In insurance claims, Quality Assurance (QA) evaluates the accuracy and consistency of claim handling to help ensure strong outcomes for both the insurer and the policyholder. Put simply, it defines the quality of the claims work product. QA teams uphold that standard by making sure claims are resolved in line with company policies, procedures, and regulations.
By combining industry expertise with structured review processes—and increasingly leveraging technology and automation in quality assurance—QA teams help insurers stay fair and consistent, while lowering risk and building trust with policyholders.
Their work starts with selecting claims for review, traditionally through random sampling, but now more often through data-driven methods that flag high-risk or complex cases. Once a claim is chosen, QA teams take a close look at how each claim was handled, digging into documentation, adjuster decisions, settlement strategies, and communications to uncover issues that could affect accuracy or compliance. But their job isn’t just about catching mistakes. They also provide feedback and coaching to adjusters, helping them refine their decision-making.
QA teams act as a bridge between frontline claims professionals and leadership, offering insights into training needs, process improvements, and trends that impact claim handling. By combining industry expertise with structured review processes—and increasingly leveraging technology and automation in quality assurance—they help insurers stay fair and consistent, while lowering risk and building trust with policyholders.
The Value of Human-Led QA Programs
Traditional, human-led QA brings a level of expertise and judgment that automated systems simply can’t match. Experienced professionals can evaluate complex claims with nuance, factoring in intent, unique circumstances, and liability decisions that don’t always fit neatly into predefined rules or AI models. They also provide real-time coaching to adjusters, enhancing consistency and claims-handling skills.
In addition, human reviewers identify regulatory risks and potential bad-faith exposures, protecting insurers from compliance issues and penalties. More than just enforcing processes, QA teams serve as subject matter experts, offering qualitative insights that data alone might miss. This combination of analysis and professional judgment is essential for delivering fair, accurate, and customer-focused claim outcomes.
Where AI Fits into Quality Assurance
Despite its strengths, traditional QA has its challenges. It’s labor-intensive, pulling experienced staff away from active claims work. Not only that, random sampling can miss larger patterns or systemic issues. For example, an invoice from a repair shop may appear normal to a seasoned reviewer. But when compared with tens of thousands of claims involving the same vehicle, same point of impact, and similar damage, a very different conclusion might emerge.
Reviewer interpretations can also vary, leading to inconsistencies. And since many (but not all) QA programs focus on past claims, issues are often caught too late to influence outcomes.
As seasoned professionals retire, resources shrink, and regulatory demands grow, insurers need a smarter, more scalable approach to QA. This is where Generative AI (GenAI) can make a real impact.
By integrating GenAI into QA, insurers can test and refine model performance in a highly controlled setting.
By integrating GenAI into QA, insurers can test and refine model performance in a highly controlled setting. This helps spot potential biases and monitor compliance without directly affecting real claims. It also provides valuable insights into how AI spots inconsistencies and interprets data, offering a window into how it can work alongside human oversight. These learnings give insurers the information they need to adjust models, helping decisions become more accurate and fair, while also cutting down on model/operational risk.
A Smarter, Hybrid Approach That Combines Human Expertise
with AI
By 2027, GenAI will augment 30% of all knowledge work, up from 0% in 20231. Other studies suggest automating tasks could save up to 20% in time2. Like many other insurance functions, the future of claims QA lies in blending human expertise with GenAI-driven efficiency.
Treating GenAI as a trusted colleague, rather than a new technology, can ease concerns about job loss. It’s especially useful for analyzing vast amounts of structured and unstructured data, scanning for anomalies, patterns, and risks in real time. Instead of relying on random file reviews, teams can use GenAI to zero in on the claims that need the most attention.
But GenAI can’t (and shouldn’t) replace human judgment. Claim organizations still need experienced QA professionals to not only interpret nuances and provide greater context, but also guide difficult decisions. A hybrid approach offers the best of both worlds, with GenAI delivering speed and precision, while humans bring the kind of reasoning and fairness that no model can replicate.
In addition, GenAI isn’t immune to the “garbage in, garbage out" problem. That’s why it requires strong and secure IT infrastructure for data management, as well as oversight from seasoned QA professionals3.
Key Considerations for Building a Hybrid QA Program
- Establish Clear Business Goals & Measurable KPIs
A hybrid QA program should have clear business objectives, whether that’s improving compliance, reducing leakage, analyzing customer interactions, enhancing adjuster training, or increasing overall efficiency. Set measurable KPIs to track progress and ensure that both AI and human oversight are working in collaboration to deliver results. - Use AI for Smarter Claim Selection
AI can use historical data to spot patterns, like repeated errors or signs of potential fraud. This moves QA beyond random sampling (which still has its value and should not be replaced entirely), making it more targeted and efficient. - Automate Routine QA Tasks
AI can scan files for gaps, such as missing documentation, coverage or liability errors, or deviations from best practices, reducing manual work and allowing QA teams to focus on more complex issues. Real-time dashboards tracking QA trends can help leaders make faster, more informed decisions. - Maintain Human Oversight for Complex Cases
Even when AI flags potential issues, experienced QA claim professionals should always have the final say, especially for cases requiring deeper judgment. Their role includes validating AI findings and catching errors that the model might miss, while also helping to improve its performance over time. - Leverage GenAI for Adjuster Training & Coaching
AI can analyze trends and spot common mistakes, making it easier to tailor training programs for adjusters and supervisors. It can also power real-time virtual assistants that guide adjusters through decisions using relevant policies and procedures, while keeping regulatory requirements in mind. Some organizations even use LLM’s to assess claimant calls, evaluating customer service quality based on key interaction cues. - Ensure Compliance & Ethical Safeguards
AI must align with regulations, ethics, and fair claims practices. Regular review of AI outputs, along with human oversight, help prevent bias and maintain trust. - Commit to Continuous Improvement
As regulations change over time, AI models must be regularly refined to ensure they don’t drift or reinforce outdated patterns. Comparing AI and human reviews can reveal gaps and opportunities to improve. Keeping that process effective over time depends on close collaboration across teams, from QA to data science to claims leadership.
A Balanced, High-Impact QA Program Is the Future
Integrating AI with human expertise moves QA from reactive to proactive. High-risk claims can be flagged earlier. Compliance risks can be addressed faster. Adjusters can get real-time coaching. Low risk claims can be scored and moved along efficiently.
At the same time, human oversight keeps claims handling grounded in fairness and sound judgment. This balance leads to more accurate decisions, lower costs, and better compliance. Not to mention, stronger trust with policyholders.
To make it work longterm, insurers must invest in upskilling QA teams with the skills needed to oversee AI effectively. Just as adjusters learn to interpret AI-driven insights, QA professionals will need to work closely with data scientists to validate outputs and guide the technology’s use. In doing so, QA becomes more than just a checkpoint, but a strategic asset that drives value.
How to Avoid AI Feedback Loops
One critical risk is using the same AI model for both claims decisions and QA. This creates a feedback loop, where biases or errors can go unchecked and become reinforced over time4. When both systems rely on similar logic and training data, potential issues can slip through undetected, leading to compliance risks and strained trust across claims and QA teams. To avoid this, insurers should consider:- Using independent QA models built with different methodologies and data sources, if possible
- Maintaining human oversight, regular audits, and bias detection
- Applying adversarial testing, explainable AI (or XAI), and regular compliance checks
Keeping QA and decision-making models separate can improve objectivity and reduce risk, while also building trust in how AI is used across the organization.
From Manual Checks to Meaningful Insights
When QA shifts from a manual process to a more dynamic, hybrid model, it becomes something more than a checkpoint.
As insurers evolve, so does the role of quality assurance. The most forward-looking organizations won’t choose between human judgment and AI. Instead, they’ll use both to their advantage. When QA shifts from a manual process to a more dynamic, hybrid model, it becomes something more than a checkpoint. It helps claims teams make better decisions, respond to shifting demands, and create a more consistent, high quality claims experience. Insurers that take this step now are setting themselves up to adapt faster and define quality on their own terms as the industry continues to shift.
References:
- Gartner, Primary Impact of Generative AI on Business Use Cases, Feb 2025
- Zurich Insurance, How accurate data and AI can transform claims and help customers build resilience https://www.zurich.com/commercial-insurance/sustainability-and-insights/commercial-insurance-risk-insights/how-accurate-data-and-ai-can-transform-claims-and-help-customers-build-resilience
- BCG, GenAI Will Write the Future on Insurance Claims https://www.bcg.com/publications/2023/the-future-of-insurance-claims
- Proceedings of Machine Learning Research, Data Feedback Loops: Model-driven Amplification of Dataset Biases https://proceedings.mlr.press/v202/taori23a/taori23a.pdf