Bridging General Intelligence to Actionability: Making AI Actually Useful for Healthcare Fraud Investigation
by Yubin Park, Co-Founder / CTO
At Falcon, we're excited by the remarkable progress in AI. Recent breakthroughs show models achieving PhD-level performance across domains, but a critical question remains: How do we apply this intelligence to specialized fields like healthcare program integrity?
Will GPT-5 automatically catch fraud patterns? Can Sonnet 4 find sufficient evidence to stop improper payments? What policies would Gemini rely on to determine payment violations?
Despite the impressive general intelligence of these models, we discovered a significant gap between raw capability and actionability. True actionability requires domain-specific context, business logic, and specialized rules that general models lack.
Our Approach: Giving AI the Right Tools
We've been developing specialized tools that AI can use naturally and effectively. Our AI-friendly Medicare Coverage Database (MCD) search tool is just one example in our growing toolkit.
Think of it like a master chef—even with exceptional culinary skills, they need the right knives, pans, and ingredients to create an amazing dish. Similarly, AI needs domain-specific tools to excel in specialized applications.
Top tip
General AI models know medicine but don't understand Medicare. They can explain diabetes but can't tell you which diabetic supplies are covered under DME vs. pharmacy benefits.
How It Works in Practice
Consider Q4205, a commonly abused skin graft code. Here's what happens when you ask Falcon AI about it:
The system demonstrates a multi-step process where the AI:
- Looks up the HCPCS code to understand its meaning
- Searches Medicare Coverage Determination databases for relevant policies
- Examines detailed Local Coverage Determinations (LCDs) for specific rules
- Investigates billing patterns to identify suspicious providers
This showcases the power of context-aware AI. While the underlying model is intelligent, it needs specialized tools to work effectively in healthcare fraud detection.
The Impact of Tools: A Comparative Analysis
To demonstrate this difference, we tested 20 real-world research questions from a healthcare insurance Special Investigative Unit. We compared three systems:
- Claude Sonnet 4 (baseline)
- OpenAI GPT-5 (baseline)
- Falcon AI (Sonnet 4 + specialized tools)
Top tip
When we asked baseline AI models "Is this billing pattern suspicious?", they gave general answers. When we asked Falcon AI the same question, it pulled specific Medicare policies and calculated exact risk scores.
Results:
- Falcon AI: 8.3/10 ⭐
- Sonnet 4: 7.2/10
- GPT-5: 6.5/10
See the full results and analysis
Key Findings:
Falcon's Advantages:
- Research-driven approach with Medicare database integration
- Quantified risk assessments with specific metrics
- Investigation-focused responses with actionable next steps
- Deep understanding of healthcare billing complexities
- Pattern recognition for systematic fraud indicators
Sonnet 4's Strengths:
- Balanced analysis considering multiple scenarios
- Strong medical accuracy and clinical understanding
- Clear guidance and practical next steps
GPT-5's Strengths:
- Concise, direct responses
- Generally accurate assessments
- Clear and implementable advice
Falcon AI emerged as the clear winner for healthcare fraud investigation, demonstrating superior actionability through its research capabilities, specific methodologies, and comprehensive follow-up questions that help investigators build stronger cases.
The Bottom Line
Tools make the difference between impressive demos and practical utility. Falcon AI's specialized toolkit transforms general intelligence into immediate actionability for healthcare fraud detection.
We're uncovering fascinating patterns, developing new research angles, and preventing wasteful billing practices with this approach. The future of AI in specialized domains isn't just about smarter models—it's about giving them the right tools to succeed.
Top tip
The future of AI in healthcare isn't about replacing human investigators—it's about giving them superpowers to catch fraud that would otherwise slip through the cracks.
The gap between general intelligence and domain expertise is bridgeable, but it requires intentional tool development. At Falcon, we're proving that the combination of advanced AI and specialized tools creates unprecedented actionability in healthcare program integrity.