Random Forest Classifier for Credit Card Fraud Detection
Author(s):
Sagar Yadav, Vijay Gaikwad, Suvarna Mane, Disha Chandak, Om Pardeshi, Tanaya Barawkar, Gopal Dhanpalwar
Keywords:
fraud detection, random forest algorithm, machine learning, credit card fraud detection, isolation forest algorithm.
Abstract
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

Conference Alert

NCSST-2021

AICTE Sponsored National Conference on Smart Systems and Technologies

Last Date: 25th November 2021

SWEC- Management

LATEST INNOVATION’S AND FUTURE TRENDS IN MANAGEMENT

Last Date: 7th November 2021

Go To Issue



Call For Paper

Volume 8 Issue 4

Last Date 25 September 2021

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

Contact Details

Telephone:6351679790
Email: editor@ijirt.org
Website: ijirt.org

Policies