Credit Card Fraud Detection Using Fuzzy Rough Nearest Neighbor and Sequential Minimal Optimization with Logistic Regression

Credit Card Fraud Detection Using Fuzzy Rough Nearest Neighbor and Sequential Minimal Optimization with Logistic Regression

Fraud detection Credit card Ensemble technique Stacking Machine learning

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%.

Saleh Hussein, Ameer

Salah Khairy, Rihab

Mohamed Najeeb, Shaima Miqdad

Alrikabi, Haider Th.Salim

International Journal of Interactive Mobile Technologies (iJIM); Vol. 15 No. 05 (2021); pp. 24-42

1865-7923

International Association of Online Engineering (IAOE), Vienna, Austria

2021-03-16

Copyright (c) 2021 Haider Th.Salim Alrikabi, Ameer Saleh Hussein, Rihab Salah Khairy

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Peer-reviewed Article