Credit Card Fraud Detection

Main Article Content

Kumud Sharma
Manish Kumar
Shalini Tiwari
Malpani Vijay Rakesh
Mahamad Shafeek Yalagi

Abstract

Abstract---In the current economic scenario, credit card use has become extremely important. They enable the user to perform transactions of large sums of money without the requirement to carry cash for payments. They have revolutionized the path of making cashless transactions and have made it easy in making payments convenient for the buyer. This digitized form of payment is extremely beneficial but comes with its own set of shortcomings. With constant increase in number of users, credit card frauds are also increasing at a commensurate pace.Billions of dollars of losshaveresulted every year by illegitimate credit card payments. The development of effective and efficient fraud detection models is key to reducing these losses, and more algorithms depend on advanced machine learning methods to help fraud investigators. As the obtainable credit card fraud data is highly imbalanced. In this paper we are overcoming this deficiency by balancing out the data and bringing out the best algorithm that segregates the transaction efficiently.

 

Downloads

Download data is not yet available.

Article Details

Section
Articles

References

PradheepanRaghavan, Neamat El Gaya: Fraud Detection using Machine Learning and Deep Learning [IJERT 2019] International Conference on Computational Intelligence and Knowledge Economy (ICCIKE) December 11–12, 2019, Amity University Dubai, UAE

RuttalaSailusha, V.Gnaneswar, R. Ramesh, G. RamakoteswaraRao:Credit Card Fraud Detection using Machine Learning [IEEE 2020] Part Number:CFP20K74-ART; ISBN: 978-1-7281-4876-2

SamidhaKhatri, AishwaryaArora, ArunPrakashAgrawa: Supervised Machine Learning Algorithms for Credit Card Fraud Detection: A Comparison 978-l-7281-2791-0/20/$31.00 ©2020 IEEE

Ashwinkumar.U.M and Dr.Anandakumar K.R, "Predicting Early Detection of cardiac and Diabetes symptoms using Data mining techniques", International conference on computer Design and Engineering, vol.49, 2012

AlaeChouiekha, EL HassaneIbn EL Haj. “ConvNets for Fraud Detection analysisâ€. Procedia Computer Science 127, pp.133–138. 2018

OlawaleAdepoju, Julius Wosowei, Shiwanilawte, HemaintJaiman:Comparative Evaluation of Credit Card Fraud DetectionUsing Machine Learning Techniques [IEEE 2019].Global Conference for Advancement in Technology (GCAT) Bangalore, India. Oct 18-20, 2019