Your fraud alerts tripled. Your SIU team didn't.

Current insurance fraud detection relies on Special Investigations Units, often triaging rule-based referrals. Industry estimates indicate 8-10% of claims are fraudulent, amounting to $45B annually. ML scoring from vendors like Shift Technology and FRISS now identifies significantly more suspicious claims than traditional methods, shifting the primary bottleneck from detection to thorough investigation. SIU investigators and fraud analysts drive this critical activity.

AI fraud detection vendors such as Shift Technology and FRISS primarily act as model providers. They use machine learning to score claims, identifying 3-5 times more suspicious activities than prior rule-based SIU systems. This advancement allows for a more comprehensive flagging of potential fraud, transforming the workflow. The focus then shifts to the SIU team needing to investigate this increased volume efficiently, rather than spending resources on initial detection.

Investing in advanced insurance fraud detection offers significant financial returns. Given that 8-10% of claims are fraudulent, even a marginal improvement, such as a 1% increase in fraud detection, can lead to over $400 million in loss ratio impact for auto insurance alone. Our model projects substantial operating margin improvements by streamlining the entire fraud management process. The primary value comes from reducing payouts on fraudulent claims and improving operational efficiency for SIU.

AI is highly mature in core insurance fraud detection tasks, specifically in ML fraud scoring and network analysis for identifying organized crime rings. These capabilities effectively triage suspicious claims, guiding SIU efforts. However, AI's role is primarily to enhance detection; human SIU investigators remain essential for detailed case closure, legal processes, and complex decision-making. Voice analytics for stress indicators in recorded statements represents an emerging capability, but it is not yet universally deployed.

Implementing new AI fraud detection systems involves several stages. Initially, it requires robust data integration from existing core systems, including claims, underwriting, and billing. This provides a holistic view. Subsequently, models are trained using historical fraud data to establish baselines and identify emerging patterns. Integration with SIU workflows then enables seamless triage and case management. Timelines typically span several months, influenced by data readiness and organizational change management capabilities.

Operating an internal AI fraud platform, like the Moative Bastion system, offers unique advantages over external vendor solutions. Our approach focuses on consolidating fraud signals across the entire policy lifecycle, encompassing claims, underwriting, and billing. This enables the detection of sophisticated fraud rings mid-policy, not merely at the claim stage. Building internally also allows for owning the intellectual property and tailoring the solution precisely to an organization's specific data and operational nuances, moving beyond generic models.

The Moative Bastion platform integrates seamlessly with existing core insurance systems, such as Guidewire or Duck Creek. It ingests data from claims, underwriting, and billing modules. Our approach overlays intelligent fraud detection capabilities without necessitating a complete overhaul of your existing infrastructure. The platform then feeds enriched fraud alerts and actionable insights directly back into your SIU case management systems, augmenting current workflows and minimizing disruption. Integration is designed for flexibility and continuous operation within your established ecosystem.

While AI significantly boosts the volume of suspicious claims identified, the key benefit lies in cost-efficiency gains within the investigation process. With model-driven triage, our model projects SIU teams can process cases with greater focus and speed. The system reduces the average cost per *detected* fraudulent claim by eliminating manual initial screening. The goal is to optimize the entire lifecycle, ensuring faster resolution through more targeted investigations, which leads to better loss ratio management and prevents revenue bleed.