Smart claims and the evolution of FWA detection in health insurance

Health insurers are entering a period of mounting pressure. Medical costs continue to rise globally, while regulatory scrutiny intensifies and customer expectations for transparency and speed grow higher. According to Willis Towers Watson's report, medical costs are expected to increase by an average of 10.4%. Against this backdrop, fraud, waste and abuse (FWA) remains one of the most persistent structural challenges facing the industry. Although the problem is well known, traditional approaches to managing it are increasingly misaligned with the scale and complexity of modern healthcare systems.

Understanding the FWA trio

FWA is often discussed as a single issue, but it represents three distinct dynamics. Fraud is deliberate and intentional. Waste typically results from inefficiency or overuse, such as unnecessary tests or prolonged hospital stays. Abuse sits in a grey zone, involving practices like upcoding or exploiting policy loopholes without outright deception. While fraud attracts the most attention, waste and abuse quietly account for a significant share of avoidable costs, gradually eroding the sustainability of the system and patient trust.

Historically, insurers relied on manual claim reviews conducted by skilled assessors who examined both the financial accuracy and medical necessity of claims. As claim volumes increase and benefit structures grow more complex, this model struggles to scale. While financial validation can often be automated with rules and thresholds, evaluating whether a treatment was clinically justified requires medical expertise and contextual judgement.

Traditionally, it has led to backlogs, inconsistent decisions, and delayed reimbursements. To mitigate uncertainty, many insurers introduced pre-authorization processes for planned treatments. While this step improves predictability and risk control, it also adds operational overhead and increases reliance on scarce expert resources. Is there a better way?

The AI revolution in FWA detection

The AI transformation has begun to reshape this landscape. Early applications of analytics and machine learning helped insurers identify anomalous claims patterns and prioritize cases for review. However, these systems were often opaque and limited to narrow tasks, making them difficult to audit or adapt as fraud tactics evolved. Over time, this has given way to more advanced AI-driven approaches that combine data analysis with contextual reasoning.

A key shift is the move from static detection models toward more adaptive, agent-based systems. Rather than simply flagging anomalies, these systems can analyze historical data, interpret policy logic, assess medical guidelines, and continuously learn from new outcomes. Importantly, they are increasingly designed to support, not replace, human decision-makers. Assessors remain accountable for final judgments, while AI acts as a decision-support layer that surfaces insights, explains reasoning, and reduces cognitive load.

Agentic AI continues to evolve. According to Gartner, by 2029, it will be capable of resolving 80% of common customer service issues without human intervention. This, in turn, will reduce operational costs for insurance businesses by 30%.

This human-AI collaboration is the way forward, as it addresses one of the most persistent concerns surrounding automation in insurance: trust. Regulators and auditors require transparency into how decisions are made, especially when they affect patient access to care. Explainable AI models, combined with structured decision summaries, help insurers demonstrate compliance while maintaining operational efficiency. For assessors, this means less time spent gathering information and more time applying professional judgement where it matters most.

Implementing AI for modern FWA detection

To effectively combat fraud, waste and abuse (FWA), insurers must move beyond static automation toward autonomous systems capable of adapting to emerging fraud patterns in real-time. By integrating AI into the claims lifecycle, insurers can transform FWA detection from a reactive hurdle into a proactive strategic advantage.

Practical steps for AI integration

●      Data normalization & enrichment: Leverage fine-tuned Generative AI to clean and label unstructured claim data. These models can achieve over 95% accuracy, drastically reducing the manual effort required to prepare data for analysis.
●      Predictive anomaly detection: Deploy explainable AI (XAI) models on historical data to flag irregularities in pricing and medical necessity. Traditional machine learning is ideal here, providing the transparency and auditability required for regulatory compliance.
●      Autonomous evidence gathering: Utilize Agentic AI to retrieve policyholder history and past claims. These agents can autonomously contact healthcare providers to request clarifications on treatments, closing information gaps without human intervention.
●      Summarization & decision support: Use a medical-grade large language model (LLM) to synthesize findings into a concise report. This provides claims adjusters with a clear recommendation, allowing for faster, high-confidence adjudication.

The path forward

While this approach can deliver a 10x increase in processing speed, success depends on bridging the gap between legacy infrastructure and modern AI. By prioritizing organizational readiness and cross-functional collaboration, insurers can evolve FWA tools into transparent, human-centered systems. Ultimately, those who master this digital transformation will best balance operational efficiency with the fundamental mission of maintaining trust within the healthcare ecosystem.