부산대학교 도서관

Every second, Fraud Detection Systems (FDS) check streams of thousands of credit or debit card transactions. Most of the models in production and in the literature rely on batch learning, wasting part of the most recent data. Incremental learning may be beneficial in terms of compu-tational/architectural cost and confidentiality (e.g. avoiding the storage of sensitive data). The full paper focuses on two questions related to the use of incremental learning for FDS: (1) Can incremental learning be as competitive as batch and retraining approaches? (2) Can combining incremental, ensembles, diversity, and transfer learning lead to efficient models under concept drift? We investigate those elements and provide an experimental evaluation on a real-life case study including more than 150 days of e-commerce transactions (or 50 million transactions).