[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-31241":3,"doc-seo-31241":26},{"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,"file_id":15,"file_url":16,"file_type":17,"file_size":18,"view_count":19,"is_deleted":4,"is_public":19,"is_downloadable":19,"audit_status":19,"page_count":20,"language":21,"table_of_contents":22,"faqs":23,"seo_title":13,"seo_description":14,"update_tm":24,"read_time":25},31241,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","A Review of Unsupervised Anomaly Detection Techniques for Health Insurance Fraud","This paper surveys unsupervised anomaly detection methods used for health insurance fraud, covering studies from 2017 to 2024. It evaluates machine-learning techniques for complex, high-dimensional, and imbalanced healthcare datasets, highlighting approaches such as Isolation Forest, Bayesian hierarchical models, and deep autoencoders. Compared with traditional methods, these techniques show stronger performance. Remaining challenges include transfer learning, model interpretability/explainability, and real-time incremental learning. Future work should target these gaps to improve accuracy and trust in fraud detection systems.","cbCaidV8E84P59Hu","https://ap.wps.com/l/cbCaidV8E84P59Hu","pdf",136907,1,9,"English","# Introduction\n## Anomaly detection basics and outputs\n## Unsupervised learning approaches\n## Health insurance fraud problem context\n## Survey scope and search strategy","[{\"question\":\"What is the main goal of anomaly detection in this survey?\",\"answer\":\"The survey focuses on identifying unexpected or uncommon occurrences in data, especially outliers that may indicate irregular events such as fraud in healthcare contexts.\"},{\"question\":\"How do unsupervised anomaly detection methods work when labels are unavailable?\",\"answer\":\"They rely on the assumption that anomalies are rare and different from normal data, learning the structure or distribution through methods such as clustering, density-based approaches, autoencoders, or PCA and then flagging deviations.\"},{\"question\":\"Which techniques are highlighted as showing superior performance?\",\"answer\":\"The review emphasizes Isolation Forest, Bayesian hierarchical models, and deep autoencoders as demonstrating stronger results compared with traditional approaches on relevant healthcare datasets.\"}]",1779224496,23,{"code":4,"msg":27,"data":28},"ok",{"site_id":29,"language":30,"slug":31,"title":13,"keywords":32,"description":14,"schema_data":33,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":24},105,"en","a-review-of-unsupervised-anomaly-detection-techniques-for-health-insurance-fraud","",{"@graph":34,"@context":84},[35,52,67],{"@type":36,"itemListElement":37},"BreadcrumbList",[38,42,46,49],{"item":39,"name":40,"@type":41,"position":19},"https://docshare.wps.com","Home","ListItem",{"item":43,"name":44,"@type":41,"position":45},"https://docshare.wps.com/document/","Document",2,{"item":47,"name":12,"@type":41,"position":48},"https://docshare.wps.com/document/research-report/",3,{"item":50,"name":13,"@type":41,"position":51},"https://docshare.wps.com/document/a-review-of-unsupervised-anomaly-detection-techniques-for-health-insurance-fraud/31241/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"description":14,"dateModified":60,"datePublished":61,"encodingFormat":59,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":39,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-05-20","2026-05-19",true,{"@type":64,"interactionType":65,"userInteractionCount":19},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What is the main goal of anomaly detection in this survey?","Question",{"text":74,"@type":75},"The survey focuses on identifying unexpected or uncommon occurrences in data, especially outliers that may indicate irregular events such as fraud in healthcare contexts.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How do unsupervised anomaly detection methods work when labels are unavailable?",{"text":79,"@type":75},"They rely on the assumption that anomalies are rare and different from normal data, learning the structure or distribution through methods such as clustering, density-based approaches, autoencoders, or PCA and then flagging deviations.",{"name":81,"@type":72,"acceptedAnswer":82},"Which techniques are highlighted as showing superior performance?",{"text":83,"@type":75},"The review emphasizes Isolation Forest, Bayesian hierarchical models, and deep autoencoders as demonstrating stronger results compared with traditional approaches on relevant healthcare datasets.","https://schema.org",{"og:url":50,"og:type":86,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":88,"canonical":50},"index,follow",{"doc_id":7,"site_id":29}]