Guidewire DEVTrails Hackathon Devlog
What if insurance didn’t ask you to file a claim…
and just paid you when something went wrong?
That idea became the starting point for KAVACH (कवच) — a parametric, AI-powered income protection system designed for India’s gig workers. This post documents what we built, why we built it, and how the system actually works under the hood.
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The Problem
Gig workers operate in highly volatile conditions. A delivery partner’s income depends on:
- Weather disruptions (rain, storms)
- Air quality spikes (AQI)
- Flooding and road blockages
- Localized disasters
- Platform demand fluctuations
A single bad day can wipe out ₹500–₹2000 of expected income.
Traditional insurance doesn’t work here:
- Claims require manual filing
- Verification takes days or weeks
- Payouts arrive too late
- Small income losses aren’t covered
So the real question became:
Can we detect income disruption automatically and trigger payouts instantly?
That’s the foundation of parametric protection.
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The Idea: Parametric Income Protection
Instead of asking:
«"Did the user file a claim?"»
We ask:
«"Did the worker’s earning environment degrade?"»
If yes → trigger payout automatically.
No claims. No paperwork. No waiting.
This is what KAVACH attempts to solve.
System Overview
KAVACH combines three main components:
- Real-time disruption detection
- ML-based earnings prediction
- Automated payout engine
The flow looks like this:
External Data → Disruption Detection → Income Prediction → Loss Calculation → Auto Payout
Real-Time Disruption Detection
We aggregate multiple data sources:
- Weather APIs (rainfall, alerts)
- AQI feeds
- Disaster indicators
- Geo-based environmental signals
These inputs are normalized and converted into disruption scores per location.
Example:
Heavy rain + AQI spike + low visibility
→ High disruption score
→ Trigger evaluation
The system runs continuously and flags abnormal conditions.
Earnings Prediction Model
This is where ML comes in.
We estimate expected earnings for a worker using:
- Historical earnings
- Time of day
- Day of week
- Location demand patterns
- Weather sensitivity
- Behavioral trends
We used:
- XGBoost regression model
- Feature engineering for time series signals
- Rolling baseline computation
- Dynamic adjustment windows
Output:
Expected earnings today: ₹1200
Actual earnings detected: ₹540
Loss detected: ₹660
This becomes the payout base.
Automated Payout Engine
Once disruption + income drop is confirmed:
- Risk score is computed
- Fraud checks are applied
- Loss amount validated
- Payout triggered
Payout happens via:
- UPI transfer
- Instant disbursement logic
- Rule-based caps
The goal is:
Minutes, not days.
Fraud & Abuse Prevention
Parametric systems are vulnerable without safeguards.
We added:
- Anomaly detection on earnings
- Location spoofing checks
- Sudden behavioral deviation filters
- Multi-factor validation scoring
Only validated losses trigger payouts.
Why This Matters
This isn’t just about insurance.
It’s about:
- Financial resilience
- Predictable income
- Gig worker stability
- Instant safety nets
For millions of workers, a single disrupted day matters.
KAVACH tries to make that risk invisible.
What We Built During the Hackathon
In the hackathon timeframe, we implemented:
- Disruption detection pipeline
- ML income baseline model
- Loss calculation engine
- Decision trigger logic
- Simulated payout flow
- Dashboard for monitoring
Still rough. Still evolving. But functional.
Tech Stack
Backend
- Python
- FastAPI
- Real-time ingestion
ML
- XGBoost
- Pandas
- Feature engineering pipeline
Data
- Weather APIs
- AQI feeds
- Synthetic earnings dataset
System
- Decision engine
- Rule evaluator
- Risk scoring module
Challenges We Faced
Data scarcity
Real gig-worker earnings data is not easily available.
Dynamic environments
Weather impact varies across cities.
False positives
Not every income drop is disruption-based.
Payout fairness
Avoiding over-compensation.
Each of these required iterative tuning.
What’s Next
- Better earnings prediction models
- Real gig platform integration
- Geo-level risk calibration
- Live payout simulation
- Mobile interface
- Policy engine tuning
This is still early — but the direction is clear.
Team
Built during Guidewire DEVTrails by:
- Anisha
- Compilation
- Flux
- Fresh
- Priyanshu
Different roles, shared goal — build something meaningful.
Final Thoughts
Hackathons usually produce demos.
We tried building something closer to a product.
KAVACH explores a simple idea:
If risk can be detected automatically, protection should be automatic too.
Still early. Still imperfect.
But worth building.
Seed Phase 2 is coming. Let’s go.
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