Abstract
<p>The global online communication channel made possible with the internet has increased credit card fraud leading to huge loss of monetary fund in their billions annually for consumers and financial institutions. The fraudsters constantly devise new strategy to perpetrate illegal transactions. As such, innovative detection systems in combating fraud are imperative to curb these losses. This paper presents the combination of multiple classifiers through stacking ensemble technique for credit card fraud detection. The fuzzy-rough nearest neighbor (FRNN) and sequential minimal optimization (SMO) are employed as base classifiers. Their combined prediction becomes data input for the meta-classifier, which is logistic regression (LR) resulting in a final predictive outcome for improved detection. Simulation results compared with seven other algorithms affirms that ensemble model can adequately detect credit card fraud with detection rates of 84.90% and 76.30%.</p>
Highlights
The motive that drives fraud is for criminal purposes
A comparative analysis of algorithms used mostly for credit card fraud detection was conducted in the work by Ref. [16] that involves logistic regression, decision tree, and random forest
This paper presents a stacking ensemble classification model based on fuzzy-rough nearest neighbor algorithm, sequential minimal optimization, and logistic regression for credit card fraud detection
Summary
The motive that drives fraud is for criminal purposes. This act of pursuing committing fraud is basically for siphoning money illegally that leads to loss of financial or personal gain [1]. There are different numbers of techniques that have been proposed and developed for tackling fraud detection. They comprise of Bayesian network, Markov model, decision tree, support vector machines, and a host of algorithms that are nature-inspired [8]–[12]. An alternative method for the detection of credit card fraud is proposed based on stacking ensemble technique. It adopted machine learning algorithms of fuzzy-rough nearest neighbor (FRNN), sequential minimal optimization (SMO), and logistic regression (LR). The organization of the paper is as follows: Section 2 summarizes relevant literatures in relation to credit card fraud detection.
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