Credit Card Fraud Prediction And Detection using Artificial Neural Network And Self-Organizing Maps

E. Saraswathi, Prateek P. Kulkarni, Momin Nawaf Khalil, Shishir Chandra Nigam
{"title":"Credit Card Fraud Prediction And Detection using Artificial Neural Network And Self-Organizing Maps","authors":"E. Saraswathi, Prateek P. Kulkarni, Momin Nawaf Khalil, Shishir Chandra Nigam","doi":"10.1109/ICCMC.2019.8819758","DOIUrl":null,"url":null,"abstract":"The credit card business has increased speedily over the last two decades. Corporations and establishments are moving towards various online services, which aims to permit their customers with high potency and accessibility. The evolution is a huge step towards potency, accessibility and profitableness of view. Nevertheless, it additionally has some downsides. These smart services are recently prone to significant security related vulnerabilities. Developing business through card depends on the fact that neither the card nor the user needs to be present at the point of transaction. Thus, it is impossible for merchandiser to check weather the cardholder is real or not. Companies’ loss in recent times are majorly due to the credit card fraud and the fraudsters who ceaselessly obtain new ways to commit the unlawful activities. As we know that Artificial Neural Network has the ability to work as a human brain when trained properly. We have also implemented SOM for accuracy purpose. In this paper, we discuss about the performance of the network and their accuracy.","PeriodicalId":232624,"journal":{"name":"2019 3rd International Conference on Computing Methodologies and Communication (ICCMC)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 3rd International Conference on Computing Methodologies and Communication (ICCMC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCMC.2019.8819758","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

The credit card business has increased speedily over the last two decades. Corporations and establishments are moving towards various online services, which aims to permit their customers with high potency and accessibility. The evolution is a huge step towards potency, accessibility and profitableness of view. Nevertheless, it additionally has some downsides. These smart services are recently prone to significant security related vulnerabilities. Developing business through card depends on the fact that neither the card nor the user needs to be present at the point of transaction. Thus, it is impossible for merchandiser to check weather the cardholder is real or not. Companies’ loss in recent times are majorly due to the credit card fraud and the fraudsters who ceaselessly obtain new ways to commit the unlawful activities. As we know that Artificial Neural Network has the ability to work as a human brain when trained properly. We have also implemented SOM for accuracy purpose. In this paper, we discuss about the performance of the network and their accuracy.
基于人工神经网络和自组织映射的信用卡欺诈预测与检测
在过去的二十年里,信用卡业务增长迅速。公司和机构正在转向各种在线服务,其目的是让他们的客户具有高效力和可访问性。这一演变是朝着视野的效力、可及性和可盈利性迈出的一大步。然而,它也有一些缺点。这些智能服务最近容易出现重大的安全相关漏洞。通过卡开展业务取决于这样一个事实,即卡和用户都不需要出现在交易点。因此,商家不可能检查持卡人是否真实。近年来,公司的损失主要是由于信用卡诈骗和欺诈者不断获得新的方法来进行非法活动。正如我们所知,人工神经网络在训练得当的情况下具有像人类大脑一样工作的能力。我们还实现了SOM的准确性目的。在本文中,我们讨论了网络的性能和它们的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信