Performance Analysis of Machine Learning Algorithms in Credit Cards Fraud Detection

Vinod Jain, Mayank Agrawal, Anuj Kumar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

39 Scopus citations

Abstract

Credit cards are very commonly used in making online payments. In recent years' frauds are reported which are accomplished using credit cards. It is very difficult to detect and prevent the fraud which is accomplished using credit card. Machine Learning(ML) is an Artificial Intelligence (AI) technique which is used to solve many problems in science and engineering. In this paper, machine learning algorithms are applied on a data set of credit cards frauds and the power of three machine learning algorithms is compared to detect the frauds accomplished using credit cards. The accuracy of Random Forest machine learning algorithm is best as compared to Decision Tree and XGBOOST algorithms.

Original languageEnglish
Title of host publicationICRITO 2020 - IEEE 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)
PublisherInstitute of Electrical and Electronics Engineers
Pages86-88
Number of pages3
ISBN (Electronic)9781728170169
DOIs
StatePublished - 1 Jun 2020
Externally publishedYes
Event8th IEEE International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2020 - Noida, India
Duration: 4 Jun 20205 Jun 2020

Publication series

NameICRITO 2020 - IEEE 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)

Conference

Conference8th IEEE International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2020
Country/TerritoryIndia
CityNoida
Period4/06/205/06/20

Keywords

  • Artificial Intelligence
  • Credit Cards Fraud Detection
  • Machine Learning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Signal Processing
  • Software
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Waste Management and Disposal
  • Control and Optimization

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