UGC Approved Journal no 63975(19)

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Published in:

Volume 7 Issue 2
February-2020
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2002292


Registration ID:
228061

Page Number

573-575

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Title

Analysis on Credit Card Fraud Identification based on KNN and Outlier Detection

Abstract

Popular payment mode which is credit card is acquired both offline and online that provides us with its great advantage of cashless transaction. A credit card is a convenient financial product that can be used for everyday purchases such as gas, groceries, and other goods and services. It becomes very easy, suited and groovy to make payments and other transactions through credit card. Credit card fraud is also growing along with the development in technology. Along with improvement in the global communication the economic fraud is remarkably increasing in the global communication. It is being recorded every year that the loss due to these fraudulent actions is billions of dollars. These activities are carried out so gracefully which looks similar to authentic transactions. Therefore, to have a systematized method of fraud detection has become a need for all banks in order to minimize heu and cry and bring order in place. Thus techniques of KNN and outlier detection are implemented to optimize the best result for the fraud detection problem. These methods are proved to slash the false alarm rates and enlarge the fraud detection rate. KNN and Outlier detection are quiet familiar area of research. Outlier Detection is absolutely important task in various application domains. Earlier outliers were considered as noisy data and now it has become severe difficulty in various areas of research. The discovery of outlier is useful in detection of unpredicted and unidentified data in certain areas like fraud detection of credit cards, calling cards, discovering computer intrusion and criminal behaviors etc. Whereas KNN (k-Nearest Neighbor) is used for classification because of its interpretation and low calculation time.

Key Words

Credit card fraud, KNN, Outlier detection

Cite This Article

"Analysis on Credit Card Fraud Identification based on KNN and Outlier Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 2, page no.573-575, February-2020, Available :http://www.jetir.org/papers/JETIR2002292.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Analysis on Credit Card Fraud Identification based on KNN and Outlier Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 2, page no. pp573-575, February-2020, Available at : http://www.jetir.org/papers/JETIR2002292.pdf

Publication Details

Published Paper ID: JETIR2002292
Registration ID: 228061
Published In: Volume 7 | Issue 2 | Year February-2020
DOI (Digital Object Identifier):
Page No: 573-575
Country: Vadodara, Gujarat, India .
Area: Science & Technology
ISSN Number: 2349-5162
Publisher: IJ Publication


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