Artificial intelligence (AI) is revolutionizing various industries, including healthcare. AI has the potential to be applied to many areas of healthcare, ranging from diagnostics to health administration. Health insurance companies, in particular, are racing to leverage AI technologies to improve operational efficiency, reduce costs, and deliver more personalized services. AI is reshaping key areas such as risk assessment, claims processing, customer engagement, and predictive analytics.

AI’s impact on risk assessment and underwriting for health insurance is significant. Traditionally, insurers relied on historical data and manual processes to assess risk. However, AI allows insurers to analyze large datasets from diverse sources, such as medical history, genetic information, and lifestyle habits, to more accurately predict future health risks. Machine learning algorithms can identify patterns or trends that human analysts may miss, feeding into efforts by health insurance companies to match pricing to individual risk profiles and improve their financial stability ^1,2.

AI is also transforming claims processing by automating administrative tasks. Traditional claims processes are often slow and resource-intensive, but AI can streamline these tasks when applied correctly, resulting in faster decisions and reimbursements for healthcare providers and patients. AI’s role in fraud detection is also crucial. AI algorithms can analyze claims data to spot inconsistencies or unusual patterns, such as exaggerated claims or unnecessary treatments. By identifying potential fraud early, AI can help insurers avoid financial losses ^3–5.

In addition, AI-powered chatbots and virtual assistants could enhance customer service by reducing long wait times. AI systems can also enable insurers to monitor policyholders’ health more closely, providing real-time insights and promoting healthier behaviors ^6–8. The health insurance industry may also use AI to analyze healthcare provider’s performance data, tracking patient outcomes and cost efficiency.

AI’s ability to process vast amounts of data enables predictive analytics, allowing insurers to identify individuals at high risk for chronic diseases. By recognizing these risks early, insurers could encourage proactive care and wellness programs, preventing more serious and costly health issues. This preventive approach would improve health outcomes for policyholders while reducing the financial burden on both insurers and the healthcare system ^12,13.

Despite its benefits, applications of AI in the field of health insurance raises ethical concerns. Privacy is a major issue, as insurers must safeguard sensitive health data and comply with various regulations. The risk of algorithmic bias also presents itself: If AI systems are trained on biased data, they may produce discriminatory outcomes. To mitigate this, insurers must regularly audit their AI systems to ensure they are fair and inclusive ^14–16. Overall, AI presents potential applications as a tool, but it must be used appropriately to produce positive impacts.

References

1. An expanded role for AI in Life & Health Predictive Underwriting | Swiss Re. https://www.swissre.com/reinsurance/insights/ai-predictive-underwriting-life-and-health.html (2025).
2. Revolutionizing Group Health Underwriting: AI for Carriers. https://www.gradientai.com/revolutionizing-health-underwriting-with-ai-for-carriers-blog (2024).
3. Shift Technology | AI for Insurance Decisions. https://www.shift-technology.com.
4. Use of machine learning for fraud detection within the claims management process in the Philippines. https://www.who.int/publications/i/item/9789240101333.
5. AI’s impact on health insurance: from fraud detection to claims processing. https://www.insurancebusinessmag.com/nz/news/breaking-news/ais-impact-on-health-insurance-from-fraud-detection-to-claims-processing-492480.aspx.
6. Alowais, S. A. et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Medical Education 23, 689 (2023). DOI: 10.1186/s12909-023-04698-z
7. Johnson, K. B. et al. Precision Medicine, AI, and the Future of Personalized Health Care. Clin Transl Sci 14, 86–93 (2021). DOI: 10.1111/cts.12884
8. How AI in healthcare can improve consumer experiences | McKinsey. https://www.mckinsey.com/industries/healthcare/our-insights/harnessing-ai-to-reshape-consumer-experiences-in-healthcare.
9. Natalie, C. OECD Artificial Intelligence Papers. (2024).
10. Khanna, N. N. et al. Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment. Healthcare (Basel) 10, 2493 (2022). DOI: 10.3390/healthcare10122493
11. AI in Healthcare: A Cost-Saving Revolution – Biotech Spain. https://biotech-spain.com/en/articles/ai-in-healthcare-a-cost-saving-revolution/.
12. Bajwa, J., Munir, U., Nori, A. & Williams, B. Artificial intelligence in healthcare: transforming the practice of medicine. Future Healthc J 8, e188–e194 (2021). DOI: 10.7861/fhj.2021-0095
13. Dixon, D. et al. Unveiling the Influence of AI Predictive Analytics on Patient Outcomes: A Comprehensive Narrative Review. Cureus 16, e59954. DOI: 10.7759/cureus.59954
14. Harishbhai Tilala, M. et al. Ethical Considerations in the Use of Artificial Intelligence and Machine Learning in Health Care: A Comprehensive Review. Cureus 16, e62443. DOI: 10.7759/cureus.62443
15. Farhud, D. D. & Zokaei, S. Ethical Issues of Artificial Intelligence in Medicine and Healthcare. Iran J Public Health 50, i–v (2021). DOI: 10.18502/ijph.v50i11.7600
16. Naik, N. et al. Legal and Ethical Consideration in Artificial Intelligence in Healthcare: Who Takes Responsibility? Front. Surg. 9, (2022). DOI: 10.3389/fsurg.2022.862322