Calculate the ROI of AI Fraud Detection for Your Claims Department

Independent insurance agencies and benefits platforms frequently find that their existing AMS platforms, such as Applied Epic, Vertafore, or HawkSoft, excel at record-keeping but fall short in sophisticated fraud detection. These systems generally rely on basic, rule-based flags that can identify obvious discrepancies, like duplicate invoice numbers, but struggle with more subtle, evolving patterns of fraud. They lack the native capability to perform advanced analysis across large, disparate datasets, which is essential for uncovering complex fraud schemes.

Consider the common challenge of a fraud ring targeting auto claims. They might submit several low-value claims over months, each just below the threshold for manual enhanced review, using slightly different claimant names or even distinct body shops. Your AMS, designed for transactional record-keeping, treats these as isolated incidents. It cannot easily connect a shared phone number from one FNOL report to a specific vehicle VIN from another claim, especially if policyholders or names are intentionally varied. The data model within these platforms is not built for the kind of cross-claim correlation needed to spot these connections.

The architectural limitation means AMS platforms aren't designed for vectorized similarity searches or real-time pattern analysis across both structured and unstructured data (like adjuster notes, images, or policy documents). Attempting to force this functionality through custom fields, extensive manual reporting, or convoluted workflows often results in systems that become unmanageable or time out during queries. For instance, you cannot readily ask Vertafore to 'show all claims in the last 18 months that share a contact number, even if the policyholder name differs across those claims.'

This gap forces experienced adjusters to rely heavily on institutional knowledge and memory to identify suspicious claims. This method becomes unsustainable with staff turnover, increasing claim volumes, or when dealing with high-frequency, low-value fraud that incrementally impacts loss ratios. The cost extends beyond the fraudulent payouts; it includes the significant time adjusters spend manually cross-referencing records for claims that 'feel off,' diverting their focus from legitimate customer service and efficient claims processing. This also impacts workflows such as policy comparison where fragmented data prevents accurate side-by-side analysis, and renewal processing, where incomplete document collection can delay applications.

Syntora would approach the challenge of fraud detection by first performing a detailed audit of 12-24 months of your historical claims data, pulled directly from your AMS platform. The objective would be to pinpoint the specific data features and behaviors that statistically correlate with past fraudulent activity unique to your book of business, rather than applying generic industry patterns. This initial phase would culminate in a comprehensive data quality report and a prioritized list of the most predictive signals, which could include anomalies in claim filing timestamps, inconsistencies in photo metadata, or undisclosed network links between claimants and service providers.

The core of the system would be engineered to process and analyze unstructured text from First Notice of Loss (FNOL) reports, adjuster notes, and other relevant documents. We have built document processing pipelines using Claude API for similar tasks with financial documents, and the same pattern applies to insurance claims documents. The Claude API would parse this text, extracting key entities and converting the contextual information into numerical vectors. These vectors would then be stored in a Supabase Postgres database, utilizing the pgvector extension to enable highly efficient similarity searches across your entire claims history.

A FastAPI application, deployed on AWS Lambda for cost-effective, pay-per-use scaling, would expose an API endpoint. This API would be designed to provide a fraud score and a clear, plain-English explanation of the underlying flags, with a target response time under 200ms. The intention is not to introduce a separate dashboard that adjusters must learn. Instead, the delivered system would be an API that integrates with your existing AMS. When an adjuster opens a new claim in Applied Epic, a background call to this API would return a real-time fraud assessment, such as, 'Warning: Claimant's phone number is associated with two prior claims under different names.' This functionality would act as a co-pilot, seamlessly flagging claims for review within the adjuster's current workflow, enhancing their capabilities without requiring a disruptive change in tools or processes.

Projects of this complexity typically involve a discovery and design phase of 2-4 weeks, followed by a build and integration phase that can range from 6-12 weeks, depending on the volume and variety of historical data provided by the client, and the specific integration points with their AMS or CRM platforms like Hive.