[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117094-en":3,"doc-seo-117094-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117094,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Medical Insurance Fraud Detection Using Machine Learning - Research Article","Medical insurance fraud creates significant financial pressure and undermines patient care within the healthcare industry. This research applies machine learning approaches to automatically identify fraudulent activities hidden inside healthcare insurance claims. By analyzing large claim datasets, the study aims to detect patterns, anomalies, and outliers that signal possible fraud. Real-time detection supports insurers in reducing losses from bogus claims while improving the accuracy, efficiency, and ethical compliance of fraud prevention systems.","Journal of Advanced Zoology  \nISSN: 0253-7214  \nVolume 44 IssueS-8 Year 2023 Page 37-43  \nMedical Insurance Fraud Detection Using Machine Learning  \nP.P. Shenoy1*, P. K. Vidhate2, S. C. Gund3  \n1*Assistant Professor, Department Of Information Technology, Changu Kana Thakur Arts, Commerce and  \nScience College, New Panvel, [Maharashtra-410206 Email:-shenoypriya89@gmail.com](Maharashtra-410206 Email:-shenoypriya89@gmail.com)  \n2Department Of Information Technology, Changu Kana Thakur Arts, Commerce and Science College, New  \nPanvel, [Maharashtra-410206 Email:-pratikshavidhate0112@gmail.com](Maharashtra-410206 Email:-pratikshavidhate0112@gmail.com)  \n3Department Of Information Technology, Changu Kana Thakur Arts, Commerce and Science College, New  \nPanvel, [Maharashtra-410206 Email:-shwetagund18@gmail.com](Maharashtra-410206 Email:-shwetagund18@gmail.com)  \n* Corresponding Author: P.P. Shenoy  \n*Assistant Professor, Department Of Information Technology, Changu Kana Thakur Arts, Commerce and  \nScience College, New Panvel,[Maharashtra-410206 Email:-shenoypriya89@gmail.com](Maharashtra-410206 Email:-shenoypriya89@gmail.com)  \n\n| CC License\u003Cbr>CC-BY-NC-SA 4.0 | Abstract\u003Cbr>Medical insurance fraud poses significant challenges to the healthcare industry, impacting financial resources and patient care. This research explores the application of machine learning methodologies to detect fraudulent activities within healthcare insurance claims. Medical insurance fraud detection is crucial to help insurance companies save money. Machine learning is a powerful tool that can be used to detect fraudulent activities in the healthcare industry. Fraud can be spread broadly and extremely costly to the therapeutic protection framework. Protection can be made unscrupulous and be a case designed to hide or alter such information meant for social insurance benefits. Cheats might be numerous and submitted by the protection guarantor or the safeguarded. The unscrupulous social insurance providers are the reason for extortion in the well-being segment.\u003Cbr>This research approach is to apply machine learning to find incidents of medical insurance fraud automatically. In conclusion, machine learning is a promising tool for detecting medical insurance fraud. It can help insurers detect fraudulent activities in real time, saving money on bogus claims.\u003Cbr>Keywords: machine learning, fraud detection, healthcare, insurance. |\n| --- | --- |\n\n1. INTRODUCTION  \nMedical insurance fraud detection is identifying and preventing fraudulent claims or activities in the health insurance sector. Fraudulent claims can result from intentional deception, misrepresentation, or manipulation of facts by healthcare providers, insurance subscribers, or other parties involved in the insurance process. Fraudulent activities can lead to increased healthcare costs, reduced quality of care, and wasted financial resources.  \nComputer science can be categorized into machine learning, a form of artificial intelligence that teaches computers how to make inferences or decisions based on collected data without specifically instructing them. Machine learning can be used to detect medical insurance fraud by analyzing large amounts of insurance claims data and identifying patterns, anomalies, or outliers that indicate possible fraud. Machine learning can also  \nhelp to automate the fraud detection process, reduce human errors, and improve the accuracy and efficiency of fraud prevention.  \nThis research endeavors to explore, analyze, and advance methodologies in medical insurance fraud detection through the lens of machine learning. By delving into the diverse spectrum of machine learning algorithms, feature engineering approaches, and ethical considerations, this study seeks to unravel the intricacies of detecting fraudulent behavior in healthcare insurance claims. The aim is not only to enhance accuracy and efficiency but also to ensure ethical compliance, transparency, and fairness in the detec","cbCaimWRoUT2FHkN","https://ap.wps.com/l/cbCaimWRoUT2FHkN","pdf",945534,1,7,"English","en",105,"# Introduction\n# Objective\n# Limitations of the Study","[{\"question\":\"What problem does the study target?\",\"answer\":\"The study targets fraud in health insurance claims that can involve intentional deception, misrepresentation, or manipulation by parties in the insurance process.\"},{\"question\":\"How does machine learning help detect medical insurance fraud?\",\"answer\":\"Machine learning detects fraud by analyzing large volumes of insurance claim data to find patterns, anomalies, and outliers indicative of suspicious behavior, while automating the detection workflow.\"},{\"question\":\"What are the key limitations of the proposed approach?\",\"answer\":\"Model performance depends on data quality and completeness; class imbalance can introduce bias, feature selection is challenging, results may not generalize across regions or datasets, and some complex models are difficult to interpret.\"}]","Medical Insurance Fraud Detection Using Machine Learning - Research Article | PDF",1785673713,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"medical-insurance-fraud-detection-using-machine-learning-research-article","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/medical-insurance-fraud-detection-using-machine-learning-research-article/117094/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study target?","Question",{"text":75,"@type":76},"The study targets fraud in health insurance claims that can involve intentional deception, misrepresentation, or manipulation by parties in the insurance process.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does machine learning help detect medical insurance fraud?",{"text":80,"@type":76},"Machine learning detects fraud by analyzing large volumes of insurance claim data to find patterns, anomalies, and outliers indicative of suspicious behavior, while automating the detection workflow.",{"name":82,"@type":73,"acceptedAnswer":83},"What are the key limitations of the proposed approach?",{"text":84,"@type":76},"Model performance depends on data quality and completeness; 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