IJNRD Research Journal

WhatsApp
Click Here

WhatsApp editor@ijnrd.org
IJNRD
INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)

Call For Paper

For Authors

Forms / Download

Published Issue Details

Editorial Board

Other IMP Links

Facts & Figure

Impact Factor : 8.76

Issue per Year : 12

Volume Published : 9

Issue Published : 94

Article Submitted :

Article Published :

Total Authors :

Total Reviewer :

Total Countries :

Indexing Partner

Join RMS/Earn 300

Licence

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Published Paper Details
Paper Title: Credit Card Fraud Detection Using AI/ML/CNN
Authors Name: Parthib Ranjan Ray , Dr. R. Renuka Devi
Download E-Certificate: Download
Author Reg. ID:
IJNRD_188949
Published Paper Id: IJNRD2303287
Published In: Volume 8 Issue 3, March-2023
DOI: http://doi.one/10.1729/Journal.33412
Abstract: In this new era of digital payments gaining momentum and a cashless world due to the current ongoing pandemic most of the payments have gone online rather than physical payments being the first choice in pre pandemic years. But as it is said every coin has two sides, credit card payments are highly risky and frauds can easily be committed by hackers and fraudsters to siphon off money from peoples account for their own personal gains. So to combat this a fraud detection machine is put in place for banks to detect such frauds and counter it accordingly. This fraud detection model is created using upcoming technologies like CNN(convolutional neural networks),Machine Learning which come under the canopy of Artificial Intelligence(AI). This model if used in a large scale on a commercial basis can reduce fraud rates to a very minimal level with a precision of about 99%. The added feature in this model is that using various contemporary machine learning algorithms and with the help of some data rectifiers the user will be able to graphically analyze the fraud rate using feature importance graphs to name a few. This software is an upgraded version of the conventional fraud detection machines currently in use in financial institutions.
Keywords: Fraud, Machine Learning, Machine Learning Models, Sampling techniques, Preprocessing, AI, Precision, Accuracy, Test Data, Training Data, Threshold of Tolerance, Weighted Average, Convolutional Neural Networks, Feature Importance.
Cite Article: "Credit Card Fraud Detection Using AI/ML/CNN", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 3, page no.c767-c772, March-2023, Available :http://www.ijnrd.org/papers/IJNRD2303287.pdf
Downloads: 000118746
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publication Details: Published Paper ID:IJNRD2303287
Registration ID: 188949
Published In: Volume 8 Issue 3, March-2023
DOI (Digital Object Identifier): http://doi.one/10.1729/Journal.33412
Page No: c767-c772
Country: Nagpur, Maharashtra, India
Research Area: Engineering
Publisher : IJ Publication
Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2303287
Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2303287
Share Article:
Share

Click Here to Download This Article

Article Preview
Click Here to Download This Article

Major Indexing from www.ijnrd.org
Semantic Scholar Microsaoft Academic ORCID Zenodo
Google Scholar ResearcherID Thomson Reuters Mendeley : reference manager Academia.edu
arXiv.org : cornell university library Research Gate CiteSeerX PUBLON
DRJI SSRN Scribd DocStoc

ISSN Details

ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
Journal Starting Year (ESTD) : 2016

DOI (A digital object identifier)


Providing A digital object identifier by DOI
How to Get DOI? DOI

Conference

Open Access License Policy

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Creative Commons License This material is Open Knowledge This material is Open Data This material is Open Content

Important Details

Social Media

Licence

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Join RMS/Earn 300

IJNRD