VIDEO_SCRIPT.md

InsureClaim AI - 1 Minute Demo Video Script

OpenEnv Hackathon | Statement 3.1 + Scaler AI Labs

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VIDEO SCRIPT (60 seconds)

[0:00-0:10] HOOK

SHOW: Terminal with training running SAY:

"Insurance claims processing costs $40 billion annually. Today's LLMs rush to approve or deny without investigating. We built an RL environment that teaches them to think like expert adjusters."

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[0:10-0:25] THE ENVIRONMENT

SHOW: HuggingFace Space health check + architecture diagram SAY:

"InsureClaim AI is a 10-action RL environment with partial observability. The agent must query policy databases, run fraud detection, and verify transactions through real Plaid APIs before making decisions."

SHOW: Quick scroll of valid actions:

• query_policy, check_fraud, verify_purchase, approve, deny, escalate

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[0:25-0:45] LIVE DEMO - FRAUD DETECTION

SHOW: Terminal running demo_training.py or WebSocket test SAY:

"Watch the agent catch fraud in real-time."

SHOW:

Claim: CLM-2024-006 (Auto Theft) - $35,000

Step 1: query_policy    → Coverage active ✓
Step 2: check_fraud     → Risk: 0.80 HIGH ⚠️
Step 3: verify_purchase → DISCREPANCY! Paid $22K, claimed $35K
Step 4: deny            → Reward: +17.4 🎯

Agent caught $13,000 inflated claim!

SAY:

"The agent detected a $13,000 inflated claim that a naive LLM would have approved. That's +17 reward for catching fraud."

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[0:45-0:55] TRAINING RESULTS

SHOW: reward_curves.png SAY:

"After 50 episodes, our agent improved from -5 to +12 average reward. It learned to investigate efficiently - just 3 steps instead of 12 - while catching fraud cases."

SHOW: Key metrics:

• Start: -5.5 reward
• End: +11.75 reward
• Improvement: +17.25
• Fraud detection: +17.4 max reward

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[0:55-1:00] CLOSE

SHOW: Links on screen SAY:

"InsureClaim AI - teaching LLMs to investigate before they decide. Links in description."

SHOW:

• Live: https://pramodmisra-claims-env.hf.space
• GitHub: https://github.com/pramodmisra/claims-env-hackathon

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RECORDING TIPS

1. Screen recording: Use QuickTime or OBS
2. Resolution: 1920x1080
3. Terminal font: Large (18-20pt) for readability
4. Pace: Speak clearly, not rushed
5. Background: Clean desktop, dark terminal theme

WHAT TO RECORD

1. Terminal 1: Run python training/demo_training.py
2. Terminal 2: Show WebSocket test catching fraud
3. Browser: HuggingFace Space health check
4. Image: reward_curves.png full screen

BACKUP COMMANDS

# Test HF Space
curl https://pramodmisra-claims-env.hf.space/health

# Run training demo
python training/demo_training.py

# Quick fraud detection demo
python demo_claims.py

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KEY TALKING POINTS FOR JUDGES

1. Real APIs: Plaid transaction verification (not mocks in production vision)
2. Enterprise complexity: 8 scenarios, coverage limits, exclusions, escalation
3. Meaningful rewards: +10 correct, +5 fraud caught, -10 fraud missed
4. Efficiency learning: Agent optimizes for fewer steps
5. Partial observability: Agent must query to reveal information