Fraud, waste, and abuse (FWA) in healthcare represent a significant challenge to the sustainability and efficiency of healthcare financing systems worldwide. Within Third-Party Administrators (TPAs), which play a critical role in managing medical claims for self-insured employers, the reliance on predominantly manual claims processing exposes organizations to operational inefficiencies, financial leakage, and heightened fraud risks. This descriptive–explanatory case study examines the nature and extent of FWA in medical billing within a Malaysian healthcare TPA, analyses the limitations of existing manual claims checking processes, and explores the potential role of automation in strengthening fraud detection and risk management. Using a single-case study design, data were collected through semi-structured interviews with key operational personnel, document analysis of internal fraud management reports covering the period 2020–2025, and limited observation of claims workflows. The findings reveal recurring forms of FWA, including forging or altering of billing documents, phantom billing, misrepresentation of non-covered treatments, medical identity theft, and manipulation of diagnoses. Manual detection processes were found to be labour-intensive, reactive, prone to human error, and constrained by fragmented data systems and inconsistent rule application. Applying the Fraud Triangle Theory and internal control frameworks, the study explains how weaknesses in manual processes create opportunities for FWA. Comparative analysis indicates that automation through rule-based engines and Artificial Intelligence/Machine Learning (AI/ML) has strong potential to improve efficiency, enhance detection accuracy, reduce operational costs, and shift FWA management from a reactive to a proactive approach. The study concludes by offering managerial recommendations for the phased adoption of automation within Malaysian TPAs to strengthen governance, reduce financial losses, and improve the integrity of healthcare claims administration. Keywords: Fraud, Waste, and Abuse (FWA), Third-Party Administrators (TPA), Healthcare Claims Processing, Automation, Artificial Intelligence and Machine Learning