Wrl crop insurance2.0

C R O PI N S U R A N C E S O L U T I O N S -O V E R V I E W

S O N U A G R A W A L

M A N A G I N G D I R E C T O R

sonu.agrawal@weather- r isk .com N O V E M B E R 2 0 1 4

To provide all in the world security against climate change as the world’s No. 1 climate risk management company innovatively combining Big Data, Analytics, Smart Devices & Financial Services

Founded2004

Team101 People

AsiaPhilippines

Cambodia

InvestorsSIDBI / IIT / ILO

Ford Foundation

Global FootprintIN / Africa / EU / US / LatAM

V I S I O N W H AT W E D O C L I E N T S T E AM I M PAC T O P P O R T U N I TY

Cambodia

Bangladesh

Sri Lanka

AfricaTanzania

Rwanda

Zambia

Mozambique

Existing Presence

Building Presence

We are a global Risk Management company delivering

innovative products and services to large enterprises as well

as farmers in the remotest of villages worldwide.

Big Data Services & Analytics

Smart Devices & Technology

Weather Insurance1 2 3� India

� Global

� Agri Information Systems

� Weather Forecasting

� Remote Sensing

� Automated Weather Stations

� Vehicle Tracking Systems

� Energy Efficiency


as farmers in the remotest of villages worldwide.

2004 2006 2008 2010 2012 2004 2006 2008 2010 2012

Founded with

ICICI Lombard as

the first client

SIDBI Seed Fund

Incubation at IIT

Kanpur

Ford Foundation

grant

Convinced GoI for regulatory

approval & subsidize weather

insurance with Rs. 1000 Cr.

Stabilizing pan-

India business

AWS Grid

~ 100 Stations

SIDBI invests for

working capital

First International

projects in Tanzania

and Bangladesh

Expansion of Products

& Services to Africa &

ASEAN

2005 2007 2009 2011 20142005 2007 2009 2011 2014

Vijay MahajanMentor

Founder & Chief Executive Officer

Basix Social Enterprise Group

Dr. AK BohraDirector

Weather Scientist and Former Director

National Centre for Medium Range Weather Forecasting

Pankaj KhandelwalDirector

Chairman & Managing Director

A D V I S O R Y B O A R D

Sonu Agrawal, 39Founder & Managing Director

Weather & Climate Risk Pioneer

IIT Kanpur and IIM Calcutta Alumnus

Anuj Khumbat, 38Chief Executive Officer

Finance and Management Specialist

Chartered Accountant

E X E C U T I V E T E A M

Ashish Agarwal, 31Chief Technology Officer


Chairman & Managing Director

INI Farms

Dr. Kanti PrasadDirector

Weather Scientist and Former Dy. Director General

Indian Meteorological Department

Dr. BV PhaniDirector

Professor / Finance

Indian Institute of Technology - Kanpur

Dr. Jayant K. SinghTechnology Advisor

Professor / Chemical Engineering

Indian Institute of Technology - Kanpur

Passionate and Diverse Team101 people with expertise in:

T E A M W R L

FINANCIAL RISK MANAGEMENT

COMPUTER SCIENCE

METEOROLOGY

ATMOSPHERIC PHYSICS

CLIMATOLOGY

ELECTRONICS & INDUSTRIAL DESIGN

Electronics and Technology Specialist

IIT Kanpur Alumnus

Industry Leader with robust,

comprehensive and integrated services

Weather Insurance1

Underwriting Services for Insurance Companies ��

�

Risk Management Application

� Fraud Analytics

• Claims Control


• Claims Control

• Remote Sensing & UAV

� Portfolio Risk Management

Reinsurance Services ��

Market Making ��

As market makers we help financial institutions & companies to design / execute

innovative risk offerings in Agriculture & Power

�� We work with several farmer associations

W E AT H E R S E RV I C E S

Automatic Weather

Station

Temperature

Sensor

Seasonal Forecast

and Anomalies


Solar Radiation


S AM P L E F O R E C AS T

7 Days Forward Forecast for every 15 minutes interval / updated every 6 hours


W E AT H E R I N S U R AN C E AN ALY T I C S P L AT F O R M

9

Arial SurveysRemote Sensing


C R O P AS S E S S M E N T & E VAL U AT I O N

10

Cloud Sourcing

Get Market Prices

Get Temperature Data

Get Insurance

Monsoon Outlook

�Location�Commodity�Unit�Price

�Max Temperature�Min Temperature

�Product�Location�Sum Insured� Get Premium

�Monsoon Outlook by Dr. Kanti Prasad


AG R I C U LT U R E I N F O R M AT I O N S Y S T E M S

11

Get Satellite Data

Get NDVI Data

Get Forecast

�Data Date�Rain

�Year�Month

�Max Temp�Min Temp�Rain�Humidity�Wind Speed�Wind Direction

Kanti Prasad

Click to View Video

Business Case What we did Impact

Cholamandalam • WRL helped to structure Weather and • Cholamandalam

Cholamandalam is a leading Indian general insurance company. In 2011 Weather Risk was hired to help structure Weather and Yield Index based Crop Insurance Products.


Representative Case Study

Cholamandalam

wanted to establish

a robust Crop

Insurance Products

portfolio

• WRL helped to structure Weather and

Yield Index based products as well as

advised Cholamandalam to manage its

Crop Insurance portfolio.

• We helped to build necessary banking

channels for sales and distribution.

• WRL also installed Weather Stations and

conducted claim settling audits.

• Cholamandalam

generated a premium

income of US $ 20m in FY

13 - 14

• The portfolio covers

20000 farmers in Bihar

and Rajasthan

• WRL continues to help

Cholamandalam target

US $40 m in 2014 - 15


Large Scale • WRL embarked on an exercise to • The Ministry kindly

Since 2007 we have helped ICICI Lombard implement a joint nationwide weather and agriculture Insurance market making and strategic development exercise.



Large Scale

implementation of

subsidized weather

insurance program

• WRL embarked on an exercise to

remove technological, policy level and

reinsurance capacity bottlenecks.

• WRL developed low cost weather

stations, unmanned aerial vehicles, and

automated yield measurement

instruments to facilitate claim

settlement.

• WRL also pursued the Ministry of

Agriculture for subsidy support.

• The Ministry kindly

consented in 2007 and

granted support to

Weather Insurance

Products.

• In Rabi 2009 - 10 season

WRL covered close to

4000 farmers for 10000

acres in just 2 districts of

West Bengal.


Difficult to extend • WRL worked with the PepsiCo ground • Program successfully

Late Blight is a lethal potato disease. In 2006 PepsiCo contract farmers lost 60 % of their crops due to this bane. We helped PepsiCo provide risk management to over 10000 farmers in Punjab.



Difficult to extend

cover under

existing Crop

Insurance programs

due to moral hazard

• WRL worked with the PepsiCo ground

team in Punjab.

• Our analysis indicated high correlations

with location humidity and

temperature.

• We created an index to cover blight risk

specific to the region.

• Additional weather stations were

installed to minimize basic risk – critical

for extending this kind of cover.

• Program successfully

running in its 9th year.

• Covers 10000 potato

farmers in Punjab.

• Strong demonstration of

how insurance can be

used by contract farming

companies to sustain

their grower base.


Innovation for • Bayer Bio Science was facing a risk of • The cover was

We have helped Bayer face the challenge of insufficient data availability and develop an innovative Cloud Cover product helping to insure seed crop during pollination under cloudy conditions.



Innovation for

Cloud Cover

• Bayer Bio Science was facing a risk of

loss of its seed crop on account of

cloudy conditions during pollination

• Due to lack of historical data for cloud

coverage, insurance companies were

not able to design a suitable product to

cover the possible losses of Bayer

• We designed a cover correlating rainfall

of all weather stations within a 50 km

radius of the cropped location to cover

the risk of cloud cover.

• The cover was

successfully tested in

sample of 200 acres.

• Bayer is now working on

a product launch

� Indian Market:

– Provide tools for risk assessment and evaluation for

Weather and Yield Insurance in India: other than already

serviced clients

– Offer comprehensive reinsurance services to smaller

players in the market who are not offered capacity by


What we can do

with Willis Re

players in the market who are not offered capacity by

major reinsurers

• Risk Assessment and Evaluation to be done by WRL

• Reinsurance placements to be arranged by Willis Re

– Include Agriculture Insurance in the general Cat-XL given to

insurance companies in India

• This well help optimize Cat-XL treaty for insurer

– Risk Evaluation inputs to be given by WRL

– Placements to be arranged by Willis Re

• Project: Launching Index Insurance in Bangladesh

• Objective: Index Insurance through ADB project client Sadharan

Bima Corporation / Donor: Asian Development Bank

• Work Done:

– Have collected all the available weather data available in the country

– Have completed feasibility study for launching Index Insurance (IFC Funded)


Global Market Development:

Weather InsuranceB AN G L AD E S H

– Have completed feasibility study for launching Index Insurance (IFC Funded)

– Have investment plan ready for the launch of the product

• Risks Covered:

– Phase-wise launch starting with simple weather indexes on Deficit and Excess

Rainfall in Phase I

– Phase II launch of complex products such as Flood & Cyclone Index Covers

• Business Size:

– Intend to cover 3000 farmers in the first season but overall 20000 farmers

during project duration

• Project: Launching Index Insurance for Paddy farmers in Cambodia

• Objective: Covering borrower farmers of a Large MFI client of

insurance company / Partner: Forte General Insurance Co.

• Work Done:

– Have rainfall data of the all the major weather observatories in the Country

– Initial product has been designed and proposal submitted by insurance



Weather InsuranceC AM B O D I A


company to client

• Risks Covered:

– Drought resulting in increased cost of operations for Paddy Farmer

– Unusually long break in monsoon captured in the form of Dry Days Index

– Unseasonal severe rainfall resulting in loss of paddy crop

• Business Size:

– Intend to cover 5000 farmers in the first season

• Project: Testing NDVI based Index Insurance for Pasture-land in some

areas of Mexico

• Objective: Covering rangeland owners through NDVI based

insurance / Partner: Proagro Seguros

• Work Done:




Weather InsuranceM E X I C O



company to client

• Risks Covered:

– Drought resulting in increased cost of operations for Paddy Farmer

– Unusually long break in monsoon captured in the form of Dry Days Index

– Unseasonal severe rainfall resulting in loss of paddy crop

• Business Size:

– Overall Index Insurance market is of around USD 400 million in Premium

• Project: Crop & Weather Insurance for Maize, Beans & Horticulture

Crops

• Partner: Leading insurers / Commodity Exchange in South Africa

• Work Done:

– Designed few products for Horticulture Crops

– Have rainfall and temperature data of few stations in South Africa



Weather InsuranceS O U T H AF R I C A

– Have rainfall and temperature data of few stations in South Africa

• Risks Covered:

– Frost Loss to Grapes

– Loss due to unseasonal rainfall

– Drought

• Project: Installation of Automatic Weather Station for insurance

settlement

• Partner: MicroEnsure, Bankable Frontier

• Work Done:

– Have done IFC / SDC / World Bank funded feasibility study & Risk Review

Projects in Zambia, Malawi, Kenya & Mozambique



Weather InsuranceE AS T & C E N T R AL AF R I C A

Projects in Zambia, Malawi, Kenya & Mozambique

– Have installed 60 AWS stations in Tanzania and Rwanda

– Stations measure Rainfall, Temperature and RH

• Further Work

– Review of Insurance program in Malawi

– Launching Index Insurance projects in Mozambique and Malawi

� International Market

– Product Development & Portfolio Risk Assessment

• WeatherSecure Platform can be used for:

– Designing and Pricing weather / yield insurance products for any crop in any country

– Portfolio Risk Evaluation and Assessment

– Weather Station Installation for settlement of Weather

Insurance Contracts


What we can do

with Willis Re

Insurance Contracts

– Yield Assessment / Claims Management

• Using Ground Surveys and Satellite Imagery for estimating crop yields or

evaluating flood losses

22

The sky is not the limit

C R O P I N S U R A N C E –D E S I G N , P R I C I N G , R I S K A S S E S S M E N T


sonu.agrawal@weather- r isk .com

N O V E M B E R 2 0 1 4

SecureWeather Risk Management Application for

Insurance Business


� Key Features

• Secure Machine independent web-

based tool allows flexibility of use

• Comprehensive Termsheet options

� Application Architecture

• Comprehensive Termsheet options

allow complete migration from

error-prone Excel based workings

• Large database of cleaned and

enhanced weather and satellite data

from various sources

• Provide Portfolio Analytics and

Monitoring Tools

SecureWeather Risk Management Application for

Insurance Business


� Historical Data Manager

• Secure upload & access to all weather

data in database

• Tools for identifying errors in data

• Options to compare data with nearby • Options to compare data with nearby

locations

• Tools for data cleaning & gap filling

� HDM Snapshots

All India Data: Historical Data Manager now contains historicalweather parameter data for each and every part of India

Filling Options: New options have been added in data filling to allowmore control over filled data. Backup data filling can now be used tofill data for only uploaded months, uploaded years or any day data

SecureHistorical Data Manager


SecureProduct Development Application


• Includes both Weather & Crop Insurance Products

• Can make diverse term sheets in single module

• Accommodates large variety of term sheets required for Indian MarketIndian Market

• Almost 95% of term sheet can be made on the App

• Upto 50 locations can be priced in Single Termsheet

• Allows de-trending and VAR Distribution fitting

• Normal, Lognormal and Gamma Distribution included

• Goodness of fit details are provided

• Allows user change the return on capital rate for pricing

SecurePDA Snapshots


SecurePortfolio Builder


� Can build one or more portfolio with different

weights or sales scenarios

� Portfolio gets automatically updated for any � Portfolio gets automatically updated for any

change in individual termsheets

� Allows additions and deletions in the Portfolio

� Displays Basic Portfolio Statistics

� Allows VAR Distribution fitting & de-trending

on the Portfolio payoffs

SecurePortfolio


GIS Display Displays the portfolio locations with PML distribution in

GIS. Any location marker can be clicked to get the acreage, structure

detail and historical index of the structure for that location.

SecureClaims Manager


� Claims monitoring for both

Weather and Crop Insurance

� GIS mapping of claims� GIS mapping of claims

� Comparison with claims in adjacent

weather stations

� Real-time monitoring possible if

data is updated

� Claim sheets can be extracted in

Excel for govt submission

SecureCMA Snapshot


The sky is not the limit

C R O P I N S U R A N C E – C L A I M A S S E S S M E N T


sonu.agrawal@weather- r isk .com

N O V E M B E R 2 0 1 4


Y I E L D E S T I M AT I O NV I D E O G R AP H Y & C L O U D S O U R C I N G

� ANDROID Application for SmartPhones



In - Season Crop Damage / Loss Yield Estimation

Dividing crop

period into

different

vegetative and

reproductive

stages.

� Vegetative stages – counted as consecutive

unfolded leaves, until the reproductive parts

are visible on the plants.

� Reproductive stages – as soon as the

flowers/tuber/ear head are visible until all the

kernels/seed/tuber are physiologically mature.

Crop damage

based on parts of

the crop which is

damaged.

� Crop Stand damage – Count or percentage

of crop stand area with no living axils/buds.



In-Season Crop Damage/Loss Yield Estimation

Crop damage

based on parts

of the crop

which is

damaged.

�Crop stem damage – Count and

percentage of crop stem snapped off with

physiologically unable to produce yield or

inactive.

�Branch damage – Position and percentage

of branches snapped off or damaged.of branches snapped off or damaged.

�Leaf damage - Count and percentage of

leaves are snapped off, shredded, de-

colourized and physiologically inactive that

wilts and dies.

�Ear/Pod/Head/Boll damage – Count and

percentage of yield part knocked

off/chaffed/shriveled/

broken or disease/pest infected.

In-Season Crop Damage/Loss Yield Estimation

Fruit damage � Count and percentage of fruits/ tree knocked

off/ malformed/ disease/pest infected and

quality degraded.

�Crop Yield

estimation

before

� Locating representative sample area.

� Determining the plant stand and row width.



before

harvesting

period.� Determining the plants (or ear/ fruit/ pod)

sample population / 100 sq mt.

� Filling observation report.

� Estimating the yield based on observations.

Forecast Yield (Y) = F (seed weight, plants, row width)

Y = …… t/ha

Yield Loss = Normal Yield – Forecast yield


Y I E L D E S T I M AT I O NU AV & S AT E L L I T E

UAV Images from 100 meters

Object - based hierarchical image analysis to classify imagery of

plots measured concurrently on the ground using standard

rangeland monitoring procedures.



UAV Images using regular camera from 250 meters

Objects are further classified into vegetative groups and to

species level by Rule Based Classification with well defined

thresholds and Near Neighbor Classification Algorithm, feasible

for few crops.



UAV Images using spectral camera from 250 meters

� Monitor Yields through Satellite

images (LISS4, LANDSA, SAR).

� We use LANDSAT images of

resolution 30m*30m.



Methodology

resolution 30m*30m.

� In case, of more detailed analysis,

will use LISS4 images of 5m*5m

resolution.

� Where visibility is affected due to

clouds, Microwave SAR data can be

used.

Methodology

Stasny-Goel method

(Bayesian method)

Bayesian yield estimation algorithm with a simple spatial component based on

the crop yields (close geographic proximity tend to be more similar than those

further apart).

Griffith method (AR

model)

Box-Cox and Box-Tidwell transformations are employed in conjunction with an

autoregressive specification so as to optimize agreement with model

assumptions.


Y I E L D E S T I M AT I O NM O D E LL I N G

assumptions.

Standard ratio estimation Multi-Phase stratified sampling is used to generate ratio estimates that are

weighted by the sampling rate.

Econometric methodology Use of Econometric principles and model building by considering endogenous

and exogenous variables such as prices of both product and inputs, farmer

planting decision and consumer preferences, etc..

Agro-Met methodology These model are crop growth stimulation model which is a function of

complex interaction of different physiological processes with the environment,

biotic and a biotic factors.

Geographic Information

System and Remote

Sensing Methodology

These model use RS and GIS information for quick assessment based on

multispectral, large area. Best for taking critical decision on procurement,

transportation, storage and trade.

Frame work for Integrated Crop Yield Forecast

Growth Monitoring

- Crop phenology,

Vegetative Index (NDVI)

Crop Survey, AWS and Satellite Data

Input

Empirical Method

YPA = f(xi)

Yield Per unit Area (YPA)



Agro Meteorological

Data – Precipitation,

PAR, Water Holding

Capacity, GDD

Input YPA = f(xi)

Xi = Meteorological

indices, Drought Index,

Vegetative indices

Crop yield estimate

Yield = f(NDVI , Rain Index, GDD, Ancillary data)

Integrated Yield Estimation Model

Crop yield can be estimated by adopting advance technologies such as remote sensing imagery

(RS), Geographical Information system (GIS), etc. and appropriate methodologies such as

Multivariate regression.

Crop models with other important inputs from weather data, land based observations and

economic parameters that influence the farmer’s decision on cultivating particular crop.



A general integrated yield estimation model for estimating crop yield.

Y = f (RD, RFs, W, S, Pt, I, G)

Y = Yield of the crop.

RD = Remote sensing imagery data (NDVI, SAR, IRS-WiFS, etc.)

RFs = Rainfall received during sowing and Vegetative stage.

W = other important weather parameters.

Pt = Previous year yield of the crop.

S = Soil type and its Parameters

I = Irrigated area availability.

G = Ground truth data by using CCE (crop cutting experiment) approach.

Parameters Source

Remote Sensing Imagery Landstat, NRRS, UAV’s and Aerial vehicles

Weather Data Ingen AWS, IMD, TRMM, GFS, NASA

Previous year’s Data State Govt. DES



Previous year’s Data State Govt. DES

Fertilizer and Input Details State Agriculture Department

Soil Details NBSS & LUP

Irrigation Details Central Ground Water Board

Ground truth Data Weather Risk (Field Survey data)

Wrl crop insurance2.0

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