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Building Trust in AI With Proactive Risk Management

Author

Kelly Coomer
Senior Vice President & Chief Information Officer, Sammons Financial Group

March 2026

Artificial intelligence (AI) and Generative AI (GenAI) are transforming the insurance industry, driving operational efficiencies, enabling personalized customer experiences, and fostering innovative product development. Yet, these benefits come with inherent risks — from algorithmic bias to regulatory uncertainty — that require structured oversight. To navigate this complexity, insurers increasingly rely on governance frameworks that provide a systematic approach to identifying, assessing and mitigating AI-related risks.

The Need for AI Governance

Unlike traditional software, AI systems often operate as opaque “black boxes,” generating outputs through intricate, nonlinear processes that are difficult to interpret. This lack of transparency introduces ethical and operational challenges, particularly in an industry built on trust and fairness.

Key risk areas include:

  • Bias and Discrimination: Models trained on incomplete or skewed datasets can perpetuate inequities in underwriting and claims decisions.
  • Data Privacy and Security: AI’s reliance on large-scale data aggregation heightens exposure to breaches and misuse.
  • Regulatory Fragmentation: While the European Union (EU) has enacted comprehensive AI legislation, U.S. regulations remain fragmented, creating compliance uncertainty.

Without proactive governance, insurers risk reputational damage, financial penalties, and erosion of consumer confidence. A structured framework helps organizations move beyond ad hoc controls toward consistent, scalable governance.

Global Regulatory Landscape

The regulatory environment underscores the urgency of adopting frameworks. The EU AI Act, effective August 2024, sets a global benchmark by categorizing AI systems into four risk tiers: unacceptable, high, limited and minimal. High-risk applications — such as those used in underwriting — must meet rigorous standards for transparency, data governance and human oversight.

In contrast, U.S. efforts remain decentralized. The 2023 Executive Order on AI outlines safety principles but lacks enforceable mandates. This regulatory gap highlights the importance of industry-led frameworks that establish consistent governance practices across organizations.

Why Frameworks Matter

Frameworks serve as practical tools for operationalizing responsible AI practices. They embed risk evaluation into every stage of the AI life cycle — from model design and vendor selection to deployment and monitoring. By leveraging a framework, insurers can:

  • Standardize Risk Assessment: Create a common language and methodology for evaluating AI systems across departments.
  • Prioritize Resources: Focus compliance and oversight efforts where they matter most — on high-risk applications.
  • Enhance Regulatory Readiness: Anticipate evolving global standards and demonstrate proactive compliance to regulators.
  • Build Stakeholder Trust: Communicate governance measures transparently to customers, partners and investors.
  • Enable Scalable Innovation: Balance risk mitigation with agility, allowing teams to innovate confidently without fear of regulatory setbacks.

In short, frameworks transform governance from a reactive compliance function into a strategic enabler — positioning insurers as leaders in ethical, customer-centric innovation.

Strategic Themes for Insurers

To maximize the value of frameworks, insurers should align governance with five strategic themes:

  1. Transparency and Explainability: Interpretability is critical for regulatory compliance and stakeholder trust, requiring models to provide clear decision pathways.
  2. Ethical Deployment: Governance must extend beyond technical safeguards to include fairness, accountability and consumer protection.
  3. Continuous Monitoring: Ongoing audits and recalibration are essential to maintain accuracy and fairness as AI systems evolve.
  4. Collaborative Standards: Industrywide alignment of best practices will prevent fragmented approaches and systemic vulnerabilities.
  5. Innovation with Accountability: Governance should enable responsible experimentation, balancing risk mitigation with competitive agility.

The AI Risk Evaluation Framework

The AI Risk Evaluation Framework (AIRE) — developed in collaboration with the LIMRA and LOMA AI Governance Group, a committee of 140 senior executives from over 70 companies — offers a tailored approach for the insurance sector. AIRE provides a structured methodology for assessing and mitigating AI risks through two core components:

  1. Risk Classification Model
    Expanding on the EU AI Act, AIRE introduces five categories:
  • Unacceptable Risk: Prohibited systems, such as those enabling manipulative practices
  • High Risk: AI used in critical functions like underwriting and fraud detection
  • Limited Risk: Applications with moderate impact, such as customer service automation
  • Minimal Risk: Low-impact tools, often internal-facing
  • Inadvertent AI (Unique to AIRE): Unintended AI embedded in third-party or legacy systems
  1. Evaluation and Scoring Mechanism
    AIRE employs decision trees and attribute-based scoring to assess risk across dimensions such as:
  • Data Integrity: Accuracy, representativeness, and bias mitigation
  • Transparency: Explainability of outputs and decision logic
  • Security: Resilience against adversarial attacks
  • Compliance: Alignment with ethical and regulatory standards

This structured approach ensures proportional governance — avoiding unnecessary constraints on low-risk systems while applying rigorous oversight to high-risk applications.

Implications for the Insurance Sector

Adopting a framework like AIRE is not merely a compliance exercise — it is a strategic imperative. By embedding risk evaluation into the AI life cycle, insurers can:

  • Accelerate innovation responsibly
  • Enhance trust through transparency and ethical practices
  • Mitigate operational and reputational risks
  • Position themselves as leaders in responsible AI adoption

AI offers transformative potential, but its risks are complex and multifaceted. Frameworks provide the guardrails needed to balance innovation with accountability. The AIRE framework equips insurers with a practical, scalable approach to governance — ensuring that technological progress aligns with industry values and consumer protection. In an era where trust is paramount, proactive risk management is not optional; it is the foundation of sustainable success.

Kelly Coomer leads the LIMRA Artificial Intelligence Governance Group aimed at driving responsible adoption of AI within the Life Insurance industry.

 

 

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