Credit card fraud detection: A fusion approach using Dempster-Shafer theory and Bayesian learning

By S. Panigrahi, Amlan Kundu, Shamik Sural, Arun Kumar Majumdar. Information Fusion, 2009

365 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/c749796328e8b20ab31665d3cd52aba2665488ac

How researchers see this paper

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

In Artificial Intelligence

1. Asoke Kumar Nandi
2. Chee Peng Lim
3. Manjeevan Seera
4. John Awoyemi
5. Adebayo Olusola Adetunmbi
6. Samuel Adebayo Oluwadare

In Electrical and Electronic Engineering

1. Asoke Kumar Nandi
2. John Awoyemi
3. Adebayo Olusola Adetunmbi
4. Samuel Adebayo Oluwadare

Citations in the corpus

• Credit card fraud detection using machine learning techniques: A comparative analysis (2017, methodology): “A fusion approach using Dempster–Shafer theory and Bayesian learning for detecting credit card fraud is proposed [27].”
• Intelligent financial fraud detection: A comprehensive review (2016, neutral): “Data mining has already been shown to be useful in similar domains such as credit card approval, bankruptcy prediction, and analysis of share markets [35].”
• Credit Card Fraud Detection Using AdaBoost and Majority Voting (2019, methodology): “Simulation results indicated a 98% true positive rate [8].”