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Improving Card Fraud Detection Through Suspicious Pattern Discovery.

, , , , and . IEA/AIE (2), volume 10351 of Lecture Notes in Computer Science, page 181-190. Springer, (2017)

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Understanding Telecom Customer Churn with Machine Learning: From Prediction to Causal Inference., , , , and . BNAIC/BENELEARN (Selected Papers), volume 1196 of Communications in Computer and Information Science, page 182-200. Springer, (2019)Combining unsupervised and supervised learning in credit card fraud detection., , , , , and . Inf. Sci., (2021)An Adversary Model of Fraudsters' Behavior to Improve Oversampling in Credit Card Fraud Detection., , , and . IEEE Access, (2023)Adversarial Learning in Real-World Fraud Detection: Challenges and Perspectives., , , and . DEC@SIGMOD, page 27-33. ACM, (2023)Credit card fraud detection and concept-drift adaptation with delayed supervised information., , , , and . IJCNN, page 1-8. IEEE, (2015)Embeddings of Categorical Variables for Sequential Data in Fraud Context., , and . AMLTA, volume 723 of Advances in Intelligent Systems and Computing, page 542-552. Springer, (2018)Online Non-linear Gradient Boosting in Multi-latent Spaces., , , , and . IDA, volume 11191 of Lecture Notes in Computer Science, page 99-110. Springer, (2018)Multiple perspectives HMM-based feature engineering for credit card fraud detection., , , , , , and . SAC, page 1359-1361. ACM, (2019)A Selecting-the-Best Method for Budgeted Model Selection., and . ECML/PKDD (1), volume 6911 of Lecture Notes in Computer Science, page 249-262. Springer, (2011)When is Undersampling Effective in Unbalanced Classification Tasks?, , and . ECML/PKDD (1), volume 9284 of Lecture Notes in Computer Science, page 200-215. Springer, (2015)