{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/network-analytics-for-insurance-fraud","title":"Network analytics for insurance fraud detection: a critical case study","arxiv_id":null,"date":"2024-05-14","proceeding":"European Actuarial Journal 2024 5","authors":["Bruno Deprez","Félix Vandervorst","Wouter Verbeke","Bart Baesens","Tim Verdonck"],"abstract":"There has been an increasing interest in fraud detection methods, driven by new regulations and by the financial losses linked to fraud. One of the state-of-the-art methods to fight fraud is network analytics. Network analytics leverages the interactions between different entities to detect complex patterns that are indicative of fraud. However, network analytics has only recently been applied to fraud detection in the actuarial literature. Although it shows much potential, many network methods are not yet applied. This paper extends the literature in two main ways. First, we review and apply multiple methods in the context of insurance fraud and assess their predictive power against each other. Second, we analyse the added value of network features over intrinsic features to detect fraud. We conclude that (1) complex methods do not necessarily outperform basic network features, and that (2) network analytics helps to detect different fraud patterns, compared to models trained on claim-specific features alone.","url_abs":"https://doi.org/10.1007/s13385-024-00384-6","url_pdf":"https://link.springer.com/article/10.1007/s13385-024-00384-6","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"network-analytics-for-insurance-fraud","repo_url":"https://github.com/B-Deprez/NetworkFraud_BiRank_M2V_SAGE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"fraud-detection","task_name":"Fraud Detection"}],"methods":[{"method_slug":"graphsage","method_name":"GraphSAGE"},{"method_slug":"metapath2vec","method_name":"metapath2vec"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/fraud-detection-on-healthcare-provider-fraud","task":"Fraud Detection","dataset":"Healthcare Provider Fraud Detection Analysis","model":"BiRank","rank_in_archive_order":1,"of":3,"metrics":{"AUC":"0.786","AUPRC":"0.175"},"uses_additional_data":false},{"leaderboard":"/sota/fraud-detection-on-healthcare-provider-fraud","task":"Fraud Detection","dataset":"Healthcare Provider Fraud Detection Analysis","model":"GraphSAGE","rank_in_archive_order":2,"of":3,"metrics":{"AUC":"0.668","AUPRC":"0.201"},"uses_additional_data":false},{"leaderboard":"/sota/fraud-detection-on-healthcare-provider-fraud","task":"Fraud Detection","dataset":"Healthcare Provider Fraud Detection Analysis","model":"metapath2vec","rank_in_archive_order":3,"of":3,"metrics":{"AUC":"0.513","AUPRC":"0.054"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}