Ensuring Responsible AI Usage in Insurance Operations
Artificial intelligence (AI) is transforming workflows across various industries, including the insurance sector. According to a recent report, AI usage in global insurance surged to 48% in 2025, a significant increase from 29% in 2024. However, this rapid growth has raised concerns regarding the risks associated with AI implementation.
Emerging AI Risks
AI-related risks often begin during the model training process. Biases can develop during this phase, leading to discriminatory outcomes in underwriting or claims processing. For instance, if an AI model is trained on biased data, it may produce biased decisions that unfairly affect certain groups.
Additionally, AI systems can “hallucinate”, meaning they might present incorrect or nonsensical information as facts. Reliability is another concern; AI does not always provide consistent answers when faced with the same input. This inconsistency can lead to a lack of trust among brokers and policyholders.
Moreover, AI can make decisions without clear explanations. For example, if a claim is denied without a comprehensible rationale, it can damage trust and increase the risk of lawsuits. A notable case involved a company facing legal action over its AI algorithm, which allegedly denied over 300,000 claims in an unreasonably quick manner.
Unchecked automation can also scale flawed decisions quickly. AI systems often handle sensitive data, creating potential vulnerabilities for data breaches. Therefore, brokers and insurers must take proactive steps to mitigate these risks.
Risk Mitigation Strategies
Brokers and insurers can support risk mitigation by:
- Spotting and reporting biased decisions to push for fairer AI models
- Reviewing AI-generated content to catch errors or misleading information
- Flagging inconsistent outcomes to improve model reliability
- Demanding clear reasoning behind AI decisions, especially for denials
- Escalating client issues early to reduce the risk of legal or public relations fallout
- Identifying patterns of faulty decisions before they spread widely
- Ensuring clients understand how their data is used and promoting secure handling
Developing Risk-Aware AI Systems
To avoid the aforementioned risks, establishing guardrails at the project’s outset is crucial. This involves clearly defining team responsibilities for building, testing, reviewing, and approving AI models. Regular testing is vital to ensure accuracy and identify biases. Alerts should be set up to catch inconsistencies or illogical outputs, such as sudden spikes in claim rejections.
Furthermore, creating a transparent and accessible dashboard to monitor model performance is essential. It is important to ensure that every AI-assisted underwriting decision is traceable, with records maintained for compliance and regulatory review.
Barriers to AI Adoption
Despite the benefits, there is still hesitancy regarding AI adoption in the insurance sector. Common misconceptions include fears that AI is untrustworthy due to its probabilistic nature and concerns about job displacement. However, AI should be viewed as a tool that complements human judgment rather than replacing it. When utilized responsibly, AI can lead to smarter, fairer, and more accountable decision-making.
Building Confidence in AI Decisions
Brokers and insurers can enhance confidence and fairness in AI decisions by:
- Advocating for transparency in AI decisions
- Monitoring for consistency and fairness
- Educating clients on AI-driven processes
- Raising concerns early
- Requesting human oversight in critical cases
- Ensuring data privacy and security awareness
- Pushing for fair and ethical AI development
- Staying informed and up-to-date
- Supporting regulatory compliance
- Advocating for client appeals and review mechanisms
The Future Role of Brokers
As the role of AI in the insurance industry evolves, it is crucial to understand that AI does not replace the broker; rather, it enhances their capabilities. These tools assist brokers by offloading repetitive tasks, allowing them to focus on faster, smarter interactions with carriers and clients.
Brokers can evolve their roles by learning how to supervise, tune, and collaborate with AI tools, positioning themselves as not only risk experts but also as digital orchestrators in the evolving landscape of insurance.
