Random Forest Classifier for Credit Card Fraud Detection
Sagar Yadav, Vijay Gaikwad, Suvarna Mane, Disha Chandak, Om Pardeshi, Tanaya Barawkar, Gopal Dhanpalwar
fraud detection, random forest algorithm, machine learning, credit card fraud detection, isolation forest algorithm.
Credit card fraud (CCF) is a straightforward and appealing target. Online sites as well as E- commerce have enlarged their payment options, as risk of online fraud is increasing rapidly. Researchers began using various machine learning (ML) algorithms to detect and analyses online transaction fraud as fraud rates increased. This paper presents a random forest-based model to detect fraudulent transactions by analyzing customers' historical transaction details and extracting behavioral patterns. Cardholders are divided into groups based on the volume of their transactions. Then, using a sliding window method, the transactions performed by cardholders are aggregated from various categories. The behavioral patterns of the various groupings are then derived. The random forest (RF) classifier shows the greatest accuracy and hence proved to be one of the most excellent ways for detection/prediction of frauds. As a result, a feedback mechanism is implemented to address the issue of notion drift. The proposed model provides high accuracy of 99.99% with precision of 93% and recall of 73%. The proposed model provides better performance than isolation forest algorithm as well logistic regression and support vector machine.
Article Details
Unique Paper ID: 157570

Publication Volume & Issue: Volume 9, Issue 7

Page(s): 587 - 593
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 11 Issue 1

Last Date for paper submitting for Latest 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