Day 1 of DEVTrails 2026 by Guidewire Software.
Four students from KL University. One problem statement.
Parametric insurance for gig workers.
Most teams open a problem statement and immediately think
about the tech stack.
We did that too — for about 10 minutes.
Then we stopped.
The Question That Changed Everything
We spent the next 15 days asking one question on repeat:
Who actually gets hurt when the storm hits?
The answer was always the same.
The Swiggy rider outside your window.
The Zomato partner sitting at home watching it rain,
knowing the day's earnings just disappeared.
The Zepto delivery partner who can't ride in a heatwave.
2.8 crore gig workers in India.
₹0 income protection built specifically for them.
Not health insurance. Not accident insurance.
Income insurance — the kind that triggers when it rains
and you simply cannot work.
That gap is what we built GigWeatherWage to close.
What is Parametric Insurance?
Traditional insurance works like this:
Something bad happens → you file a claim → a human reviews it
→ weeks later you maybe get paid.
Parametric insurance works differently.
You define the trigger condition upfront. The moment that
condition is met — rainfall above 50mm, AQI above 300,
a declared city curfew — the payout fires automatically.
No claim. No wait. No rejection.
For a gig worker who loses ₹800 on a rainy day, waiting
weeks for a manual claim review is not a solution.
Automatic payout in seconds is.
What GigWeatherWage Does
Workers register and choose a weekly plan:
- Basic Shield — ₹20/week — ₹2,000 coverage
- Storm Guard — ₹45/week — ₹3,500 coverage
- Full Shield — ₹80/week — ₹5,000 coverage
The system monitors real-time weather APIs, AQI feeds,
and city alert systems for their registered zone.
When a threshold is crossed — payout goes directly
to their UPI account. Instantly.
Five disruption triggers:
| Trigger | Threshold | Payout |
|---|---|---|
| Heavy Rainfall | > 50mm | ₹300 |
| Heatwave | > 42°C | ₹250 |
| Extreme AQI | > 300 AQI | ₹200 |
| City Curfew | Active alert | ₹300 |
| Platform Outage | > 2 hours | ₹150 |
Each tied to a verifiable real-world data source.
No human decision required.
The Fraud Problem Nobody Talks About
On Day 12, Guidewire dropped a 24-hour challenge into
the hackathon.
A syndicate of 500 delivery workers had organized via
Telegram to use GPS spoofing apps — faking their locations
in rain zones while sitting safely at home — draining a
competitor's insurance pool.
Simple GPS verification was declared obsolete.
We had to redesign our fraud architecture under
extreme time pressure.
Our response — 5-signal AI fraud detection:
Signal 1 — Weather API Match (+40 pts)
Does it actually rain at the claimed location?
A spoofer can fake GPS. They cannot fake the weather
at that fake location.
Signal 2 — Movement Pattern (+30 pts)
A genuine stranded rider still moves — to shelter,
to a chai shop.
A spoofer sitting at home shows completely static
coordinates for hours.
Signal 3 — Device Integrity (+20 pts)
GPS spoofing apps run on emulators.
Emulators leave detectable signatures.
Signal 4 — Historical Behaviour (+15 pts)
Fraud rings create accounts fast.
A 3-day-old account claiming on the first major
weather event is suspicious.
Signal 5 — Geo-Cluster Detection (+30 pts)
47 workers all claiming from the exact same GPS
coordinates in the same 10-minute window is not
a coincidence.
That is the Telegram syndicate pattern.
Risk score decision:
| Score | Decision |
|---|---|
| 0 – 40 | ✅ Safe — instant payout |
| 40 – 70 | ⚠️ Medium — 2hr verification window |
| 70+ | ❌ Blocked — escalated for review |
Genuine workers are never hard-blocked on a single signal.
That fairness layer was as important as the fraud
detection itself.
The 4 Personas — Live in the App
We built four interactive user personas that judges
and users can log into right now:
| Persona | Type | Score | Outcome |
|---|---|---|---|
| 👨 Raju Kumar | Genuine worker, 3 years | 12 | ✅ Paid instantly |
| 👩 Meena Devi | New account, genuine | 55 | ⚠️ Delayed, then paid |
| 👩 Priya Sharma | Real worker, insider fraud | 85 | ❌ Blocked |
| 🕵️ Vikram #7749 | GPS spoof ring | 135 | ❌ Blocked + ring flagged |
Priya is the most important scenario.
She is a real registered Zomato worker — 8 months of
history, trusted device, looks completely legitimate.
But the weather API confirms no rain at her actual
device location.
The system catches her anyway.
Log in as any of them. Watch the AI decide. Live.
The Feature Nobody Else Built
On Day 13 we had one realization:
A parametric insurance app that protects workers
during storms must work during storms — even when
the network is unreliable.
So we built offline-first claim filing.
Claims queue on-device when there's no network.
A fraud pre-check runs on cached data.
The moment connectivity returns — claim auto-syncs,
verification runs, payout processes.
The worker never loses their claim because of
bad signal in a storm.
No other solution addressed this.
For a product literally designed for storm conditions,
it felt like the most obvious thing in the world
once we saw it.
What We Actually Built
A fully deployed React prototype on Vercel with:
- ✅ Complete 5-step registration with UPI payment simulation
- ✅ 4 interactive fraud personas with live AI analysis
- ✅ 5-tab dashboard — Home, Claims, Payments, Alerts, Profile
- ✅ Offline detection with claim queuing and auto-sync
- ✅ Full claims history with fraud scores per claim
- ✅ UPI deep links that open PhonePe or GPay on real device
Tech stack:
React · Node.js · Python · MongoDB ·
OpenWeatherMap API · AQI API · Razorpay · Vercel
What We Learned
The hardest part of building any system is not
writing the code.
It is understanding the problem deeply enough that
the right solution becomes obvious.
We also learned:
- Fraud detection is fundamentally a fairness problem, not a security problem
- The GPS spoofing scenario taught us to design for adversarial users from Day 1
- Offline-first is not optional for infrastructure that workers depend on during emergencies
- Spending 15 days on the problem before touching a keyboard was the best decision we made
What's Next
Phase 2:
Python AI risk model · Live API integrations ·
Razorpay actual payouts · Flutter mobile app ·
Pilot with 10 real workers in Hyderabad
Phase 3:
City-wide disruption dashboards · Dynamic premium pricing ·
Platform API integrations · IRDAI parametric license ·
Expand to 5 Indian metros
We are not building for a hackathon.
We are building for the 2.8 crore workers who deserve
better than losing a day's earnings to the rain.
Try It Yourself
🔗 Live Prototype → deployed link
📺 Pitch Walkthrough → pitchdeck demo
💻 GitHub → Repository
Log in as Priya. Try to claim. Watch the system catch her.
Then log in as Raju. Watch him get paid in seconds.
The difference between those two experiences
is the entire product.
— Kotapothula Siva Raga Adithi
Team Code Alchemists · KL University · DEVTrails 2026
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