A benchmark of health insurance fraud detection using machine learning techniques

Ossama Cherkaoui, H. Anoun, A. Maizate
{"title":"A benchmark of health insurance fraud detection using machine learning techniques","authors":"Ossama Cherkaoui, H. Anoun, A. Maizate","doi":"10.11591/ijai.v13.i2.pp1925-1934","DOIUrl":null,"url":null,"abstract":"Health insurance fraud is a complex problem that also has a significant financial impact. Recently, with the availability of large volumes of data and the evolution of computing power, machine learning techniques have become the preferred method for fraud detection. However, the main difficulty facing researchers in this field is the lack of real data sets and the absence of reliable fraud labels. Most published studies use aggregated provider-level or simulated data to test fraud detection algorithms, which may not deliver accurate results. The present study aims to provide a more accurate assessment of fraud detection methods by using real detailed health insurance claims data to compare six of the most common supervised classification algorithms including neural networks and the use of two categorical feature preparation methods. The study was conducted under the guidance of insurance experts, who provided the fraud label inference rules and reviewed the results. A comprehensive description of the benchmarking process and an interpretation of the results are provided in this paper. The results show that supervised classification can be used effectively to detect health insurance fraud, improving detection accuracy by a factor of 4.2 (84% recall for a positive rate of 20%). ","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IAES International Journal of Artificial Intelligence (IJ-AI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.11591/ijai.v13.i2.pp1925-1934","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Health insurance fraud is a complex problem that also has a significant financial impact. Recently, with the availability of large volumes of data and the evolution of computing power, machine learning techniques have become the preferred method for fraud detection. However, the main difficulty facing researchers in this field is the lack of real data sets and the absence of reliable fraud labels. Most published studies use aggregated provider-level or simulated data to test fraud detection algorithms, which may not deliver accurate results. The present study aims to provide a more accurate assessment of fraud detection methods by using real detailed health insurance claims data to compare six of the most common supervised classification algorithms including neural networks and the use of two categorical feature preparation methods. The study was conducted under the guidance of insurance experts, who provided the fraud label inference rules and reviewed the results. A comprehensive description of the benchmarking process and an interpretation of the results are provided in this paper. The results show that supervised classification can be used effectively to detect health insurance fraud, improving detection accuracy by a factor of 4.2 (84% recall for a positive rate of 20%). 
使用机器学习技术检测医疗保险欺诈的基准
医疗保险欺诈是一个复杂的问题,同时也具有重大的经济影响。最近,随着大量数据的可用性和计算能力的发展,机器学习技术已成为欺诈检测的首选方法。然而,该领域研究人员面临的主要困难是缺乏真实数据集和可靠的欺诈标签。大多数已发表的研究使用提供商级别的汇总数据或模拟数据来测试欺诈检测算法,这可能无法提供准确的结果。本研究旨在通过使用真实详细的医疗保险理赔数据来比较六种最常见的监督分类算法,包括神经网络和使用两种分类特征准备方法,从而对欺诈检测方法进行更准确的评估。这项研究是在保险专家的指导下进行的,他们提供了欺诈标签推理规则并对结果进行了审核。本文对基准测试过程进行了全面描述,并对结果进行了解释。结果表明,监督分类法可有效用于检测医疗保险欺诈,检测准确率提高了 4.2 倍(20% 的阳性率下召回率为 84%)。
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