Enhancing Credit Card Fraud Detection A Neural Network and SMOTE Integrated Approach

TL;DR

This paper introduces a combined Neural Network and SMOTE approach to improve credit card fraud detection by effectively handling data imbalance, resulting in higher accuracy and robustness compared to traditional methods.

Contribution

It presents a novel integration of Neural Networks with SMOTE to enhance fraud detection performance on imbalanced datasets.

Findings

Higher precision, recall, and F1-score compared to traditional models

Effective handling of imbalanced credit card transaction data

Potential for improved financial transaction security

Abstract

Credit card fraud detection is a critical challenge in the financial sector, demanding sophisticated approaches to accurately identify fraudulent transactions. This research proposes an innovative methodology combining Neural Networks (NN) and Synthet ic Minority Over-sampling Technique (SMOTE) to enhance the detection performance. The study addresses the inherent imbalance in credit card transaction data, focusing on technical advancements for robust and precise fraud detection. Results demonstrat e that the integration of NN and SMOTE exhibits superior precision, recall, and F1-score compared to traditional models, highlighting its potential as an advanced solution for handling imbalanced datasets in credit card fraud detection scenarios. This rese arch contributes to the ongoing efforts to develop effective and efficient mechanisms for safeguarding financial transactions from fraudulent activities.