{"type":"ScholarlyArticle","title":"Federated Meta-Learning for Fraudulent Credit Card Detection","authors":[{"name":"Wenbo Zheng","slug":"wenbo-zheng","given":"Wenbo","family":"Zheng","affiliation":"School of Software Engineering , Xi'an Jiaotong University; The State Key Laboratory for Management and Control of Complex Systems , Institute of Automation , Chinese Academy of Sciences"},{"name":"Lan Yan","slug":"lan-yan","given":"Lan","family":"Yan","affiliation":"The State Key Laboratory for Management and Control of Complex Systems , Institute of Automation , Chinese Academy of Sciences; School of Artificial Intelligence , University of Chinese Academy of Sciences"},{"name":"Chao Gou","slug":"chao-gou","given":"Chao","family":"Gou","affiliation":"School of Intelligent Systems Engineering , Sun Yat-sen University"},{"name":"Fei-Yue Wang","slug":"fei-yue-wang","given":"Fei-Yue","family":"Wang","affiliation":"The State Key Laboratory for Management and Control of Complex Systems , Institute of Automation , Chinese Academy of Sciences; School of Artificial Intelligence , University of Chinese Academy of Sciences"}],"abstract":"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.","doi":"10.24963/ijcai.2020/642","url":"https://ijcai.org/proceedings/2020/642","pdf":"https://ijcai.org/proceedings/2020/0642.pdf","markdown":"https://ijcai.org/proceedings/2020/642.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/642","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Special Track on AI in FinTech","track":"Special Track on AI in FinTech","number":642,"pages":{"first":4654,"last":4660},"keywords":["AI for regulation: AI for financial fraud detection","AI for payment: AI for payment risk modeling","AI for banking: AI for credit analysis and pricing","AI for regulation: AI for financial crime detection","AI for banking: AI for banking risk and fraud modeling"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/23993/file_23993.m3u8","length_sec":298,"provider":"hls"}],"references":[{"position":1,"raw":"[Bahnsen et al., 2014] Alejandro Correa Bahnsen, Aleksan- dar Stojanovic, Djamila Aouada, and Björn Ottersten. 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