# Credit Card Fraud Detection Using Autoencoder Neural Network

arXiv: https://arxiv.org/abs/1908.11553 · HTML: https://mlsanity.com/paper/1908.11553/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2019.

## Built on by (most cited first, 4 of 4)

- [Dataset Bias Mitigation Through Analysis of CNN Training Scores](https://mlsanity.com/paper/2106.14829/) (2021)
- [Data Leakage and Deceptive Performance: A Critical Examination of Credit Card Fraud Detection Methodologies](https://mlsanity.com/paper/2506.02703/) (2025)
- [Deep Semi-Supervised Anomaly Detection for Finding Fraud in the Futures Market](https://mlsanity.com/paper/2309.00088/) (2023)
- [Improving Credit Card Fraud Detection with an Optimized Explainable Boosting Machine](https://mlsanity.com/paper/2602.06955/) (2026)

## Abstract (arXiv)

Imbalanced data classification problem has always been a popular topic in the field of machine learning research. In order to balance the samples between majority and minority class. Oversampling algorithm is used to synthesize new minority class samples, but it could bring in noise. Pointing to the noise problems, this paper proposed a denoising autoencoder neural network (DAE) algorithm which can not only oversample minority class sample through misclassification cost, but it can denoise and classify the sampled dataset. Through experiments, compared with the denoising autoencoder neural network (DAE) with oversampling process and traditional fully connected neural networks, the results showed the proposed algorithm improves the classification accuracy of minority class of imbalanced datasets.