# Credit Card Fraud Detection Using Asexual Reproduction Optimization

arXiv: https://arxiv.org/abs/2306.01008 · HTML: https://mlsanity.com/paper/2306.01008/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 140 references that are in the map.
- First posted on arXiv 31 May 2023.

## Builds on (most cited first, 6 of 6)

- [Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs](https://mlsanity.com/paper/1909.01185/) (2019)
- [Process Comparison Using Object-Centric Process Cubes](https://mlsanity.com/paper/2103.07184/) (2021)
- [A Scalable Database for the Storage of Object-Centric Event Logs](https://mlsanity.com/paper/2202.05639/) (2022)
- [A Python Tool for Object-Centric Process Mining Comparison](https://mlsanity.com/paper/2202.05709/) (2022)
- [Predictive Object-Centric Process Monitoring](https://mlsanity.com/paper/2207.10017/) (2022)
- [Clustering Object-Centric Event Logs](https://mlsanity.com/paper/2207.12764/) (2022)

## Abstract (arXiv)

As the number of credit card users has increased, detecting fraud in this domain has become a vital issue. Previous literature has applied various supervised and unsupervised machine learning methods to find an effective fraud detection system. However, some of these methods require an enormous amount of time to achieve reasonable accuracy. In this paper, an Asexual Reproduction Optimization (ARO) approach was employed, which is a supervised method to detect credit card fraud. ARO refers to a kind of production in which one parent produces some offspring. By applying this method and sampling just from the majority class, the effectiveness of the classification is increased. A comparison to Artificial Immune Systems (AIS), which is one of the best methods implemented on current datasets, has shown that the proposed method is able to remarkably reduce the required training time and at the same time increase the recall that is important in fraud detection problems. The obtained results show that ARO achieves the best cost in a short time, and consequently, it can be considered a real-time fraud detection system.