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This article examines how federated learning (FL) converges with healthcare finance while safeguarding patient privacy through machine-learning safeguards. Because clinical and financial records are inherently sensitive, FL offers a way for organisations to train models together without ever pooling those records in a single location. A systematic survey of the literature, drawn from Scopus and Web of Science between 2018 and early 2025, catalogues existing frameworks, use cases, and privacy boosters that researchers have deployed in fiscal health systems. The review organises findings around three pivotal domains: uncovering claims fraud, forecasting treatment costs, and streamlining the billing process. Across the papers, we document protective layers-such as differential Privacy, secure multiparty computation (SMPC), and homomorphic encryption-that seek to shield conversations among patients, providers, and insurers. Even so, several weaknesses remain, particularly in achieving seamless interoperability, establishing mutual trust, and ensuring that models converge when the underlying financial datasets differ significantly. Looking ahead, the review suggests greater emphasis on explainable outputs, strict compliance with regulatory demands, and the adoption of blockchain-linked federated architectures to bolster both security and transparency.

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Federated Learning in Healthcare Finance: A Systematic Review of Privacy-Preserving Models

  • 章节

  • 发表日期：

    2026年

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  • Manika Garg,
  • Sunitaa Tank,
  • Bharat Kumar Tank

摘要

This article examines how federated learning (FL) converges with healthcare finance while safeguarding patient privacy through machine-learning safeguards. Because clinical and financial records are inherently sensitive, FL offers a way for organisations to train models together without ever pooling those records in a single location. A systematic survey of the literature, drawn from Scopus and Web of Science between 2018 and early 2025, catalogues existing frameworks, use cases, and privacy boosters that researchers have deployed in fiscal health systems. The review organises findings around three pivotal domains: uncovering claims fraud, forecasting treatment costs, and streamlining the billing process. Across the papers, we document protective layers-such as differential Privacy, secure multiparty computation (SMPC), and homomorphic encryption-that seek to shield conversations among patients, providers, and insurers. Even so, several weaknesses remain, particularly in achieving seamless interoperability, establishing mutual trust, and ensuring that models converge when the underlying financial datasets differ significantly. Looking ahead, the review suggests greater emphasis on explainable outputs, strict compliance with regulatory demands, and the adoption of blockchain-linked federated architectures to bolster both security and transparency.