一种基于bat优化算法的信用卡数据异常检测智能方法

IF 3.4 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Haseena Sikkandar, Saroja S, Suseandhiran N, Manikandan B
{"title":"一种基于bat优化算法的信用卡数据异常检测智能方法","authors":"Haseena Sikkandar, Saroja S, Suseandhiran N, Manikandan B","doi":"10.4114/intartif.vol26iss72pp202-222","DOIUrl":null,"url":null,"abstract":"As technology advances, many people are utilising credit cards to purchase their necessities, and the number of credit card scams is increasing tremendously. However, illegal card transactions have been on the rise, costing financial institutions millions of dollars each year. The development of efficient fraud detection techniques is critical in reducing these deficits, but it is difficult due to the extremely unbalanced nature of most credit card datasets. As compared to conventional fraud detection methods, the proposed method will help in automatically detecting the fraud, identifying hidden correlations in data and reduced time for verification process. This is achieved by selecting relevant and unique features by using Bat Optimization Algorithm (BOA). Next, balancing is performed in the highly imbalanced credit card fraud dataset using Synthetic Minority over-sampling technique (SMOTE). Then finally the CNN model for anomaly detection in credit card data is built using full center loss function to improve fraud detection performance and stability. The proposed model is tested with Kaggle dataset and yields around 99% accuracy.","PeriodicalId":43470,"journal":{"name":"Inteligencia Artificial-Iberoamerical Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An intelligent approach for anomaly detection in credit card data using bat optimization algorithm\",\"authors\":\"Haseena Sikkandar, Saroja S, Suseandhiran N, Manikandan B\",\"doi\":\"10.4114/intartif.vol26iss72pp202-222\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As technology advances, many people are utilising credit cards to purchase their necessities, and the number of credit card scams is increasing tremendously. However, illegal card transactions have been on the rise, costing financial institutions millions of dollars each year. The development of efficient fraud detection techniques is critical in reducing these deficits, but it is difficult due to the extremely unbalanced nature of most credit card datasets. As compared to conventional fraud detection methods, the proposed method will help in automatically detecting the fraud, identifying hidden correlations in data and reduced time for verification process. This is achieved by selecting relevant and unique features by using Bat Optimization Algorithm (BOA). Next, balancing is performed in the highly imbalanced credit card fraud dataset using Synthetic Minority over-sampling technique (SMOTE). Then finally the CNN model for anomaly detection in credit card data is built using full center loss function to improve fraud detection performance and stability. The proposed model is tested with Kaggle dataset and yields around 99% accuracy.\",\"PeriodicalId\":43470,\"journal\":{\"name\":\"Inteligencia Artificial-Iberoamerical Journal of Artificial Intelligence\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2023-09-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Inteligencia Artificial-Iberoamerical Journal of Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4114/intartif.vol26iss72pp202-222\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Inteligencia Artificial-Iberoamerical Journal of Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4114/intartif.vol26iss72pp202-222","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

随着科技的进步,许多人使用信用卡购买生活必需品,信用卡诈骗的数量急剧增加。然而,非法信用卡交易呈上升趋势,每年给金融机构造成数百万美元的损失。开发有效的欺诈检测技术对于减少这些赤字至关重要,但由于大多数信用卡数据集的极度不平衡性质,这很困难。与传统的欺诈检测方法相比,该方法有助于自动检测欺诈,识别数据中隐藏的相关性,减少验证过程的时间。这是通过使用Bat优化算法(BOA)选择相关且独特的特征来实现的。接下来,使用合成少数派过采样技术(SMOTE)对高度不平衡的信用卡欺诈数据集进行平衡。最后利用全中心损失函数建立了信用卡数据异常检测的CNN模型,提高了欺诈检测的性能和稳定性。该模型在Kaggle数据集上进行了测试,准确率达到99%左右。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An intelligent approach for anomaly detection in credit card data using bat optimization algorithm
As technology advances, many people are utilising credit cards to purchase their necessities, and the number of credit card scams is increasing tremendously. However, illegal card transactions have been on the rise, costing financial institutions millions of dollars each year. The development of efficient fraud detection techniques is critical in reducing these deficits, but it is difficult due to the extremely unbalanced nature of most credit card datasets. As compared to conventional fraud detection methods, the proposed method will help in automatically detecting the fraud, identifying hidden correlations in data and reduced time for verification process. This is achieved by selecting relevant and unique features by using Bat Optimization Algorithm (BOA). Next, balancing is performed in the highly imbalanced credit card fraud dataset using Synthetic Minority over-sampling technique (SMOTE). Then finally the CNN model for anomaly detection in credit card data is built using full center loss function to improve fraud detection performance and stability. The proposed model is tested with Kaggle dataset and yields around 99% accuracy.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
2.00
自引率
0.00%
发文量
15
审稿时长
8 weeks
期刊介绍: Inteligencia Artificial is a quarterly journal promoted and sponsored by the Spanish Association for Artificial Intelligence. The journal publishes high-quality original research papers reporting theoretical or applied advances in all branches of Artificial Intelligence. The journal publishes high-quality original research papers reporting theoretical or applied advances in all branches of Artificial Intelligence. Particularly, the Journal welcomes: New approaches, techniques or methods to solve AI problems, which should include demonstrations of effectiveness oor improvement over existing methods. These demonstrations must be reproducible. Integration of different technologies or approaches to solve wide problems or belonging different areas. AI applications, which should describe in detail the problem or the scenario and the proposed solution, emphasizing its novelty and present a evaluation of the AI techniques that are applied. In addition to rapid publication and dissemination of unsolicited contributions, the journal is also committed to producing monographs, surveys or special issues on topics, methods or techniques of special relevance to the AI community. Inteligencia Artificial welcomes submissions written in English, Spaninsh or Portuguese. But at least, a title, summary and keywords in english should be included in each contribution.
×
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