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AI-based financial transaction monitoring and fraud prevention with behaviour prediction
- 1 Master of Science in Financial Engineering,University of Southern California , CA,USA
- 2 Financial Risk Management,University of Connecticut,Stamford CT,USA
- 3 Electrical Engineering,University of Washington,Seattle,WA,USA
- 4 Information Studies, Trine University, AZ, USA
- 5 Electrical Engineering,University of Texas at Austin,Austin, TX,USA
* Author to whom correspondence should be addressed.
Abstract
In this study, we explored the application of deep learning techniques for credit card fraud detection, aiming to improve the performance and reliability of anomaly detection methods in financial transactions. We first utilized the Isolation Forest algorithm, achieving a detection accuracy of 26% for the top 1000 transactions. Subsequently, we experimented with the Autoencoder algorithm, an unsupervised deep neural network model, which enhanced the detection accuracy to 33.6% in the best case, despite some fluctuations. However, the high imbalance in the dataset, with only 0.17% of transactions being fraudulent, poses a significant challenge. This study underscores the necessity for further experimentation and optimization of network structures and hyperparameters to achieve more stable and efficient fraud detection. The findings provide valuable insights and reference points for future research in the field of financial fraud detection using deep learning methodologies.
Keywords
Deep Learning, Fraud Detection, Autoencoder, Financial Transactions
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Cite this article
Xu,J.;Yang,T.;Zhuang,S.;Li,H.;Lu,W. (2024). AI-based financial transaction monitoring and fraud prevention with behaviour prediction. Applied and Computational Engineering,77,218-224.
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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Volume title: Proceedings of the 2nd International Conference on Software Engineering and Machine Learning
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