<![CDATA[The digital transformation of the health insurance industry, particularly through the implementation of Artificial Intelligence (AI) in claims verification processes, has enhanced operational efficiency while simultaneously introducing new, complex, and technology-driven risks. This study aims to identify the risks emerging from AI implementation, evaluate the application of Enterprise Risk Management (ERM), analyze key risk factors, and examine the role of risk governance in overseeing AI-based claims verification processes. This research adopts a qualitative case study approach focusing on a health insurance company in Indonesia that has implemented AI in cashless claims services. Data were collected through document analysis, process observation, semi-structured interviews involving the three lines of defense, and literature review. The analysis was conducted using thematic analysis, supported by the ISO 31000 ERM framework and the Three Lines Model for risk governance. The findings indicate that the implementation of AI in datafication, verification, and claim settlement significantly improves operational efficiency in processing large volumes of claims. However, it also introduces new risks, including data dependency risk, model risk, algorithmic bias, and systemic risk amplification. Furthermore, the current ERM implementation remains general and has not fully incorporated AI-specific risks. Although a risk governance structure has been established, its effectiveness in overseeing technology-driven risks remains limited. This study concludes that AI implementation not only transforms operational processes but also fundamentally reshapes the organization’s risk profile. Therefore, strengthening ERM and risk governance through more adaptive, integrated, and technology-oriented approaches is essential to effectively manage emerging AI-related risks.]]>
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