Credit Card Fraud Detection Using Machine Learning
Author(s):
A.S.Malini, J.M Shajitha Banu, M.I Sharmila Fathima
Keywords:
Anomaly Detection, Isolation Forest, Credit Card Fraud Detection, Classification using Machine Learning.
Abstract
Anomaly Detection is a way of recognizing suspicious occurrences of events and data items that may cause difficulties for the authorities. Security difficulties, server breakdowns, bank fraud, building structural weaknesses, clinical abnormalities, and other issues are often related with data anomalies. In today's digital money milieu, credit card fraud has become a big and major concern. These transactions are carried out with such finesse that they resemble legal transactions. As a result, the goal of this research work is to create an autonomous, highly efficient classifier for fraud detection that can detect fraudulent credit card transactions. Many fraud detection strategies and models have been proposed by researchers, including the use of various algorithms to identify fraud trends. In this paper, we look at the Isolation forest, which is a machine learning approach used to train the system using H2O.ai. In the domain of anomaly detection, the Isolation Forest was not widely utilized or researched. The version's overall performance was tested largely using commonly established metrics: accuracy and recall. Kaggle provided the test data for our study.
Article Details
Unique Paper ID: 155230

Publication Volume & Issue: Volume 9, Issue 1

Page(s): 200 - 205
Article Preview & Download


Share This Article

Join our RMS

Conference Alert

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024

Last Date: 15th March 2024

Call For Paper

Volume 10 Issue 10

Last Date for paper submitting for March Issue is 25 June 2024

About Us

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org

Social Media

Google Verified Reviews