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

arXiv: https://arxiv.org/abs/2405.00026 · HTML: https://mlsanity.com/paper/2405.00026/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 27 Feb 2024.

## Builds on (most cited first, 3 of 3)

- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [A Semi-supervised Graph Attentive Network for Financial Fraud Detection](https://mlsanity.com/paper/2003.01171/) (2020)
- [DeepGI: An Automated Approach for Gastrointestinal Tract Segmentation in MRI Scans](https://mlsanity.com/paper/2401.15354/) (2024)

## Built on by (most cited first, 5 of 5)

- [Image anomaly detection and prediction scheme based on SSA optimized ResNet50-BiGRU model](https://mlsanity.com/paper/2406.13987/) (2024)
- [Utilizing the LightGBM Algorithm for Operator User Credit Assessment Research](https://mlsanity.com/paper/2403.14483/) (2024)
- [Improving Credit Card Fraud Detection through Transformer-Enhanced GAN Oversampling](https://mlsanity.com/paper/2509.19032/) (2025)
- [Improving Credit Card Fraud Detection with an Optimized Explainable Boosting Machine](https://mlsanity.com/paper/2602.06955/) (2026)
- [EmDT: Embedding Diffusion Transformer for Tabular Data Generation in Fraud Detection](https://mlsanity.com/paper/2603.13566/) (2026)

## Abstract (arXiv)

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.