Artificial intelligence (AI) is rapidly reshaping the business environment across nearly every industry, and the actuarial profession is no exception. Organizations are increasingly investing in predictive analytics, machine learning, automation and generative AI tools to improve operational efficiency and enhance decision-making capabilities.[1] While some professionals fear that AI may eventually replace portions of actuarial work, the more realistic outcome is that AI will enhance actuarial capabilities and fundamentally transform how actuaries operate within modern organizations.
The actuarial profession has historically adapted itself to technological advancement. From manual calculations to spreadsheets and from deterministic methods to stochastic modeling, actuaries have consistently evolved their methods with changing analytical tools. AI represents the next major transition. It significantly increases the automation capability of other technological developments, including applications to highly specialized activities such as data cleaning, experience monitoring, model calibration and reporting processes.
AI in Healthcare and Medicare
The healthcare and Medicare sectors provide some of the strongest examples of AI-driven transformation within actuarial work. Medicare Advantage plans operate in a highly data-intensive environment involving claims data, risk adjustment methodologies, chronic disease management and increasing healthcare cost pressures.[2] AI technologies allow insurers to identify emerging claim trends earlier and improve forecasting accuracy. Machine learning models can analyze utilization patterns, prescription drug trends, provider performance and demographic information to support more informed pricing and reserving decisions. Predictive analytics can also identify members at high risk of hospitalization and support targeted intervention programs. Early intervention strategies may improve patient outcomes while reducing long-term healthcare costs.
Fraud detection is another major area of opportunity. AI-powered systems can identify abnormal billing behavior more effectively than traditional rule-based systems.[3] In Medicare-related programs, these systems may help insurers and regulators detect suspicious claim patterns, duplicate billing and provider outliers more efficiently.
Predictive Analytics and Machine Learning
One of the most important applications of AI within actuarial science is predictive modeling. Machine learning algorithms can process extremely large datasets and identify relationships more efficiently than traditional statistical approaches in certain scenarios. Healthcare insurers increasingly use predictive models to estimate claim costs, identify high-risk populations, improve care management programs and optimize operational performance.[4] Traditional generalized linear models (GLMs) remain foundational in actuarial practice because of their interpretability and regulatory acceptance. However, machine learning approaches such as random forests, gradient boosting methods and neural networks are becoming increasingly common for supplementary analysis. These models can capture nonlinear relationships and complex interactions that may not be fully reflected in conventional actuarial techniques. Generative AI tools are also beginning to influence actuarial workflows. Applications involving natural language processing, automated coding assistants and AI-generated reporting tools can reduce time spent on repetitive tasks. Actuaries may use these tools to summarize experience studies, organize large datasets, generate programming scripts and draft reports more efficiently.
Risks and Ethical Considerations
Despite the advantages of AI-driven analytics, these technologies introduce important risks and ethical concerns. Complex machine learning algorithms may lack transparency, making it difficult to explain outputs to regulators, management teams or external stakeholders.[5] In healthcare and Medicare environments, fairness and bias are especially important concerns because inaccurate or biased predictions could disproportionately impact vulnerable populations. Actuaries therefore play a critical role in validating models, ensuring ethical data usage and maintaining compliance with professional standards. Professional judgment remains essential because AI-generated outputs may contain inaccuracies, unsupported assumptions or misleading results.
Another significant concern involves data privacy and cybersecurity. AI systems often require large amounts of healthcare and financial information, increasing the importance of governance frameworks and regulatory compliance. Organizations must maintain strong safeguards to protect sensitive consumer information and comply with regulations such as the Health Insurance Portability and Accountability Act (HIPAA).
The Evolving Role of Actuaries
The growing adoption of AI means actuarial education and skill development must continue evolving. Future actuaries will require strong foundations not only in traditional actuarial mathematics but also in programming, data science, analytics and model governance. Skills in Python, R, SQL, cloud computing and machine learning are becoming increasingly valuable in actuarial roles.
Modern actuaries are also increasingly expected to operate as strategic business advisors rather than purely technical specialists. While AI can automate repetitive calculations and support data analysis, it cannot fully replace professional judgment, ethical responsibility, communication skills or business understanding. Professional organizations such as the Society of Actuaries (SOA) have already recognized this shift by expanding predictive analytics content within actuarial education and encouraging technological literacy across the profession.
Future Outlook and Conclusion
AI should not be viewed as a threat to the actuarial profession but rather as a transformational tool that can enhance actuarial capabilities. In many ways, AI may strengthen the profession by allowing actuaries to focus more on interpretation, governance, strategic analysis and innovation instead of repetitive manual processes. The future of actuarial work will likely involve increasing collaboration between actuaries, data scientists, software engineers and healthcare professionals. Cross-functional teamwork is becoming increasingly important because modern analytical projects require a combination of technical expertise, business insight and communication ability.
The actuarial profession has historically demonstrated resilience and adaptability. As artificial intelligence continues to evolve, actuaries who embrace innovation, strengthen their technical skills and maintain professional accountability will be well positioned for long-term success. In health and Medicare insurance specifically, AI offers opportunities to improve forecasting accuracy, operational efficiency, fraud detection and patient outcomes while supporting more informed risk management decisions. Ultimately, the future belongs not to actuaries who compete against AI, but to actuaries who learn how to work effectively alongside it.
This article is provided for informational and educational purposes only. Neither the Society of Actuaries nor the respective authors’ employers make any endorsement, representation or guarantee with regard to any content, and disclaim any liability in connection with the use or misuse of any information provided herein. This article should not be construed as professional or financial advice. Statements of fact and opinions expressed herein are those of the individual authors and are not necessarily those of the Society of Actuaries or the respective authors’ employers.
Alizain Khoja, ASA, is a graduate assistant and master’s student at Georgia State University, a SOA University Success Coach and serves on the SOA Young Professionals Advisory Council. Alizain can be reached at alizain.khoja@yahoo.com.
Endnotes
[1] McKinsey & Company, “The State of AI in 2025: Agents, Innovation and Transformation,” McKinsey, November 5, 2025, https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai.
[2] Centers for Medicare & Medicaid Services (CMS). “Risk Adjustment,” CMS, last modified October 24, 2024, https://www.cms.gov/medicare/payment/medicare-advantage-rates-statistics/risk-adjustment.
[3] Bjorn Blom, et al, “Anomaly Detection Techniques in Fraud Detection, Performance Optimization, and Data Quality,” Milliman, March 8, 2023, https://www.milliman.com/en/insight/anomaly-detection-techniques-in-fraud-detection.
[4] Jean-Philippe Larochelle, et al, “Predictive Analytics and Machine Learning—Practical Applications for Actuarial Modeling (Nested Stochastic),” Society of Actuaries Research Institute, May 2023, https://www.soa.org/resources/research-reports/2023/predictive-analytics-and-machine-learning/.
[5] “Predictive Analytics in Health Care,” Deloitte Insights, July 19, 2019, https://www.deloitte.com/us/en/insights/topics/analytics/predictive-analytics-health-care-value-risks.html.