# A Combination of Deep Neural Networks and K-Nearest Neighbors for Credit Card Fraud Detection

arXiv: https://arxiv.org/abs/2205.15300 · HTML: https://mlsanity.com/paper/2205.15300/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 28 references that are in the map.
- First posted on arXiv 27 May 2022.

## Builds on (most cited first, 1 of 1)

- [Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks](https://mlsanity.com/paper/1908.00734/) (2019)

## Abstract (arXiv)

Detection of a Fraud transaction on credit cards became one of the major problems for financial institutions, organizations and companies. As the global financial system is highly connected to non-cash transactions and online operations fraud makers invent more effective ways to access customers' finances. The main problem in credit card fraud detection is that the number of fraud transactions is significantly lower than genuine ones. The aim of the paper is to implement new techniques, which contains of under-sampling algorithms, K-nearest Neighbor Algorithm (KNN) and Deep Neural Network (KNN) on new obtained dataset. The performance evaluation showed that DNN model gives precise high accuracy (98.12%), which shows the good ability of presented method to detect fraudulent transactions.