Agri-Fintech Data Playbook

Executive Summary

Agricultural lending and insurance in India suffer from basis risk — the gap between insured/policy areas and actual loss occurrences. State-level pricing ignores micro-climatic variations that dramatically affect yields and prices.

Our platform combines 15 years of IMD weather station data with live eNAM mandi price feeds to create district-level risk profiles for:

• Crop Yield Prediction: Weather-based yield estimates by district
• Price Volatility Modeling: Understanding how weather shocks propagate to prices
• Insurance Product Design: Parametric triggers tied to actual weather events
• Lending Risk Scoring: Weather-adjusted borrower risk assessment

Methodology

Data Sources

Data Integration Pipeline

┌──────────────────────────────────────────────────────────────────┐
│                    DATA INTEGRATION PIPELINE                      │
├──────────────────────────────────────────────────────────────────┤
│                                                                  │
│  Weather Data (IMD)           Price Data (eNAM)                  │
│  ├── 700 stations             ├── 1,000+ mandis                  │
│  ├── Daily T/P/R/H            ├── 50+ commodities                │
│  └── 2010-2025                └── Daily updates                  │
│           ↓                           ↓                          │
│  ┌───────┴───────┐           ┌───────┴───────┐                  │
│  │Spatial Join   │           │Zone Aggregation│                  │
│  │District-Level │           │450 Trade Zones │                  │
│  └───────┬───────┘           └───────┬───────┘                  │
│          │                           │                          │
│          └───────────┬───────────────┘                          │
│                      ↓                                          │
│          ┌─────────────────────────┐                            │
│          │  WEATHER-PRICE MODEL    │                            │
│          │  Correlation Analysis   │                            │
│          │  Event Detection        │                            │
│          │  Yield Estimation       │                            │
│          └─────────────────────────┘                            │
│                      ↓                                          │
│          ┌─────────────────────────┐                            │
│          │  RISK INTELLIGENCE      │                            │
│          │  • Yield Risk Score     │                            │
│          │  • Price Volatility     │                            │
│          │  • Basis Risk Index     │                            │
│          │  • Claims Prediction    │                            │
│          └─────────────────────────┘                            │
└──────────────────────────────────────────────────────────────────┘

Key Findings

1. Weather-Price Correlation Strength

We identified which weather variables most strongly predict price movements:

2. District Risk Profiles

3. Basis Risk Comparison

Traditional state-level pricing vs. our district-level approach:

4. Use Case: Parametric Insurance Design

We designed a sample parametric product for Marathwada Cotton:

Modeled Loss Ratio: 78% (vs. 95% for traditional products)

Use Cases

For Insurance Companies

• Micro-Level Pricing: District-specific premiums reflecting actual risk
• Parametric Products: Design triggers based on verified weather data
• Claims Automation: Auto-validate claims against weather events
• Reinsurance Modeling: Granular catastrophe modeling

For Lenders (Banks/NBFCs)

• Risk-Based Lending: Adjust interest rates by district risk profile
• Collateral Valuation: Weather-adjusted crop value estimation
• Portfolio Monitoring: Early warning for climate-stressed regions
• Loan Default Prediction: Incorporate weather shock history

For Agritech Companies

• Input Pricing: Dynamic pricing based on weather outlook
• Market Intelligence: Predict price movements for procurement planning
• Farmer Scoring: Creditworthiness assessment using farm-level data

Dataset Schema

{
  "district": "Marathwada",
  "state": "Maharashtra",
  "commodity": "Cotton",
  "weather_zone": "Vidarbha",
  "price_zone": "Central India",
  "risk_metrics": {
    "yield_volatility": 0.42,
    "price_volatility": 0.38,
    "basis_risk_index": 0.72,
    "weather_risk_score": 68,
    "price_risk_score": 62
  },
  "triggers": {
    "rainfall_deficit_threshold": 450,
    "excess_rainfall_threshold": 1200,
    "heat_wave_threshold": 5
  },
  "price_indices": {
    "current": 5820,
    "3yr_average": 5450,
    "yoy_change": 0.068,
    "volatility_index": 0.38
  },
  "last_updated": "2025-12-30"
}

Platform Capabilities

Weather Monitoring

• Real-time station data feeds
• 7-day forecast integration
• Extreme event alerts (heat waves, floods, droughts)

Price Intelligence

• Daily mandi price updates
• Inter-commodity correlation analysis
• Seasonal price pattern identification

Risk Analytics

• District risk scorecards
• Trend analysis (5-year window)
• Scenario modeling (normal/drought/excess rain)

API Access

• REST API for integration
• Bulk data export
• Custom dashboard creation

Limitations & Caveats

1. Station Coverage: 15% of districts have limited weather station coverage; interpolation used
2. Mandi Coverage: eNAM covers 66% of mandis; prices for others modeled
3. Policy Lag: Government price support schemes can distort market prices
4. Climate Change: Historical data may understate future risk due to climate shifts

Next Steps

Our Agri-Risk Intelligence Platform provides:

• Daily weather and price data feeds
• API for integration with insurance/lending systems
• Custom district and commodity analysis
• Model development assistance

Contact us for platform demo or custom analysis.