Article

A Japan-Calibrated Synthetic Medical Claims Benchmark for Anti-Fraud Machine Learning

2026-06-12

Abstract excerpt

<title>Abstract</title> <p>Healthcare anti-fraud machine learning is constrained by restricted claim-level access, sparse confirmed fraud labels, privacy risk, and weak reproducibility across jurisdictions. This study presents a Japan-calibrated synthetic medical claims benchmark for anti-fraud analytics. The framework generates relational claims data from Japanese reference assets, temporal episodes of care, dia...

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Literature Corpus work: ebf8ca94-e643-547f-aae5-2b9d599decbc
DOI: 10.21203/rs.3.rs-9809133/v1

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