Credit Card Fraud Detection Using Hybrid Proximal Policy Optimization and Artificial Bee Colony Optimization with Mutual Learning.
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| Title: | Credit Card Fraud Detection Using Hybrid Proximal Policy Optimization and Artificial Bee Colony Optimization with Mutual Learning. |
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| Authors: | Yuanyuan Zhang1 zhangyy4935@163.com |
| Source: | Informatica (03505596). Mar2026, Vol. 50 Issue 1, p3-23. 21p. |
| Subjects: | Credit card fraud, Bees algorithm, Reinforcement learning, Online marketplaces, Artificial neural networks |
| Abstract (English): | The surge in e-commerce has intensified credit cardfraud, resulting in massive global losses and creating an urgent need for stronger detection systems. This research presents an advanced model for detecting credit cardfraud to address challenges often overlooked, such as class imbalance and sensitivity to initial parameter settings. Our model leverages an artificial neural network (ANN) to extract feature vectors necessary for accurate fraud detection. We utilize proximal policy optimization (PPO) to address class imbalance during training of the ANN. PPO improves the treatment of minority classes by assigning higher rewards for correct predictions and more substantial penalties for errors. This approach leads to more balanced learning. Additionally, our model incorporates a mutual learning-based artificial bee colony (ML-ABC) algorithm for efficiently pre-training the parameters of the ANN. Experiments on the Universite Libre de Bruxelles credit card dataset show that the proposed approach achieves 90.197% accuracy and an F-measure of 91.287%. It outperforms the best existing method by about 3%. These results highlight the robustness of the model and its potential for real-world e-commerce fraud detection. [ABSTRACT FROM AUTHOR] |
| Abstract (Slovenian): | Raziskava predstavlja napreden model za zaznavanje goljufij s kreditnimi karticami, ki izboljšuje natančnost in presega obstoječe metode. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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