Managing Risks Emerging in AI-Based Claims Verification: The Role of Enterprise Risk Management and Risk Governance in Health Insurance Operations
DOI:
https://doi.org/10.37385/ttvq7h93Keywords:
Artificial Intelligence, Claims Verification, Enterprise Risk Management, Risk Governance, Health Insurance.Abstract
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.
References
Boukherouaa, E. B., AlAjmi, K., Deodoro, J., Farias, A., Ravikumar, R., & Svirydzenka, K. (2021). Powering the digital economy: Opportunities and risks of artificial intelligence in finance. International Monetary Fund. https://doi.org/10.5089/9781513594068.087
Eling, M., & Lehmann, M. (2022). The impact of digitalization on the insurance value chain and the InsurTech ecosystem. The Geneva Papers on Risk and Insurance – Issues and Practice, 47(3), 359–396. https://doi.org/10.1057/s41288-021-00201-7
Financial Stability Board. (2023). The financial stability implications of artificial intelligence. https://www.fsb.org
Florio, C., & Leoni, G. (2021). Enterprise risk management and firm performance: The Italian case. The British Accounting Review, 53(1), 100888. https://doi.org/10.1016/j.bar.2020.100888
Institute of Internal Auditors. (2020). The IIA’s three lines model: An update of the three lines of defense. The Institute of Internal Auditors.
International Organization for Standardization. (2018). ISO 31000:2018 risk management Guidelines. ISO.
OECD. (2021). Artificial intelligence, machine learning and big data in finance: Opportunities, challenges, and implications for policy makers. OECD Publishing. https://doi.org/10.1787/7c2c1c4e-en
Rahmawati, D. (2022). Manajemen risiko operasional dalam transformasi digital perusahaan. Jurnal Manajemen Indonesia, 22(1), 45–56.
Simon, H. A. (1947). Administrative behavior: A study of decision-making processes in administrative organizations. Free Press.
Utami, W., & Nugroho, L. (2021). Penerapan enterprise risk management pada industri keuangan di Indonesia. Jurnal Manajemen dan Keuangan, 10(2), 123–135.
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