Preventing Leakage in Claims Management: A Governance Model for Detecting Errors, Fraud, and Process Gaps

Abstract

The subject of claims leakage, which too often hides behind measurements at the surface level, is one of the essential and recurring losses in insurance business due to errors, inefficiencies, fraud, and failure in processes antics. The conventional practice is more involved with the post-payment audit, or individual fraud control methods, which do not resolve the systematic flaws and prevent the losses in the future. Through this paper, the author proposes a proactive, governance-typical model in which leakage of claims are prevented through structured data validation, workflow automation, fraud detection processes and cross-functional responsibility. Using the experience in health insurance, the paper engages in analyzing the advantages of having real-time validation rules, use of fraud scores through machine learning models, automatic routing, and any audit trail being embedded. Quantitative data of a real-life insurer shows the overall 59.4 percent leakage decrease and the significant rise in SLA compliance, predictive accuracy of fraud detection, and customer experience. The proposed governance model offers scalable and flexible model, which adopts monitoring control at the heart of the claims process. It changes the organizational mindset regarding being reactive in correction and preventative in governance to increase regulatory compliance levels, efficiency in operations and strengthen the trust of the stakeholders. The study indicates the necessity of implementing the aspects of a digital framework on governance of insurance operation ecology in the contemporary insurance sector, presenting a refined guide on protecting financial performance, loss mitigation, and improvement of service delivery in claims processing.