CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING

Registration ID: IJNRD_191166

Published ID: IJNRD2304395

DOI: Click Here to Get

Snehal Nalawade , Sakshi Akangire , Samruddhi patil , Apurva Vajarinkar , Aarti Kale

Fraud Detection, Credit Card, SVM, Decision Tree, Machine Learning.

In today’s world the credit card fraud is the biggest issue and now there is need to combat against the credit card fraud. “credit card fraud is the process of cleaning dirty money, thereby making the source of funds no longer identifiable.” On daily basis, the financial transactions are made on huge amount in global mar- ket and hence detecting credit card fraud activity is challenging task. As earlier (Anti- credit card fraud Suite) is introduced to detect the suspicious activities but it is applicable only on individual transaction not for other bank account transac- tion. To Overcomes issues of we propose Machine learning method using ‘Structural Similarity’, to identify common attributes and behavior with other bank account transaction. Detection of credit card fraud transaction from large volume dataset is difficult, so we propose case reduction methods to reduces the input dataset and then find pair of transaction with other bank account with common attributes and behavior.

Published Paper   E-Certificate

Snehal Nalawade, Sakshi Akangire, Samruddhi patil, Apurva Vajarinkar, & Aarti Kale (April-2023). CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(4), d692-d696. https://ijnrd.org/papers/IJNRD2304395.pdf

Volume 8 Issue 4, April-2023

Pages : d692-d696

Paper Reg. ID: IJNRD_191166

Published Paper Id: IJNRD2304395

Downloads: 000121974

Research Area: Information Technology 

Country: Pune, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2304395.pdf

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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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