Synopsis
Sophisticated fraud hides in the details of unstructured invoices. This case study breaks down how the Hepha AI platform moves beyond surface-level analysis. We demonstrate how our synthesis of Generative AI and proprietary Knowledge Graphs reads and categorizes every single line item to uncover subtle yet critical anomalies—such as a prescribed drug that doesn't align with the patient's diagnosis.
The result is irrefutable, explainable evidence that empowers insurers to prevent leakage with surgical precision.
More Content Coming Soon
We're working on a comprehensive deep-dive that will explore the technical architecture, real-world implementation challenges, and measurable outcomes of this solution. This expanded case study will include:
- Detailed breakdown of our Knowledge Graph construction methodology
- How we combine LLMs with domain-specific medical knowledge bases
- Specific examples of detected fraud patterns and anomalies
- Quantifiable impact on fraud detection rates and false positive reduction
- Technical architecture and integration patterns with existing insurance systems
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The Challenge
Medical insurance fraud often manifests in subtle inconsistencies that human reviewers can easily miss when processing thousands of claims. Traditional rule-based systems struggle with the nuanced medical knowledge required to identify mismatches between prescribed medications and documented diagnoses.
Our Approach
Hepha AI's platform leverages the power of Generative AI grounded by comprehensive medical Knowledge Graphs to analyze unstructured invoice data at scale. By combining the flexibility of LLMs with the precision of domain expertise, we enable automated detection of complex fraud patterns while maintaining explainability and auditability.