Using AI to Spot Insurance Fraud Without Losing Human Judgment

Insurance fraud has always been a tricky problem. Fraudsters find new ways to exploit gaps in the system, while insurers need to protect honest customers. Traditionally, human analysts reviewed claims for red flags, but this approach can be slow and inconsistent, especially as the volume of claims grows. In recent years, AI tools have been introduced to help insurers identify suspicious claims faster, but these systems need careful testing and human oversight to be reliable and fair.

Jalees Ahmad, a seasoned professional, is working to make AI both useful and trustworthy in fraud detection. He leads quality assurance efforts for AI-powered platforms that detect suspicious insurance claims. “AI is powerful, but it should support decisions, not replace them,” he said. “Our goal is to make sure underwriters can trust the system without losing their authority over final decisions.” His work sits at the intersection of technology, business, and compliance, ensuring that AI recommendations are accurate, explainable, and aligned with regulatory requirements.

Ahmad’s role involves designing and executing test strategies covering model accuracy, bias detection, explainability, and human-in-the-loop workflows. This helps ensure that AI outputs are not just technically correct but also actionable for real-world use. “It’s not enough for AI to flag potential fraud,” he explained. “We need to know why it flagged it, and ensure the reasoning aligns with real-world scenarios. Otherwise, you risk undermining the trust of both underwriters and customers.”

One challenge he faced early on was that similar claims often received different AI confidence scores, making it hard to determine what should be considered “correct.” To address this, Ahmad worked with data scientists to define acceptable ranges of confidence and behavior-based expectations rather than relying on fixed outputs. By building test cases around trends, thresholds, and explainability rather than exact values, he helped create a system that could flag truly suspicious claims while remaining consistent over time.

Another issue came from the system over-flagging claims, which caused confusion for underwriters and risked eroding trust. The professional developed edge-case scenarios and negative test cases to check borderline claims and validated override workflows so that humans could easily step in when needed. “We designed scenarios to test borderline claims and confirmed that humans could easily challenge AI suggestions,” he said. “The goal is not to replace judgment but to make it easier for humans to focus on what really matters.”

The impact of these efforts has been significant. False positives dropped by 42 to 47%, testing coverage for model validation exceeded 90%, and production defects related to AI recommendations were reduced by about 60%. This not only improved efficiency for underwriters but also increased confidence in the AI system. “Seeing AI and human reviewers work together efficiently is rewarding,” Ahmad noted.

He emphasizes that QA for AI differs from traditional testing. “It’s about consistency, boundaries, and trust,” he added. “AI should be treated as a decision-support tool, not a decision-maker. That mindset keeps the system fair, reliable, and aligned with regulations.” In practical terms, this means continuously monitoring AI behavior, checking for bias, and making sure humans can easily override recommendations when necessary.

For the insurance industry, the key lesson is that technology cannot replace human judgment. AI can process vast amounts of data and highlight patterns that might go unnoticed, but human insight ensures that decisions are fair, transparent, and accurate. According to Ahmad, “The best results come when AI highlights what matters and humans decide how to act. That combination protects customers and supports underwriters in making informed, compliant decisions.”

Ultimately, success in fraud detection relies on collaboration between AI and humans. Algorithms are excellent at spotting anomalies, but human judgment provides context, ethics, and accountability. Industry experts are showing that careful testing, thoughtful oversight, and human-in-the-loop systems are essential for making AI practical, trustworthy, and effective in insurance.