Credit Card Fraud Detection Using Convolutional Neural Networks
Credit card is becoming more and more popular in financial transactions, at the same time frauds are also increasing. Conventional methods use rule-based expert systems to detect fraud behaviors, neglecting diverse situations, extreme imbalance of positive and negative samples. In this paper, we propose a CNN-based fraud detection framework, to capture the intrinsic patterns of fraud behaviors learned from labeled data. Abundant transaction data is represented by a feature matrix, on which a convolutional neural network is applied to identify a set of latent patterns for each sample. Experiments on real-world massive transactions of a major commercial bank demonstrate its superior performance compared with some state-of-the-art methods.
KeywordsCredit card fraud Convolutional neural network Imbalanced data
The work was supported by the National Natural Science Foundation of China (61272251), the Key Basic Research Program of Shanghai Municipality, China (15JC1400103) and the National Natural Science Foundation of China (91420302).
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