Ensemble and Mixed Learning Techniques for Credit Card Fraud Detection
This addresses financial losses from fraud for credit card systems, but it appears incremental as it builds on existing machine learning strategies.
The paper tackled credit card fraud detection by applying a mixed learning technique with K-means preprocessing and an adapted ensemble detector using OR-logic aggregation, resulting in reduced computational cost and enhanced performance compared to state-of-the-art techniques.
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.