Credit card fraud detection using machine learning techniques: A comparative analysis

By John Awoyemi, Adebayo Olusola Adetunmbi, Samuel Adebayo Oluwadare. 2017 International Conference on Computing Networking and Informatics (ICCNI), 2017

486 citations recorded by the source corpus.

Subfields: Accounting, Artificial Intelligence, Political Science and International Relations

View Paper: https://www.semanticscholar.org/paper/d65c2cbc0980d0840b88b569516ae9c277d9d200

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Accounting

1. Yanxia Sun
2. A. Taha
3. Emmanuel Ileberi
4. Zenghui Wang
5. Sharaf Jameel Malebary
6. Ibomoiye Domor Mienye

In Artificial Intelligence

1. Yanxia Sun
2. A. Taha
3. Emmanuel Ileberi
4. Zenghui Wang
5. Sharaf Jameel Malebary
6. Ibomoiye Domor Mienye
7. Grace W. Lindsay

In Political Science and International Relations

1. Emmanuel Ileberi
2. Yanxia Sun
3. Zenghui Wang

Citations in the corpus

• An Intelligent Approach to Credit Card Fraud Detection Using an Optimized Light Gradient Boosting Machine (2020, background): “More comprehensive reviews can be found in [8]–[11].”
• A machine learning based credit card fraud detection using the GA algorithm for feature selection (2022, methodology): “[16] presented a comparison analysis of different ML methods on the European cardholders credit card fraud dataset.”