Defensible Data & AI
Secure your platforms and products to drive insights from AI and accelerate healthcare transformation.
SOLUTIONS
-
Research & Development
-
Real World Evidence
-
Commercialization
-
Safety & Regulatory Compliance
-
Technologies
LIFE SCIENCE SEGMENTS
HEALTHCARE SEGMENTS
- Information Partner Services
- Financial Institutions
- Global Health
- Government
- Patient Associations
- Payers
- Providers
THERAPEUTIC AREAS
- Cardiovascular
- Cell and Gene Therapy
- Central Nervous System
- GI & Hepatology
- Infectious Diseases & Vaccines
- Oncology & Hematology
- Pediatrics
- Rare Diseases
- View All
Impacting People's Lives
"We strive to help improve outcomes and create a healthier, more sustainable world for people everywhere.
LEARN MORE
Harness the power to transform clinical development
Reimagine clinical development by intelligently connecting data, technology, and analytics to optimize your trials. The result? Faster decision making and reduced risk so you can deliver life-changing therapies faster.
Research & Development OverviewResearch & Development Quick Links
Real World Evidence. Real Confidence. Real Results.
Generate and disseminate evidence that answers crucial clinical, regulatory and commercial questions, enabling you to drive smarter decisions and meet your stakeholder needs with confidence.
REAL WORLD EVIDENCE OVERVIEWReal World Evidence Quick Links
See markets more clearly. Opportunities more often.
Elevate commercial models with precision and speed using AI-driven analytics and technology that illuminate hidden insights in data.
COMMERCIALIZATION OVERVIEWCommercialization Quick Links
Service driven. Tech-enabled. Integrated compliance.
Orchestrate your success across the complete compliance lifecycle with best-in-class services and solutions for safety, regulatory, quality and medical information.
COMPLIANCE OVERVIEWSafety & Regulatory Compliance Quick Links
Intelligence that transforms life sciences end-to-end.
When your destination is a healthier world, making intelligent connections between data, technology, and services is your roadmap.
TECHNOLOGIES OVERVIEWTechnology Quick Links
CLINICAL PRODUCTS
COMMERCIAL PRODUCTS
COMPLIANCE, SAFETY, REG PRODUCTS
BLOGS, WHITE PAPERS & CASE STUDIES
Explore our library of insights, thought leadership, and the latest topics & trends in healthcare.
DISCOVER INSIGHTSTHE IQVIA INSTITUTE
An in-depth exploration of the global healthcare ecosystem with timely research, insightful analysis, and scientific expertise.
SEE LATEST REPORTSFEATURED INNOVATIONS
-
IQVIA Connected Intelligence™
-
IQVIA Healthcare-grade AI®
-
IQVIA AI Assistant
-
Human Data Science Cloud
-
IQVIA Innovation Hub
-
Decentralized Trials
-
Patient Experience Solutions with Apple devices
WHO WE ARE
- Our Story
- Our Impact
- Commitment to Global Health
- Code of Conduct
- Sustainability
- Privacy
- Executive Team
NEWS & RESOURCES
Unlock your potential to drive healthcare forward
By making intelligent connections between your needs, our capabilities, and the healthcare ecosystem, we can help you be more agile, accelerate results, and improve patient outcomes.
LEARN MORE
IQVIA AI is Healthcare-grade AI
Building on a rich history of developing AI for healthcare, IQVIA AI connects the right data, technology, and expertise to address the unique needs of healthcare. It's what we call Healthcare-grade AI.
LEARN MORE
Meet the IQVIA AI Assistant
Your new expert analyst is here. Be at the forefront of data-driven decision-making with a new generative AI tool that enables you to interact with our products and solutions like never before. Get results you can trust, faster.
LEARN MORE
Your healthcare data deserves more than just a cloud.
The IQVIA Human Data Science Cloud is our unique capability designed to enable healthcare-grade analytics, tools, and data management solutions to deliver fit-for-purpose global data at scale.
LEARN MORE
Innovations make an impact when bold ideas meet powerful partnerships
The IQVIA Innovation Hub connects start-ups with the extensive IQVIA network of assets, resources, clients, and partners. Together, we can help lead the future of healthcare with the extensive IQVIA network of assets, resources, clients, and partners.
LEARN MORE
Proven, faster DCT solutions
IQVIA Decentralized Trials deliver purpose-built clinical services and technologies that engage the right patients wherever they are. Our hybrid and fully virtual solutions have been used more than any others.
LEARN MORE
IQVIA Patient Experience Solutions with Apple devices
Empowering patients to personalize their healthcare and connecting them to caregivers has the potential to change the care delivery paradigm.
LEARN MOREIQVIA Careers
Featured Careers
Stay Connected
WE'RE HIRING
"At IQVIA your potential has no limits. We thrive on bold ideas and fearless innovation. Join us in reimagining what’s possible.
VIEW ROLESBlog
Model Bias and Governance in Healthcare Insurance: Why It Matters Now
Sep 22, 2025
Artificial intelligence (AI) is transforming healthcare insurance. From streamlining underwriting to refining pricing models, data-driven decision-making is integral to competitiveness. But as these systems become more powerful, regulators, policymakers, and the public are demanding something just as important as accuracy: fairness.
For life and health insurance providers, this shift represents both a challenge and an opportunity. The challenge lies in proving that AI-driven models don’t create or perpetuate unfair discrimination. The opportunity lies in establishing governance practices that meet regulatory requirements and strengthen trust with consumers, regulators, and any interested parties.
The rising tide of scrutiny
Healthcare insurance providers operate in a heavily regulated sector. In the United States, state-level rules such as New York's Department of Financial Services Insurance Circular Letter No. 7¹ and Colorado's AI regulations for insurers² require companies to demonstrate that AI-driven underwriting and pricing decisions don’t disproportionately disadvantage protected classes or similarly situated insureds. Globally, emerging regulations are setting similar expectations for transparency, explainability, and fairness.
Similarly, the EU AI Act is setting a higher-water mark globally, classifying many AI systems used for insurance underwriting as a “high-risk” use case subject to strict transparency, human oversight, and fairness requirements.6 OECD AI Principles and regulatory developments in APAC markets, including Singapore and Australia, are also converging on the expectation that insurers can demonstrate robust governance across jurisdictions.8.9 For multinational providers, the challenge is harmonizing local compliance while maintaining consistent enterprise-wide standards.
The scrutiny is intensifying. Regulators increasingly want to see evidence that insurers have assessed how external data sources might create unintended disparities, measured outcomes to ensure consistent treatment of people with similar risk profiles, and implemented governance frameworks that can detect, mitigate, and prevent bias over time.
Public awareness and consumer advocacy
For consumers, bias is not an abstract concept, it can mean being denied affordable coverage, facing higher premiums, or experiencing delays in claims approval. Transparent governance gives individuals confidence that models are not treating them unfairly based on factors outside their control, such as zip or postal code, socioeconomic background, or prescription history. By centering governance on consumer impact, insurers can turn fairness into a differentiator rather than a defensive posture.
The risk of inaction is clear: models that unintentionally produce disparate impacts could lead to regulatory penalties, reputational harm, and loss of consumer trust. For example, consider where a U.S. insurer piloting a prescription-based risk score found that its model consistently overestimated risk for communities with limited access to pharmacies.4 By re-balancing the training data and adjusting feature weights, the insurer reduced disparate impacts while maintaining predictive accuracy.
Why now?
Several factors make bias and governance an urgent priority for healthcare insurers:
- Expanding data sources: The use of third-party datasets can introduce new potential for bias, as the provenance, completeness, and representativeness of such data can vary.5
- Complex model architectures: Modern AI models, from gradient-boosted ensembles to deep neural networks, can obscure the decision logic, making it harder to detect where bias originates.
Complex model architectures
A new layer of complexity comes from the use of generative AI and foundation models in insurance operations, such as automating claims documentation or supporting customer service chatbots. These systems introduce fresh risks, hallucinated outputs, opaque reasoning chains, and propagation of systemic biases across multiple downstream models. Insurers need to expand governance frameworks to cover these emerging technologies, ensuring oversight does not stop with traditional predictive models.
- Evolving regulatory expectations: Laws are moving from high-level principles toward prescriptive requirements, with some jurisdictions already requiring explicit fairness testing and reporting.
- Public awareness and consumer advocacy: Headlines about algorithmic bias in credit scoring, hiring, and healthcare have raised public expectations for fairness in automated decisions.6
What good practice looks like
Best-practice governance for model bias is a structured, proactive approach that ensures decisions are accurate and equitable. In healthcare insurance, this involves several key components.
Understanding the regulatory landscape: Insurers should map their AI use cases against applicable laws at the state, national, and potentially even international levels. This includes identifying protected classes, understanding definitions of unfair discrimination , and staying current with evolving guidance.
Defining fairness in context: Fairness is not one-size-fits-all. In insurance, definitions may differ between regulators, and between group-level and individual-level assessments. Organizations will want to decide which fairness metrics (e.g., demographic parity, equalized odds) are appropriate for their underwriting and pricing models, and document their rationale.
Mapping models for bias risk: Every model, from a mortality risk score to a prescription-based pricing factor, should be mapped for potential bias entry points. This means analyzing input data, feature engineering steps, model outputs, and downstream decision rules to identify where disparities could emerge.
Measuring and monitoring fairness: Bias detection involves robust statistical evaluation across three key areas: comparing predicted outcomes across demographic groups, using statistical significance tests to assess differences, and tracking changes over time to catch emerging disparities as data shifts. Monitoring should be continuous, not a one-time exercise. Feedback loops ensure that if models begin to drift toward biased outcomes, corrective action can be taken quickly.
Mitigating bias and documenting actions: When disparities are detected, mitigation strategies could involve adjusting training datasets to improve representativeness, modifying features or decision thresholds, or introducing post-processing adjustments to outputs.
Documentation is also essential. Regulators expect demonstrable proof with records showing what was tested, what was found, and what actions were taken.
Moving toward defensible AI in insurance
The end goal is creating a governance framework that is resilient to regulatory change, adaptable to new data sources, and capable of maintaining trust over the long term. In practice, that means embedding bias management into the entire AI lifecycle, from model design and development to deployment, monitoring, and retirement. Governance should be integrated with model risk management, underwriting policies, and operational workflows.
A defensible AI governance framework for healthcare insurance typically includes clear accountability, transparent methodologies, regulatory readiness, and global-local adaptability.
Why this matters for the future of insurance
Healthcare insurance providers want to innovate to remain competitive, while safeguarding against the risks that advanced analytics can introduce. Fairness in AI is a matter of business sustainability.
Done well, robust model bias governance can strengthen trust by demonstrating that fairness is embedded in decision-making, reduce the likelihood of costly disputes, regulatory interventions, or reputational crises, enable more confident adoption of new data sources and AI techniques by ensuring risks are understood and managed from the outset, and create a competitive advantage in markets where regulators, partners, and customers increasingly demand transparency.
The path forward
As AI continues to reshape healthcare insurance, bias management and governance become core capabilities. A successful approach involves structured, proactive, and transparent practices: mapping risks, selecting meaningful fairness metrics, embedding bias testing into regular workflows, and continuously improving governance frameworks.
The industry has reached a point where fairness in AI is a defining factor in long-term viability. In the coming years, leaders will build the most sophisticated and trustworthy models. By acting now, healthcare insurance providers can meet today’s expectations, prepare for tomorrow’s regulations, and ensure that the promise of AI is realized without compromising equity, integrity, or consumer trust.
To begin, insurers can prioritize by mapping the top three AI models in active use for bias risks, build an evidence pack that demonstrates compliance and fairness testing, and set up continuous monitoring to detect drift. These early actions create a defensible foundation for regulators, strengthen consumer trust, and build the internal momentum needed to expand governance across the enterprise. By starting strategically, insurers can position themselves to meet regulatory expectations with today’s rules and lead in tomorrow’s market.
Bibliography
1. New York State Department of Financial Services. Insurance Circular Letter No. 7 (2024): Use of Artificial Intelligence Systems and External Consumer Data and Information Sources in Insurance Underwriting and Pricing. 2024. Available at: https://www.dfs.ny.gov/industry-guidance/circular-letters/cl2024-07
2. Colorado Division of Insurance. Regulation 10-1-1 Governance and Risk Management Framework Requirements for Life Insurers’ Use of External Consumer Data and Information Sources, Algorithms, and Predictive Models. 2023. Available at: https://www.sos.state.co.us/CCR/GenerateRulePdf.do?ruleVersionId=11153&fileName=3%20CCR%20702-10
3. National Association of Insurance Commissioners. Principles on Artificial Intelligence. NAIC. 2020. Available at: https://content.naic.org/sites/default/files/inline-files/AI%20principles%20as%20Adopted%20by%20the%20TF_0807.pdf
4. Zink A, Rose S. Identifying Undercompensated Groups Defined by Multiple Attributes in Risk Adjustment. BMJ Health & Care Informatics. 2021 Sep 17;28(1):e100414.
5. Filabi A, Duffy S. AI-Enabled Underwriting Brings New Challenges for Life Insurance: Policy and Regulatory Considerations. Journal of Insurance Regulation. 2021 Aug 1;40(8).
6. Barocas S, Hardt M, Narayanan A. Fairness and Machine Learning: Limitations and Opportunities. MIT press; 2023 Dec 19.
7. Debevoise & Plimpton LLP. Europe’s Regulatory Approach to AI in the Insurance Sector. May 6, 2025. Available at: https://www.debevoise.com/insights/publications/2025/05/europes-regulatory-approach-to-ai-in-the-insurance
8. Organisation for Economic Co-operation and Development. OECD AI Principles. Originally adopted May 2019, updated May 2024. Available at: https://www.oecd.org/en/topics/sub-issues/ai-principles.html
9. Monetary Authority of Singapore. Principles to Promote Fairness, Ethics, Accountability and Transparency (FEAT). 2018. Available at: https://www.mas.gov.sg/-/media/mas/news-and-publications/monographs-and-information-papers/feat-principles-updated-7-feb-19.pdf
10. Australian Prudential Regulation Authority (APRA). APRA Corporate Plan 2025-26. 2025. Available at: https://www.apra.gov.au/apra-corporate-plan-2025-26
11. European Union. Artificial Intelligence Act. Adopted August 1, 2024; phased enforcement through August 2026. Official text available at: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024R1689