Ensuring Fairness: How Some AI Solutions Prevent Bias Against Policyholders

The insurance industry’s reputation for fairness and objectivity is built on a foundation of accurate risk assessment. As AI transforms how insurers evaluate risks and process claims, our industry’s foundational commitment to fairness presents challenges – among them, taking measures to manage and avoid AI model drift, which can contribute to bias and lower accuracy rates.  

However, this transformation also offers remarkable opportunities to create fairer insurance outcomes by applying the increased quantities of accurate data analysis and insights made possible by AI integration.  

The question isn't whether AI will change insurance operations – it's how these changes dovetail with our industry's long-standing dedication to equitable treatment. 

Human and AI Collaboration in Insurance Decision Making 

Recent developments in the excess and surplus lines market offer a blueprint for how AI can significantly enhance efficiency while preserving human judgment in critical decisions. However, the true test of AI's value lies not only in its speed or data processing capabilities, but in its ability to eliminate bias rather than reinforce it further.

Leading insurers are discovering that the most effective AI implementations don't replace human expertise – they expand it. AI enables underwriters to process information faster, particularly on lengthy coverage submissions, but the final judgment is exclusively a human responsibility.  

Human Expertise: Always Desired, Sometimes Required 

This approach recognizes a crucial reality – while AI excels at pattern recognition and data synthesis, human underwriters possess contextual understanding and ethical reasoning that algorithms cannot replicate. (Also, legally, some decisions can be made only by licensed, credentialed underwriters or claims adjusters.)

This partnership model addresses a fundamental concern about AI bias. Human experts anchoring the decision-making loop are emplaced to recognize patterns of discrimination that AI systems might inadvertently have “learned” from historical data.  

AI helps underwriters reach this critical "judgment phase" quicker by synthesizing and surfacing insights from vast amounts of information. Still, the actual risk assessment and pricing decisions benefit from human oversight that can identify and correct potential biases. 

Growing Regulatory Emphasis on Fair AI 

Around the world, regulators are establishing clear expectations for the ethical use of AI in financial services. In the United States, the NAIC AI Model Bulletin and the New York Department of Financial Services’ proposed guidance on AI and data governance emphasize transparency, explainability, and documented human oversight.  

Globally, the EU AI Act introduces similar requirements for risk classification and accountability. Together, these frameworks signal that fairness in AI is not only a moral obligation but also a compliance expectation, requiring insurers to maintain auditable processes that demonstrate equitable treatment and transparency in automated decision-making. 

Reducing AI Bias in Insurance Systems 

Preventing AI bias requires more than meeting regulatory requirements – it demands a proactive approach that begins with data selection to ensure fairness by using datasets large and broad enough to capture complexity in predictive outcomes based on neutral decision factors.  

The most effective strategies focus on these key areas:

• Diverse and Representative Training Data: AI systems learn from the data they're fed, making data quality the first line of defense against bias. This means ensuring training datasets reflect a full range of customer demographics, underwriting scenarios, and claims patterns – and that the broad datasets are used to avoid “putting a thumb on the scales.” When historical data contains unfair discriminatory patterns (i.e., underwriting guidelines referring to “race, national origin, sex, religion,” and other protected classes), AI will replicate and potentially amplify these biases unless specifically trained to recognize and correct them.
• Explainable Decision-Making: Modern AI systems must be able to explain their reasoning in terms that underwriters and claims professionals can understand and validate. This transparency enables human experts to identify when AI recommendations reflect biased patterns rather than legitimate risk factors. Chain-of-thought (CoT) prompting, which breaks down tasks into discrete steps to enhance understanding of internal reasoning, and Explainable AI (XAI), which focuses on external transparency, offer insurance users ways to eliminate “black-box” AI. Human-in-the-Loop (see below) review of outcomes from CoT is a best practice for confirming the model correctly interpreted its instructions.
• Continuous Performance Monitoring: Bias isn't a one-time problem – it can emerge as AI models encounter new data patterns, amplify what they “see” in the data, or even when the accepted societal understanding of fairness evolves. Real-time monitoring systems that track AI decision patterns across different cohorts (age, educational attainment, race, etc.) can identify emerging biases before they impact policyholders. This includes monitoring drift in model performance that might indicate the system is acquiring new biases from recent data.
• Human-in-the-Loop Safeguards: The most sophisticated bias prevention strategy involves maintaining meaningful human oversight throughout the AI decision process. This goes beyond simple review and approval – it requires training underwriters and claims professionals to recognize potential bias indicators and empowering them to override AI recommendations when necessary.
• Regular Algorithmic Auditing: Subject matter experts must regularly evaluate AI models against diverse test scenarios to ensure they perform fairly across all customer segments. This includes testing with edge cases and unusual scenarios that might reveal hidden biases in the system's reasoning. 
  
  

Why Fair AI Strengthens Insurance Business Outcomes 

Implementing bias-free AI systems isn't just an ethical imperative – it's a competitive advantage. Fair AI systems produce more accurate risk assessments by avoiding the distortions that bias introduces. When AI systems unfairly discriminate against certain customer groups, they ignore valid market segments and potentially misprice risks based on factors rather than actual risk indicators.

Moreover, as regulatory scrutiny of AI systems intensifies in domestic and global markets, insurers with demonstrably fair AI applications will be better positioned to meet evolving compliance requirements. Insurance’s reliance on human experts rendering decisions impacting their policyholders reflects our dedication to customer trust that goes beyond efficient processing. It is a commitment built on demonstrably fair treatment for all. 

Fair AI Implementation Strategies for Insurance Leaders 

Insurance executives evaluating AI solutions should select systems designed with fairness as a core principle. This means partnering with AI providers who can demonstrate their commitment to bias prevention through concrete technical measures, not just policy statements.

Successful implementations typically involve establishing AI governance committees that include technical experts and business leaders from underwriting, claims, and compliance functions. These teams ensure that AI initiatives align with the company's ethical standards and business objectives while maintaining human oversight expected by customers and mandated by regulators.

Data governance frameworks should evolve to address AI-specific challenges, including clear protocols for data usage, storage, and the ongoing monitoring required to detect bias. This includes establishing feedback loops between AI systems and subject matter experts who can identify when algorithms are producing questionable results.

The insurance industry's experience with AI bias prevention offers lessons for any financial services sector undergoing its own AI transformation.  

Insurers can responsibly achieve operational efficiency goals by leveraging AI’s analytical capabilities while maintaining human expertise in critical decision-making. Fair AI doesn’t limit the technology's capabilities. It’s about insurers continuing our long tradition of directing new technologies toward more accurate, unbiased decision-making.

The future of insurance AI lies not in replacing human judgment, but in creating systems that enhance human decision-making while actively safeguarding against biases that can distort risk assessment and policyholder outcomes. This approach ensures that our industry becomes more efficient and fairer, delivering on our fundamental promise to treat policyholders equitably based on their actual risk profiles rather than irrelevant characteristics.  

Ultimately, fairness in AI is a leadership choice that defines regulatory compliance, strengthens customer trust, and reflects an organization’s long-term commitment to integrity and transparency. Leaders who prioritize fairness set the standard for how technology and ethics can work together to shape a more resilient and equitable insurance industry. 

Want to benchmark where your peers really stand with AI today? Download the State of AI Adoption in Insurance 2025 report for exclusive insights, trends, and data to guide your strategy.