Spurious credit card transactions are a significant source of financial losses and urge the development of accurate fraud detection algorithms. In this paper, we use machine learning strategies for such an aim. First, we apply a mixed learning technique that uses K-means preprocessing before trained classification to the problem at hand. Next, we introduce an adapted detector ensemble technique that uses OR-logic algorithm aggregation to enhance the detection rate. Then, both strategies are deployed in tandem in numerical simulations using real-world transactions data. We observed from simulation results that the proposed methods diminished computational cost and enhanced performance concerning state-of-the-art techniques.
@article{arxiv.2112.02627,
title = {Ensemble and Mixed Learning Techniques for Credit Card Fraud Detection},
author = {Daniel H. M. de Souza and Claudio J. Bordin},
journal= {arXiv preprint arXiv:2112.02627},
year = {2021}
}