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Profit-based Logistic Regression: A Case Study in Credit Card Fraud Detection

Authors:
Azamat Kibekbaev
Ekrem Duman

Keywords: Fraud detection; Profit-based Logistic regression; MLE; cost function

Abstract:
Credit card fraud is a serious and growing problem which became increasingly rampant in recent years. In practice, many predictive models are used to identify fraudulent transactions. In this study, we developed a new profit-based logistic regression model. In order to do this, we modified the cost function in Maximum Likelihood Estimator (MLE) by changing its values according to the profit of each instance. We did this in four different scenarios and tested the results on real-life data of credit card transactions from an international Turkish bank. According to our findings, original Logistic Regression (LR) has the best performance in terms of TP rate. In terms of saving or net profit, profit-based LR scenarios outperformed others.

Pages: 101 to 105

Copyright: Copyright (c) IARIA, 2015

Publication date: July 19, 2015

Published in: conference

ISSN: 2308-4464

ISBN: 978-1-61208-423-7

Location: Nice, France

Dates: from July 19, 2015 to July 24, 2015