Guidewire DEVTrails 2026 | University Hackathon Submission Protecting the livelihoods of Zomato & Swiggy delivery partners from uncontrollable external disruptions.
India's food delivery partners (Zomato, Swiggy) earn ₹15,000–₹25,000/month working outdoors on two-wheelers. When external disruptions like heavy rain, dense fog, or civil unrest hit, platforms reduce order availability or workers are forced to stop — causing 20–30% income loss in a single week with zero financial safety net.
GigShield solves this with a parametric insurance model: no claim forms, no waiting — just automatic payouts when verified disruptions cross defined thresholds.
- Name: Raju, 26 | Operates in Chennai (Anna Nagar + Velachery Zone)
- Avg weekly earnings: ₹4,000–₹6,000
- Working hours: 10 AM – 10 PM, ~6 days/week
- Tech comfort: Moderate — uses smartphone daily for delivery app
- Pain point: No savings buffer; one bad week = skipped EMI or missed rent
| Scenario | Disruption | Impact | GigShield Response |
|---|---|---|---|
| Mumbai monsoon week | Rainfall > 50mm/day for 3+ hours | Orders drop 70%, can't ride safely | Auto-trigger: ₹500–₹1,200 payout |
| Delhi winter fog | Visibility < 50m for 4+ hours | Night deliveries impossible | Auto-trigger: ₹300–₹800 payout |
| City bandh / protest | Verified civil disruption in worker's zone | Pickup/drop zones blocked | Auto-trigger: ₹400–₹1,000 payout |
[Worker Onboarding]
│
▼
[Risk Profile Created] ← City, Zone, Avg. Weekly Earnings, Work Hours
│
▼
[Weekly Policy Purchased] ← Dynamic premium shown, UPI payment
│
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[Real-Time Monitoring] ← Weather API + News/Alert API polling every 30 min
│
▼
[Disruption Detected] ← Threshold crossed in worker's registered zone
│
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[Fraud Check] ← Location validation + anomaly scoring
│
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[Claim Auto-Approved] ← Zero manual steps for worker
│
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[Instant Payout] ← UPI / wallet transfer within minutes
│
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[Worker Dashboard Updated] ← Earnings protected, claim history shown
GigShield covers income loss only — no health, vehicle, or accident coverage.
- Data Source: OpenWeatherMap API (free tier)
- Threshold: Rainfall ≥ 40mm in a 3-hour window OR IMD red/orange alert in worker's city
- Payout Logic: ₹500 base + ₹100 per additional disrupted hour (capped at ₹1,200/day)
- Data Source: OpenWeatherMap temperature field
- Threshold: Temperature > 42°C sustained for ≥ 3 hours between 10 AM–4 PM
- Payout Logic: ₹300 base + ₹75 per disrupted hour (capped at ₹800/day)
- Data Source: GDELT Project API + NewsAPI + Twitter/X disaster monitoring feeds
- Threshold: Verified bandh/curfew notice OR 3+ credible news sources reporting civil disruption in worker's registered zone within 1 hour
- Payout Logic: ₹400 flat per disrupted half-day (max ₹1,000/day)
⚠️ Note: All triggers are verified against the worker's registered GPS zone at policy activation time. Claims filed outside the zone are flagged for review.
Gig workers operate and earn on a week-to-week cycle, so GigShield is priced weekly — not monthly or annually.
Pricing Rationale: An average Chennai food delivery partner earns ₹4,000–₹6,000/week. Industry best practice keeps insurance premium under 1–1.5% of insured income — making ₹29–₹79/week both affordable and actuarially viable.
| Plan | Weekly Premium | Max Weekly Payout | Best For |
|---|---|---|---|
| Basic Shield | ₹29/week | ₹1,500 | Part-time workers (<30 hrs/week) |
| Standard Shield | ₹49/week | ₹3,000 | Full-time workers |
| Pro Shield | ₹79/week | ₹5,000 | High-earning / peak-season workers |
The base premium is adjusted weekly using ML risk factors:
| Risk Factor | Adjustment |
|---|---|
| Zone historically flood-prone (e.g., low-lying areas) | +₹5–₹15/week |
| Worker's city has IMD pre-season warning | +₹10/week |
| Worker's zone historically low disruption | −₹5/week |
| Worker has 0 claims in last 4 weeks | −₹3/week (loyalty discount) |
| Predicted rain probability next 7 days > 70% | +₹8/week |
Premium is recalculated and shown to the worker every Sunday before the new week begins. Worker must actively renew — no auto-debit surprise charges.
- Model: Gradient Boosted Trees (XGBoost / LightGBM)
- Inputs: Zone flood history, AQI trends, seasonal weather patterns, worker's claim history, city-level disruption frequency
- Output: Risk score (0–100) → maps to weekly premium adjustment
- Phase 1: Rule-based mock; Phase 2: trained on synthetic + public weather data
- Anomaly Detection: Isolation Forest model
- Signals monitored:
- GPS location mismatch (worker not in registered zone during claimed disruption)
- Claim filed despite active delivery records on platform (simulated Swiggy/Zomato activity feed — if worker is actively completing orders, disruption claim is flagged)
- Multiple claims in rapid succession
- Device fingerprint inconsistency
- Output: Fraud risk score → Auto-approve (low), Flag for review (medium), Reject (high)
- Model: Time-series forecasting (Prophet / LSTM) on weather data
- Use: Pre-warn workers of likely disruption next day; allow insurers to provision payout reserves
- Phase 3 feature
- What it shows: Chennai zone-level risk heatmap
- 🔴 High flood/heat risk zones (e.g., Velachery, Tambaram)
- 🟡 Moderate risk zones
- 🟢 Low risk / safe zones
- Benefits: Drives hyper-local premium pricing; gives insurers real-time portfolio risk view
- Tech: Google Maps SDK + historical weather + claim data overlay
- Framework: React Native (Expo) — cross-platform iOS + Android
- UI Library: React Native Paper / NativeWind (Tailwind for RN)
- State Management: Zustand
- Maps: React Native Maps (Google Maps SDK)
- Runtime: Node.js + Express.js
- Database: PostgreSQL (user profiles, policies, claims) + Redis (real-time trigger cache)
- Auth: Firebase Auth (phone number OTP — familiar to gig workers)
- Job Scheduler: Bull Queue (for periodic weather API polling)
- Language: Python (FastAPI microservice)
- Libraries: Scikit-learn, XGBoost, Prophet
- Serving: REST API called by Node backend
| Integration | Provider | Mode |
|---|---|---|
| Weather data | OpenWeatherMap API | Real (free tier) |
| Civil disruption alerts | Google Alerts RSS + admin panel | Mock (Phase 1-2) |
| Payment gateway | Razorpay Test Mode | Sandbox |
| Platform activity (Zomato/Swiggy) | Simulated delivery data | Mock |
- Hosting: Railway.app / Render (backend) + Expo EAS (mobile builds)
- CI/CD: GitHub Actions
- Version Control: GitHub (this repo)
- Finalize persona and disruption triggers
- Define weekly pricing model
- Design application workflow
- Set up GitHub repo and project structure
- Create wireframes (Figma) for core screens
- Build minimal prototype (onboarding + policy purchase screen)
- Record 2-minute strategy video
- Worker registration + OTP auth
- Risk profiling flow (zone selection, earnings input)
- Weekly policy purchase with Razorpay sandbox
- Weather API integration + trigger monitoring engine
- Claims dashboard (auto-triggered claims visible to worker)
- Basic fraud scoring (GPS zone validation)
- Full ML-based fraud detection
- Instant payout simulation (Razorpay test mode)
- Worker dashboard (earnings protected, weekly coverage status)
- Admin/insurer dashboard (loss ratios, disruption predictions)
- Final demo video + pitch deck
gigshield/
├── mobile/ # React Native app
│ ├── screens/
│ ├── components/
│ └── navigation/
├── backend/ # Node.js + Express API
│ ├── routes/
│ ├── services/
│ │ ├── weatherService.js
│ │ ├── triggerEngine.js
│ │ └── fraudService.js
│ └── models/
├── ml/ # Python FastAPI ML service
│ ├── premium_model/
│ └── fraud_model/
├── docs/ # Architecture diagrams, wireframes
└── README.md
| Member | Role |
|---|---|
| Mridula | Team Leader — Strategy, coordination, business model & pitch |
| KarthiKeyan | Backend Lead — APIs, trigger engine, database |
| Salini | Frontend Developer — React Native app, screens & navigation |
| Karthigeiyan | UI/UX Designer — Wireframes, design system, user experience |
- 🎥 2-Minute Strategy Video: [Link to be added]
- 🖼️ Wireframes (Figma): [Link to be added]
- 📋 Project Board: [GitHub Projects link]
GigShield is built for the Guidewire DEVTrails 2026 University Hackathon. Coverage is strictly limited to income loss from external disruptions. No health, life, accident, or vehicle repair coverage is included.