Strengthening Financial Integrity in U.S. Public Health Systems: The Mediating Role of Governance and Organizational Readiness in Analytics- and Machine Learning-Enabled Audit Optimization

Abstract

U.S. public health financing systems, particularly Medicare and Medicaid, continue to face substantial improper-payment and fraud risks. Analytics and machine learning offer expanded detection coverage, more focused investigative targeting, and opportunities for earlier intervention. Evidence from technical studies and operational program-integrity initiatives demonstrates meaningful gains in detection and targeting, but the translation of those gains into durable financial-integrity outcomes is conditional rather than automatic. Drawing on a validated reference base of 28 sources spanning peer-reviewed research, federal oversight documents, operational reports, and selected practice frameworks, this state-of-the-art critical review maps the evolution of audit paradigms and develops governance capability and organisational readiness as two interdependent conceptual mediating mechanisms. Governance shapes whether algorithmic systems are embedded in accountable control environments and subjected to meaningful oversight. Organisational readiness, encompassing data quality, workforce skills, leadership commitment, infrastructure, and organisational maturity, determines whether agencies can absorb, interpret, and act on advanced tools. The review advances the Governance-Readiness Mediated Model of Intelligent Audit Optimisation (GRM-IAO). The framework proposes that analytics and machine learning contribute to audit optimisation and financial integrity when technical capability is enabled by adequate readiness and directed by coherent governance. Strengthening financial integrity through intelligent auditing is therefore an institutional and organisational achievement as well as a technological one.