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

2024/02/27 by Mengran Zhu, Zhu, Mengran, Ye Zhang +7 · 1 citation
Business, Management and Accounting · Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Computational Engineering #FOS: Computer and information sciences #Finance #Financial Distress and Bankruptcy Prediction #Imbalanced Data Classification Techniques #Vehicle License Plate Recognition #and Science (cs.CE)

paper · pdf · doi:10.48550/arxiv.2405.00026

openalex publication_date 2024/02/27 · openalex created_date 2024/05/03 · openalex updated_date 2026/07/28

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.

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