Applications. OSF Preprints, (gdvbj_v1).

[11] Bello, H. O., Ige, A. B., & Ameyaw, M. N. (2024). Adaptive machine learning models: Concepts for real-time financial fraud prevention in dynamic environments. World Journal of Advanced Engineering Technology and Sciences, 12(02), 021-034.

[12] Olayinka, O. H. (2021). Big data integration and real-time analytics for enhancing operational efficiency and market responsiveness. Int J Sci Res Arch, 4(1), 280-96.

[13] Cernat, R. (2024). Transfer Learning-Based Applications for Cross-Domain Fraud Analysis in National Security Procurement Chains. Nuvern Machine Learning Reviews, 1(1), 41-48.

[14] Mienye, I. D., Swart, T. G., & Obaido, G. (2024). Recurrent neural networks: A comprehensive review of architectures, variants, and applications. Information, 15(9), 517.

[15] Dastidar, K. G., Caelen, O., & Granitzer, M. (2024). Machine learning methods for credit card fraud detection: A survey. IEEE Access.

[16] Vanini, P., Rossi, S., Zvizdic, E., & Domenig, T. (2023). Online payment fraud: from anomaly detection to risk management. Financial Innovation, 9(1), 66.

[17] Popov, A. (2023). Feature engineering methods. In Advanced Methods in Biomedical Signal Processing and Analysis (pp. 1-29). Academic Press.

[18] Cao, J., Zheng, W., Ge, Y., & Wang, J. (2025). DriftShield: Autonomous Fraud Detection via Actor-Critic Reinforcement Learning with Dynamic Feature Reweighting. IEEE Open Journal of the Computer Society.

[19] Alonge, E. O., Eyo-Udo, N. L., Ubanadu, B. C., Daraojimba, A. I., Balogun, E. D., & Ogunsola, K. O. (2021). Enhancing data security with machine learning: A study on fraud detection algorithms. Journal of Data Security and Fraud Prevention, 7(2), 105-118.

[20] Chalapathy, R., & Chawla, S. (2019). Deep learning for anomaly detection: A survey. arXiv preprint arXiv:1901.03407.

[21] Sarıyüce, A. E. (2025). A powerful lens for temporal network analysis: temporal motifs. Discover Data, 3(1), 1-22.

[22] Georgiou, T., Liu, Y., Chen, W., & Lew, M. (2020). A survey of traditional and deep learning-based feature descriptors for high dimensional data in computer vision. International Journal of Multimedia Information Retrieval, 9(3), 135-170.

[23] Anowar, F., & Sadaoui, S. (2021). Incremental learning framework for real-world fraud detection environment. Computational Intelligence, 37(1), 635-656.

[24] Okunola, O. A., Adebayo, O. S., Favour-Bethy, T. A., & Otasowie, O. (2023). Dynamic Resilience in Credit Card Fraud Detection: The Adaptive Accuracy Weighted Ensemble Approach.

[25] Serrano, S., & Smith, N. A. (2019). Is attention interpretable?. arXiv preprint arXiv:1906.03731.

[26] Kumar, S. (2025). Designing real-time distributed systems for high-frequency, high-volume data processing. World Journal of Advanced Engineering Technology and Sciences, 15(2), 1497-1507.

[27] Cao, W., Mai, N., & Liu, W. (2025). Adaptive Knowledge Assessment via Symmetric Hierarchical Bayesian Neural Networks with Graph Symmetry-Aware Concept Dependencies. Symmetry.

[28] Xing, S., & Wang, Y. (2025). Proactive Data Placement in Heterogeneous Storage Systems via Predictive Multi-Objective Reinforcement Learning. IEEE Access.

[29] Anchoori, S. (2024). Optimizing Real-Time Data Pipelines For Financial Fraud Detection: A Systematic Analysis of Performance, Scalability, and Cost Efficiency in Banking Systems. International Journal of Computer Engineering and Technology, 15(6).