Xu, J.; Yang, T.; Zhuang, S.; Li, H.; Lu, W. AI-Based Financial Transaction Monitoring and Fraud Prevention with Behaviour Prediction. Preprints2024, 2024071107. https://doi.org/10.20944/preprints202407.1107.v1
APA Style
Xu, J., Yang, T., Zhuang, S., Li, H., & Lu, W. (2024). AI-Based Financial Transaction Monitoring and Fraud Prevention with Behaviour Prediction. Preprints. https://doi.org/10.20944/preprints202407.1107.v1
Chicago/Turabian Style
Xu, J., Huixiang Li and Wenran Lu. 2024 "AI-Based Financial Transaction Monitoring and Fraud Prevention with Behaviour Prediction" Preprints. https://doi.org/10.20944/preprints202407.1107.v1
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. The results demonstrate deep learning models' strong feature extraction capability and adaptability, highlighting their potential to surpass traditional methods. 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 financial fraud detection using deep learning methodologies.
Keywords
Deep Learning; Fraud Detection; Autoencoder; Financial Transactions
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
