Ensemble and Mixed Learning Techniques for Credit Card Fraud Detection

• URL: http://arxiv.org/abs/2112.02627v1
• Date: Sun, 5 Dec 2021 17:17:04 GMT
• Title: Ensemble and Mixed Learning Techniques for Credit Card Fraud Detection
• Authors: Daniel H. M. de Souza and Claudio J. Bordin Jr
• Abstract summary: We use a mixed learning technique that uses K-means preprocessing before trained classification to the problem at hand. We introduce an adapted detector ensemble technique that uses OR-logic algorithm aggregation to enhance the detection rate. We observed from simulation results that the proposed methods diminished computational cost and enhanced performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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