A machine learning based credit card fraud detection using the GA algorithm for feature selection

TLDR

E‑commerce and e‑payment growth has increased credit‑card fraud, making effective detection mechanisms essential, especially when selecting features for machine‑learning models. The study proposes a machine‑learning credit‑card fraud detection engine that uses a genetic algorithm for feature selection. The engine selects features via a genetic algorithm, then applies Decision Tree, Random Forest, Logistic Regression, Artificial Neural Network, and Naive Bayes classifiers, and is evaluated on a European cardholder dataset. The approach outperforms existing systems.

Abstract

Abstract The recent advances of e-commerce and e-payment systems have sparked an increase in financial fraud cases such as credit card fraud. It is therefore crucial to implement mechanisms that can detect the credit card fraud. Features of credit card frauds play important role when machine learning is used for credit card fraud detection, and they must be chosen properly. This paper proposes a machine learning (ML) based credit card fraud detection engine using the genetic algorithm (GA) for feature selection. After the optimized features are chosen, the proposed detection engine uses the following ML classifiers: Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), Artificial Neural Network (ANN), and Naive Bayes (NB). To validate the performance, the proposed credit card fraud detection engine is evaluated using a dataset generated from European cardholders. The result demonstrated that our proposed approach outperforms existing systems.