Issues in Pricing and Reserving of Crop Insurance Currency – INR or Euro Rupee font Anuj Tyagi May 19, 2017

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

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

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

Crop Insurance Evolution over the years 1965-1972 GIC launched 1st ever crop insurance scheme in 1972 in 5 states Yield based cover 1972-1999 Comprehensive crop insurance scheme launched in 1985 in 15 states Yield based, with two third risk sharing by Government 1999 -2007 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

Crop Insurance National Crop Insurance Program Emerges: Directional shift from claim subsidy mechanism to insurance solution 2007-2009 Pilot WBCIS(Weather Based Crop Insurance Scheme) launched Area approach, premium subsidy and claim liability with insurance company Private insurers allowed to participate 2009-2015 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

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

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

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

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

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

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

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

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 10-15 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

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

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 2016-17

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

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

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

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

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

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

Thank You 23