An Explainable Detection Framework for Health Insurance Fraud via Temporal Capture and Confidence Assurance

An Explainable Detection Framework for Health Insurance Fraud via Temporal Capture and Confidence Assurance | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article An Explainable Detection Framework for Health Insurance Fraud via Temporal Capture and Confidence Assurance Ben Niu, Qingli Zhang, Gustave Florentin Nkoulou Mvondo, Shuang Geng, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8092242/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Fraud detection is essential to managing health insurance funds, yet current research methods are too broad for specific medical contexts. This study aims to narrow this gap by dissecting the workflow of health service providers to create a focused detection framework. Through this approach, we not only achieve reliable prediction outcomes but also offer practical guidance for implementation in real-world medical practices.Health insurance systems are characterized by highly specialized workflows and data formats. Utilizing health insurance data, this study focuses on claims submitted by providers for comprehensive statistical analysis. Temporal features in varying claim sequences are significant indicators of fraud. Thus, an advanced long short-term memory (LSTM) model is used to analyze long claim sequences, capturing essential temporal and interval features. Furthermore, this study incorporates attention mechanism to adeptly aggregate sequences of varying lengths, facilitating focused analysis and prioritizing suspicious claims. To address data imbalance affecting classifier efficacy, conformal prediction is used to calibrate classifier biases. Besides, two design strategies offer probability guarantees for provider fraud detection under complex practical requirements. This method not only improves the classifier reliability but also helps to determining audit provider priorities, thereby improving audit fraud detection efficiency.The proposed framework was evaluated against nine prevalent models spanning machine learning, ensemble learning, and deep learning through comparative analysis and ablation experiments. It demonstrated superior performance, achieving an AUC value of 0.907 on a real-world dataset. Additionally, two conformal prediction schemes were implemented, showing effectiveness in coverage priority and size priority, with a coverage rate up to 0.992. Illustrative examples further highlight the practical value of attention weights and prediction sets in providing actionable fraud detection insights.This study constructs a framework for detecting health insurance fraud. Crucially, this design paradigm can provide actionable guidance for the practice of medical managers. Biological sciences/Computational biology and bioinformatics Health sciences/Health care Physical sciences/Mathematics and computing Health sciences/Medical research Health insurance fraud Explainable AI Conformal prediction Temporal sequence Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 12 May, 2026 Reviewers agreed at journal 16 Feb, 2026 Reviews received at journal 07 Feb, 2026 Reviewers agreed at journal 13 Jan, 2026 Reviewers agreed at journal 09 Dec, 2025 Reviewers agreed at journal 02 Dec, 2025 Reviewers invited by journal 19 Nov, 2025 Editor assigned by journal 14 Nov, 2025 Submission checks completed at journal 13 Nov, 2025 First submitted to journal 12 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8092242","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":547387882,"identity":"eb6efcfe-b89b-4262-8d73-e56be98ac6d0","order_by":0,"name":"Ben Niu","email":"","orcid":"","institution":"Shenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Ben","middleName":"","lastName":"Niu","suffix":""},{"id":547387883,"identity":"d6dca96d-d183-4c30-a01d-46652b82d03d","order_by":1,"name":"Qingli Zhang","email":"","orcid":"","institution":"Shenzhen 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