Real-time decision infrastructure for insurance claims.
by Bader Alabddan
Claims Decision Control Room · Monitor · Command Center
We built a real-time decision system for insurance claims.
It doesn't just show data.
It tells you:
- What's happening — fraud signals, anomaly detection, risk scoring
- Why it's happening — entity network analysis, decision drivers, evidence trail
- What to do — AI-driven recommendations with confidence scores
- What the financial impact is — approval cost, rejection saving, litigation risk
All decisions are auditable and explainable.
This reduces fraud loss, speeds up claims, and improves compliance.
This is not a dashboard. This is a Decision Operating System for insurance.
The flagship interface. Route: /claims
- Detects fraud signals in real-time
- Provides AI-driven claim decisions
- Shows explainable reasoning
- Tracks full audit trail
- Enables human override
| Feature | Description |
|---|---|
| Decision Engine | Approve / Reject / Escalate / Request Docs |
| Risk Scoring | 8-factor weighted assessment with category breakdown |
| Anomaly Detection | Statistical deviation alerts with severity classification |
| Entity Relationship Graph | Network visualization — flagged entities, suspicious links |
| Decision Drivers | Signal-to-decision bridge showing weighted impact factors |
| Financial Impact | Approval cost, rejection saving, litigation risk, reinsurance recovery |
| Forensic Audit Timeline | SHA-256 hashed, actor-typed event log (AI / SYSTEM / ANALYST) |
| Evidence Tracking | Document verification status with completeness scoring |
TOP: Claim ID · Claimant · Policy · Amount · Risk Score · Status · SLA
LEFT: Entity Graph + Claim Metadata + Evidence
CENTER: Risk Summary + Decision Drivers + Time Series + Anomalies
RIGHT: AI Recommendation + Confidence + Reasoning + Impact + Actions
BOTTOM: Forensic Audit Timeline
The macro intelligence layer. Route: /monitor
Five visualization engines for GCC-wide market intelligence:
| Engine | Function |
|---|---|
| GCC Map | Interactive country risk map with trade corridors |
| Wave Simulation | Causal wave propagation with animated particles |
| Cognitive Engine | Multi-path reasoning — competing futures per scenario |
| Pre-Causal Engine | Pressure fields detecting risk before signals form |
| Video Renderer | Cinematic scenario video generation |
Signals → Graph → Rules → Simulation → Decision → Action → Audit
┌─────────────────────────────────────────────────────┐
│ 7. Governance Audit trail, SHA-256, PDPL │
├─────────────────────────────────────────────────────┤
│ 6. UI & Actions Control Room, Decision Panels │
├─────────────────────────────────────────────────────┤
│ 5. API REST endpoints, WebSocket │
├─────────────────────────────────────────────────────┤
│ 4. Agent Layer AI recommendations, reasoning │
├─────────────────────────────────────────────────────┤
│ 3. Decision Logic Risk rules, anomaly detection │
├─────────────────────────────────────────────────────┤
│ 2. Feature Engine Signals, entity graphs, scoring │
├─────────────────────────────────────────────────────┤
│ 1. Data Ingestion Claims, policies, fraud feeds │
└─────────────────────────────────────────────────────┘
| Layer | Technology |
|---|---|
| Frontend | React 18 + Vite + TypeScript + Tailwind CSS |
| Design System | Graphite-gray 4-layer surface system (d-* tokens) |
| Visualization | SVG entity graphs, sparkline charts, time series |
| Backend | FastAPI + PostgreSQL + LangGraph |
| AI | Ollama (local GPU), GPT-4 (cloud) |
| Deployment | Vercel (frontend), Docker Compose (backend) |
cd frontend
npm install
npm run devOpens at http://localhost:5173 — claims demo works without backend.
For full stack:
pip install -r requirements.txt
python run.py # Backend on :8000
cd frontend && npm run dev # Frontend on :5173Set VITE_API_BASE_URL to connect a backend. Leave empty for demo mode.
MIT — see LICENSE
Built by Bader Alabddan
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