International Journal of Soft Computing And Artificial Intelligence (IJSCAI)
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Volume-12,Issue-1  ( May, 2024 )
Statistics report
Jun. 2024
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 22
Paper Published : 242
No. of Authors : 695
  Journal Paper


Paper Title :
Credit Card Fraud Detection With Data Mining and Machine Learning Approach

Author :Rushikesh Basavant Kolte, Krishnakumar Gautam Rathod, Sujeet More

Article Citation :Rushikesh Basavant Kolte ,Krishnakumar Gautam Rathod ,Sujeet More , (2022 ) " Credit Card Fraud Detection With Data Mining and Machine Learning Approach " , International Journal of Soft Computing And Artificial Intelligence (IJSCAI) , pp. 29-31, Volume-10,Issue-2

Abstract : Abstract - Nowaday’s online payment gaining popularity because of easy and convenience use of ecommerce. It became very easy mode of payment. People choose online payment and e-shopping; because of time convenience, transport convenience, etc. As the result of huge amount of e-commerce use, there is a vast increment in credit card fraud also. Machine Learning has been successfully applied to finance databases to automate analysis of huge volumes of complex data. Machine Learninghas also played a salient role in the detection of credit card fraud in online transactions. Fraud detection in credit card is a big problem, it becomes challenging due to two major reasons–first, the profiles of normal and fraudulent behaviours change frequently and secondly due to reason that credit card fraud data sets are highly skewed. This paper research and checks the performance of Random Forest on highly skewed credit card fraud data. Dataset of credit card transactions is sourced from European cardholders containing 1 lakh transactions. These techniques are applied on the raw and pre-processed data. The performance of the techniques is evaluated based on accuracy, sensitivity, and specificity, precision. Keywords - Data Analysis, Fraud In Credit Card, Decision Tree, Random Forest, Machine Learning, Security.

Type : Research paper

Published : Volume-10,Issue-2


DOIONLINE NO - IJSCAI-IRAJ-DOIONLINE-19208   View Here

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