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International Journal of Scientific Development and Research - IJSDR
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Paper Title: Credit Card fraud detection using classification techniques
Authors Name: Rutuja S. Gore , Dr.Khan Rahat Afreen
Unique Id: IJSDR1909003
Published In: Volume 4 Issue 9, September-2019
Abstract: Credit card fraud has increased considerably due to the growth of the latest technologies and global communications routes. Credit cards cost billions of dollars in consumer and financial companies per year. Con artists try to find new plans and procedures to continue illegal operations. Therefore, fraud detection systems are necessary for banks and financial institutions to reduce losses. The most common technique used to create fraud detection patterns Furthermore, detecting and preventing credit card fraud is one of the most important problems in the digital world that requires precise transaction analysis. One way to detect fraud is to investigate suspicious changes in user behavior. We have implemented a linear biased classifier using kernel method that allows us to classify data based upon selective attributes by performing vectorization of values and using probability threshold that is set to 90 % based upon which it classifies record as fraud or non fraud. We have compared our results with other techniques such as Bayesian networks, C45, J48 and decision tree along with Bay minimum risks. Our method to improve fraud detection in Credit Cards is the main objective of this work with improvement in current fraud detection process by improving fraudulent account prediction. In addition, there is a collection and discussion of the evaluation criteria used in literature. Therefore, the issue opened for detecting credit card fraud will be described as a guideline for new researchers.
Keywords: Training dataset, classifiers, Data mining
Cite Article: "Credit Card fraud detection using classification techniques", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.4, Issue 9, page no.9 - 16, September-2019, Available :http://www.ijsdr.org/papers/IJSDR1909003.pdf
Downloads: 0001823
Publication Details: Published Paper ID: IJSDR1909003
Registration ID:190871
Published In: Volume 4 Issue 9, September-2019
DOI (Digital Object Identifier):
Page No: 9 - 16
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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