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Fraud Detection

Reference Title Data source (open-sourced?) Model Type Evaluation Metric(s) Time Span Primary Research Problem Venue
Zheng et al. (2020) Federated Meta-Learning for Fraudulent Credit Card Detection European Credit Card (ECC) (NOT open source),Kaggle (open source) ResNet-34 architecture and 8-layer CNN Compared the proposed model with 10 state-of-the-art models and outperformed all of them. \ Fraudulent credit card detection IJCAI-20
Wang (2020) The Behavioral Sign of Account Theft: Realizing Online Payment Fraud Alert 3.5 million B2C transaction records from a commercial bank (NOT open source) XGBoost (performs best), Random Forest, Logistic regression, Deep NN(3-layer) XGB: precision 0.97, recall 0.92, f1 score 0.95,FPR:0.0005 01/04/2017- 30/06/2017 Ex-ante fraud detection, input historical transaction sequence (17 user-features and 37 online payment features), output risk score IJCAI-20
Wang & Wellman (2020) Market Manipulation: An Adversarial Learning Framework for Detection and Evasion \ \ A manipulator can easily fool an existing detector with adversarially generated manipulation streams. But our regulator can detect these streams \ Employ an adversarial learning framework which can generate market manipulating streams IJCAI-20
Fawcett & Provost (1997) Adaptive Fraud Detection Records of sell phone calls by users in the New York City area Rule learning Accuracy 92%, Test set cost: $5403 \ One of the earliest papers that apply data mining technology on automatic fraud detection Data Mining and Knowledge Discovery.
Chan & Stolfo (1998) Toward Scalable Learning with Non-uniform Class and Cost Distributions: A Case Study in Credit Card Fraud Detection A dataset from Chase Manhattan Bank Multi-classifier meta-learning TP,TN,FP,FN -/10/1995- -/09/1996 One of the earliest papers that apply machine learning on credit card fraud detection International Conference on Knowledge Discovery and Data Mining (1998)