Credit Card Fraud Detection Using Enhanced Random Forest Classifier for Imbalanced Data

TL;DR

This paper presents an enhanced random forest classifier combined with SMOTE to effectively detect credit card fraud in imbalanced datasets, achieving high accuracy and F1-score.

Contribution

The study introduces an improved RF classifier with hyperparameter tuning and SMOTE for better fraud detection in imbalanced credit card transaction data.

Findings

Achieved 98% accuracy in fraud detection

Attained 98% F1-score, demonstrating high model performance

Proposed model effectively handles imbalanced datasets

Abstract

The credit card has become the most popular payment method for both online and offline transactions. The necessity to create a fraud detection algorithm to precisely identify and stop fraudulent activity arises as a result of both the development of technology and the rise in fraud cases. This paper implements the random forest (RF) algorithm to solve the issue in the hand. A dataset of credit card transactions was used in this study. The main problem when dealing with credit card fraud detection is the imbalanced dataset in which most of the transaction are non-fraud ones. To overcome the problem of the imbalanced dataset, the synthetic minority over-sampling technique (SMOTE) was used. Implementing the hyperparameters technique to enhance the performance of the random forest classifier. The results showed that the RF classifier gained an accuracy of 98% and about 98% of F1-score value, which is promising. We also believe that our model is relatively easy to apply and can overcome the issue of imbalanced data for fraud detection applications.