Federated Meta-Learning for Fraudulent Credit Card Detection

Federated Meta-Learning for Fraudulent Credit Card Detection

Wenbo Zheng, Lan Yan, Chao Gou, Fei-Yue Wang

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Special Track on AI in FinTech. Pages 4654-4660. https://doi.org/10.24963/ijcai.2020/642

Credit card transaction fraud costs billions of dollars to card issuers every year. Besides, the credit card transaction dataset is very skewed, there are much fewer samples of frauds than legitimate transactions. Due to the data security and privacy, different banks are usually not allowed to share their transaction datasets. These problems make traditional model difficult to learn the patterns of frauds and also difficult to detect them. In this paper, we introduce a novel framework termed as federated meta-learning for fraud detection. Different from the traditional technologies trained with data centralized in the cloud, our model enables banks to learn fraud detection model with the training data distributed on their own local database. A shared whole model is constructed by aggregating locallycomputed updates of fraud detection model. Banks can collectively reap the benefits of shared model without sharing the dataset and protect the sensitive information of cardholders. To achieve the good performance of classification, we further formulate an improved triplet-like metric learning, and design a novel meta-learning-based classifier, which allows joint comparison with K negative samples in each mini-batch. Experimental results demonstrate that the proposed approach achieves significantly higher performance compared with the other state-of-the-art approaches.
Keywords:
AI for banking: AI for banking risk and fraud modeling
AI for banking: AI for credit analysis and pricing
AI for payment: AI for payment risk modeling
AI for regulation: AI for financial crime detection
AI for regulation: AI for financial fraud detection