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Classification and Fraud Detection in Finance Industry

by Akshansh Sinha, Shivam Mokha
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 176 - Number 3
Year of Publication: 2017
Authors: Akshansh Sinha, Shivam Mokha
10.5120/ijca2017915570

Akshansh Sinha, Shivam Mokha . Classification and Fraud Detection in Finance Industry. International Journal of Computer Applications. 176, 3 ( Oct 2017), 45-52. DOI=10.5120/ijca2017915570

@article{ 10.5120/ijca2017915570,
author = { Akshansh Sinha, Shivam Mokha },
title = { Classification and Fraud Detection in Finance Industry },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2017 },
volume = { 176 },
number = { 3 },
month = { Oct },
year = { 2017 },
issn = { 0975-8887 },
pages = { 45-52 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume176/number3/28535-2017915570/ },
doi = { 10.5120/ijca2017915570 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:41:34.207257+05:30
%A Akshansh Sinha
%A Shivam Mokha
%T Classification and Fraud Detection in Finance Industry
%J International Journal of Computer Applications
%@ 0975-8887
%V 176
%N 3
%P 45-52
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Due to increase of fraud which results in loss of money across the globe, several methodologies and techniques developed for detecting frauds Fraud detection involves analysing the activities of users in order to understand the malicious behaviour of users. Malicious behaviour is a broad term including delinquency, fraud, intrusion, and account defaulting. This paper presents a survey of current techniques used in credit card fraud detection and evaluates a new hybrid approach to identify fraud detection. The paper also discusses popular algorithms used for unsupervised and supervised learning.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Fraud detection Data mining Machine learning SVM Genetic Algorithms Anomalies.

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