1. Issues in Pricing and Reserving of Crop Insurance
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Anuj Tyagi
May 19, 2017

2. Agenda
Crop Insurance: An Introduction
Product designing and key Issues
Loss Reserving and key issues
Pricing and Loss reserving: way forward

3. Crop Insurance Indian Agricultural landscape :Biggest employment sector for the country
Contributes 14% to GDP and employ
more than 50% workforce
Demographically broadest economic sector
playing key role in socioeconomic fabric
70% small and marginal farmers using old and manual production techniques
Less than 40% of land is irrigated leading to high dependency on weather 3

4. Crop Insurance A key inclusion objective of Government running since 1972
Coverage
Covers crop losses due to aberrant weather conditions like deficit rainfall, excess rainfall, high/low temperature
Sum Insured is the loan sanctioned amount/cost of cultivation
Types
Yield based crop insurance covers crop losses based on actual yield estimation
Weather Based crop insurances covers crop losses based on the actual weather data procured for specified perils
Features
Area approach basis
Substantially, subsidized by Govt to make it affordable to farmers
Loanee farmers are covered mandatorily and non loanee farmers voluntarily 4

5. Crop Insurance Evolution over the years
GIC launched 1st ever crop insurance scheme in 1972 in 5 states
Yield based cover
Comprehensive crop insurance scheme launched in 1985 in 15 states
Yield based, with two third risk sharing by Government
Agriculture Insurance Company (AIC) was formed and National Agriculture Insurance Scheme(NAIS) launched
Yield based and area approach
Claim liability beyond 100% shared by Govt 5

6. Crop Insurance National Crop Insurance Program Emerges: Directional shift from claim subsidy mechanism to insurance solution
Pilot WBCIS(Weather Based Crop Insurance Scheme) launched
Area approach, premium subsidy and claim liability with insurance company
Private insurers allowed to participate
WBCIS and MNAIS became full fledged scheme under (National Crop Insurance Program) NCIP in 2013
Area approach, premium based subsidy and claim liability with insurance company
2016>
Pradhan Mantri Fasal Bima Yojana (PMFBY)- the flagship agri inclusion scheme of the current Government launched
Improved amalgamation of existing crop insurance schemes
18 implementing agencies allowed including 13 private players
One scheme for the entire country, marked improvements both for insured and insurers 6

7. Crop Insurance PMFBY-a new dimension to crop insurance : An effort to make scheme more beneficial as well as sustainable
Only crop insurance scheme to be implemented in the country, thereby expanding the universe for crop insurance
Weather Insurance also included as part of the new guideline
Scheme based on core contours of yield based settlements for crops arising due to natural calamites , pest & diseases with add ons
NAIS and MNAIS redrafted to bring in improvements from farmers as well as insurers perspective
Focus on making the scheme more sustainable & affordable to the farmers
Complete shift from loss subsidy plan to actuarial pricing; premium subsidy up to 90%
Use of technology for crop yield estimations, minimising discrepancies in yield estimations
Involvement of insurance companies in yield estimations
Use of Mobile phones for capturing crop cutting data
Usage of remote sensing & drone based imagery for claim settlements 7

8. Crop Insurance PMFBY - Emergence of crop insurance as a significant product line
With the launch of PMFBY, the crop insurance portfolio has shown a significant growth this year
Market size in FY16
NAIS* ` 2,100 cr +MNAIS ` 1,350 cr
+WBCIS ` 1,800 cr
= Total ` 5,250 cr
Shift to Premium Subsidy Scheme for NAIS with actuarial rates increased the crop portfolio to ` 8,500 cr
+ another ` 1,500 cr WBCIS = ` 10,000 cr
Increase in scale of finance by 30%
Market size - ` 13,000 cr
Increase in penetration from 22% to 35%
Total Market size - ` 21,000 cr
*Claim Subsidy Scheme
*Premium subsidy Scheme
Market size increased from ~ 5,250 cr to over ~21,000 cr 8

9. Crop Insurance Broad contours of existing crop insurance scheme
Yield Based Crop Insurance Scheme(PMFBY)
Comprehensive insurance product covering yield losses based on crop productivity data on area approach
Claims settlement done based on actual yield estimation done via CCE against benchmark yield based on historical data
Usage of technology(remote sensing and drone) for yield estimation study
High administrative/manpower requirement
Higher claim settlement period and prone to adverse selection
Weather Based Crop Insurance Scheme(WBCIS)
Weather Based insurance product covering yield losses based on weather parameters on area approach
Claim settlement done based on actual weather data procured from notified weather station against weather triggers fixed in policy
Usage of only weather data from notified station for yield correlation
Low administrative/manpower requirement
Lower claims settlement TAT and lesser chance of manipulation
Government of India provides premium subsidy upto 90% and plans to
increase penetration up to 50% in next 2 years from current 35%
CCE: Crop Cutting Experiments 9

10. Agenda
Crop Insurance: An Introduction
Product designing and key Issues
Loss Reserving and key issues
Pricing and Loss reserving: way forward

11. Weather insurance : products and designing parameters Parameters, perils covered and pay out structure
Weather Parameters
Perils covered
Pay out Structure
Rainfall
Deficit / Excess Rainfall
Binary Fixed Pay outs Staggered Fixed Pay outs Notional Payouts
Dry Spell
Temperature
High / Low Temperature
Relative Humidity(RH)
High / Low Humidity
Wind Speed
High Wind Speed
Sun shine hours
Low Sunshine hours
Combination of Multiple parameters
RH and Temperature
Range combination
Product designing requirements(from Govt./Client)
Crops, locations and policy period to be covered
Perils to be covered during different crop cycle stage during policy period
Index definition and triggers at different crop cycle stage

12. Weather Based Insurance : typical pricing flowchart Pricing follows statistical process but accuracy depends on quality of data
1. Procurement of historical weather data from data providing agencies such as IMD, State Govt, Private data providers
6.Calulation of average burning cost with more weightage to recent years trends
7. Loading based on data volatility
2. Analysis of weather data procured for availability and quality for reference policy period during historical years
5. Calculation of as if historical burning cost based on detrended historical weather data on defined policy indices and triggers
8. Expense loading on commission, management , weather station installation/data , contingency
3. Cleaning and gap filling of data based on average , simulation methods or graded data
4. Detrending of historical weather data
9. Calculation of final commercial premium rates 12 12

13. Weather Based Insurance : Pricing issues Quality of data & sensitivity to changing weather trends hold the key
Historical weather data
Extent of availability of weather data for pricing of products
Minimum requirement of 20 years for better trend analysis
Quality of available weather data
Basis risk
Pricing of insurance product of one location based on weather data of different location
Actual weather parameters varies from location to location
Product designing
Correlation of weather parameters to yield losses
Actual yield losses may not be reflective of deviation in weather parameters
Conversions of non parametric to parametric covers
Providing disease conducive cover based on combination of weather parameters
Changing Trends
Impact of recent trends due to global warming, el Nino phenomenon

14. Yield Insurance: products and designing parameters Coverage, perils covered and pay out structure
Prevented sowing/planting losses
Deficit rainfall Adverse seasonal conditions
Claims payment of 25% Sum Insured if Actual Sown area<25% Normal Sown area at notified unit level based on defined proxy indicators
Wide spread calamities losses(standing crops)
Drought, Flood, Dry Spells, Inundation Cyclone, Typhoon, Hurricane, Tornado Pests and Diseases, Hailstorm, Tempest Landslide, Natural Fire, Lightning
Claims payment as shortfall % of actual yield as compared to threshold yield
Post Harvest losses
Cyclone or cyclonic rains Unseasonal rains
Claim payment based on individual survey on at plot level on losses incurred within 2 weeks of harvesting
Localised calamity losses
Hailstorm Landslide Inundation
Claim payment based on individual survey at plot level
Product designing requirements(from Govt./Client)
Notified crops, locations and historical yield data
Historical yield data should be at least for years
Indemnity level for the policy
Defined calamity years to be excluded for calculation of Threshold yield
Threshold yield is the benchmark yield calculated based historical yield data for last 7 years excluding at max 2 calamity years as notified by Government
Details of cluster formed
No of districts, expected sum insured, sown area, cut off date for policy issuance

15. Yield Based Insurance: typical pricing flowchart Pricing based on historical yield data considering changing agri production practices and technological advancements
1. Procurement of historical yield data from State Government as a part of tender process
6.Calulation of weighed average burning cost based on exposure data at notified unit level
7. Nat Cat loading based on Agri contingency maps(frequency of drought, flood, cyclone etc)
2. Analysis of yield data procured for availability at granular level and quality for during historical years
5. Calculation of as if historical burning cost based on detrended historical yield data on defined indemnity and threshold yield
8. Event loading on add on covers of prevented sowing, localized risks, post harvest losses
3. Cleaning and Gap filling of data based on average , simulation methods or satellite based historical yield estimation
4. Detrending of historical yield data using slope and intercept
9. Expense loading of commission, management, claims, contingency and profit margin 15 15

16. Yield Based Insurance : Pricing issues Lack of data on Nat Cat & large variation between expected & actual insured data pose a big challenge
Historical yield data
Extent of availability of yield data for pricing of products
Minimum requirement of 10 years for better trend analysis
Basis risk
Pricing of products based on yield data higher than notified unit level
Actual yield data at gram panchayat level may varies with block level data
Lack of data on Nat Cat events
Frequency and intensity of Nat Cat events
Quantification of losses during Nat Cat events
Spread of risks
No of notified insurance units
Weighted average of exposures of each risk units
Gaps in actual and expected sum insured
Variation in expected sum insured to actual sum insured could extend up to 200% as happened in Karnataka during Rabi

17. Agenda Crop Insurance: An Introduction
Product designing and key Issues
Loss Reserving and key issues
Pricing and Loss reserving: way forward

18. Weather Based Insurance : Loss reserving Tracking of weather data on a regular basis holds the key
Policy inception stage
IBNR based on Pricing Loss Ratio of products
Policy period stage
Portfolio tracking based on
Tracking of weather data for locations
Analysis of exposure at notified weather station level
Finalization of portfolio loss estimate on fortnightly basis
Policy expiry stage
Claims management
Collection of certified weather data from notified weather station
Calculation of estimated loss% at notified weather station level
Finalization of loss% at portfolio level after exposure mapping

19. Yield based Insurance : Loss reserving Capability to gather and observe data at various crop cycles holds the key
Policy inception stage
IBNR based on Pricing Loss Ratio of products
Policy period stage
Portfolio tracking based on
Study of crop sown area based on satellite imagery and revenue record
Study of crop cycle stages and crop health based on satellite images and primary reports at field level
Tracking of weather data for locations
Analysis of exposure at notified unit level
Estimation of yield prediction based on NDVI based data
Analysis of localized risk losses, mid season adversity losses and post harvest losses based on satellite and drone based images
Finalization of portfolio loss estimate at fortnightly basis
Policy expiry stage
Claims management
Monitoring of Crop Cutting Experiments
Calculation of estimated loss% at notified unit level
Finalization of loss% at portfolio level after exposure mapping

20. Weather and Crop Insurance :Loss reserving issues Over dependence on past data and underestimating the recent trends can give shocks
Time lag in mapping exposure data at notified unit level
Significant delay in finalization of exposure data due to large set of farmers level data in hard copies which needs to be entered manually
Gaps in expected sum insured and actual sum insured at notified unit level
High/low penetration due to factors like loan disbursement and awareness
Loss quantification at policy period stage due to
Mid season adversity, localized event, unforeseen CAT events and post harvest losses
Extensive administrative requirements
Large no of Crop Cutting Experiments to be monitored, manually
Underestimated usage of technology in CCEs
Actual loss ratio may significantly vary as compared to actuarially calculated pricing loss ratio as historical trends may have little relevance in future climatic conditions

21. Agenda Crop Insurance: An Introduction
Product designing and key Issues
Loss Reserving and key issues
Pricing and Loss reserving: way forward

22. Crop Insurance : Pricing and Reserving Way forward
Technical pricing
Possibility of risk selection based on geo spread and crop wise location wise risk assessment enabling actuarial rates charging for the scheme
Improvement of quality and availability of historical data
Usage of satellite images of historical years and simulation model for yield /weather data gap filling and verification
Fine tuned Nat Cat model
Under process development of Cat event maps such as flood maps, cyclone maps etc
Exposure tracking
Enrolment of farmers and CCEs through mobile applications
Claims management
CCE monitoring mandatorily via CCE App with images/videos of crop with geo stamping
Usage of Satellite /Drone based technology for loss surveys
to the insurers 22

23. Thank You 23