Anuj Tyagi May 19 2017 Agenda Crop Insurance An Introduction Loss Reserving and key issues Product designing and key Issues Pricing and Loss reserving way forward Crop Insurance Indian Agricultural landscape Biggest employment sector for the country ID: 619932
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Slide1
Issues in Pricing and Reserving of Crop Insurance
Anuj Tyagi
May 19, 2017Slide2
Agenda
Crop Insurance: An Introduction
Loss Reserving and key issues
Product designing and key Issues
Pricing and Loss reserving: way forwardSlide3
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
3Slide4
4
Crop Insurance
A
key inclusion objective of Government running since 1972Slide5
5
Crop Insurance
Evolution over the yearsSlide6
6
Crop Insurance
National Crop Insurance Program
Emerges:
Directional shift from claim subsidy mechanism to insurance
solutionSlide7
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 farmersComplete 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 settlements7Crop InsurancePMFBY-a new dimension to crop insurance : An effort to make scheme more beneficial as well as sustainable Slide8
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 crIncrease in penetration from 22% to 35% Total Market size - ` 21,000 cr8Crop InsurancePMFBY - Emergence of crop insurance as a significant product line
*Claim Subsidy Scheme
Market size increased from ~ 5,250 cr to over ~21,000 cr*Premium subsidy SchemeSlide9
9
Crop Insurance
Broad contours of existing crop insurance scheme
CCE: Crop Cutting Experiments
Government of India provides premium subsidy
upto
90% and plans to
increase penetration up to 50% in next 2 years from current 35% Slide10
Agenda
Crop Insurance: An Introduction
Loss Reserving and key issues
Product designing and key Issues
Pricing and Loss reserving: way forwardSlide11
Weather insurance : products and designing parameters
Parameters, perils covered and pay out structure
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 stageWeather ParametersPerils covered Pay out StructureRainfall
Deficit / Excess RainfallBinary Fixed Pay outs Staggered Fixed Pay outs Notional PayoutsDry SpellTemperatureHigh / Low TemperatureRelative Humidity(RH) High / Low Humidity
Wind Speed
High Wind SpeedSun shine hoursLow Sunshine hoursCombination of Multiple parametersRH and TemperatureRange combinationSlide12
1.
Procurement of historical weather data from data providing agencies such as IMD, State
Govt
, Private data providers
2.
Analysis of weather data procured for availability and quality for reference policy period during historical years3
. Cleaning and gap filling of data based on
average , simulation methods or graded data
6
.Calulation of average burning cost with more weightage to recent years trends5. Calculation of as if historical burning cost based on detrended historical weather data on defined policy indices and triggers4. Detrending of historical weather data 7. Loading based on data volatility 8. Expense loading on commission, management , weather station installation/data , contingency
9. Calculation of final commercial premium ratesWeather Based Insurance : typical pricing flowchartPricing follows statistical process but accuracy depends on quality of dataSlide13
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 analysisQuality of available weather dataBasis riskPricing of insurance product of one location based on weather data of different location
Actual weather parameters varies from location to locationProduct designingCorrelation of weather parameters to yield lossesActual 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 phenomenonSlide14
Yield Insurance: products and designing parameters
Coverage, perils covered and pay out structure
Product designing requirements(from
Govt./Client)
Notified crops, locations and historical yield dataHistorical 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 formedNo of districts, expected sum insured, sown area, cut off date for policy issuanceCoveragePerils coveredPay out Structure
Prevented sowing/planting lossesDeficit rainfallAdverse seasonal conditionsClaims payment of 25% Sum Insured if Actual Sown area<25% Normal Sown area at notified unit level based on defined proxy indicatorsWide spread calamities losses(standing crops)Drought, Flood, Dry Spells, InundationCyclone, Typhoon, Hurricane, TornadoPests and Diseases, Hailstorm, TempestLandslide, Natural Fire, LightningClaims payment as shortfall % of actual yield as compared to threshold yield
Post Harvest losses
Cyclone or cyclonic rainsUnseasonal rainsClaim payment based on individual survey on at plot level on losses incurred within 2 weeks of harvestingLocalised calamity lossesHailstormLandslideInundationClaim payment based on individual survey at plot levelSlide15
1.
Procurement of historical yield data from State Government as a part of tender process
2.
Analysis
of yield data procured for availability at granular level and quality for during historical years
3. Cleaning and Gap filling of data based on average , simulation methods or satellite based historical yield estimation
6
.Calulation of weighed average burning cost based on exposure data at notified unit level
5.
Calculation of as if historical burning cost based on detrended historical yield data on defined indemnity and threshold yield 4. Detrending of historical yield data using slope and intercept7. Nat Cat loading based on Agri contingency maps(frequency of drought, flood, cyclone etc) 8. Event loading on add on covers of prevented sowing, localized risks, post harvest losses
9. Expense loading of commission, management, claims, contingency and profit marginYield Based Insurance: typical pricing flowchartPricing based on historical yield data considering changing agri production practices and technological advancementsSlide16
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 analysisBasis riskPricing 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 eventsFrequency and intensity of Nat Cat eventsQuantification 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 insuredVariation in expected sum insured to actual sum insured could extend up to 200% as happened in Karnataka during Rabi 2016-17Slide17
Agenda
Crop Insurance: An Introduction
Loss Reserving and key issues
Product designing and key Issues
Pricing and Loss reserving: way forwardSlide18
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 stagePortfolio tracking based onTracking of weather data for locationsAnalysis of exposure at notified weather station level
Finalization of portfolio loss estimate on fortnightly basisPolicy expiry stageClaims managementCollection 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 mappingSlide19
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 stagePortfolio tracking based onStudy of crop sown area based on satellite imagery and revenue recordStudy of crop cycle stages and crop health based on satellite images and primary reports at field level
Tracking of weather data for locationsAnalysis of exposure at notified unit levelEstimation 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 levelFinalization of loss% at portfolio level after exposure mappingSlide20
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 levelHigh/low penetration due to factors like loan disbursement and awarenessLoss 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 conditionsSlide21
Agenda
Crop Insurance: An Introduction
Loss Reserving and key issues
Product designing and key Issues
Pricing and Loss reserving: way forwardSlide22
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 verificationFine 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 managementCCE monitoring mandatorily via CCE App with images/videos of crop with geo stampingUsage of Satellite /Drone based technology for loss surveysto the insurers22
Crop Insurance : Pricing and ReservingWay forwardSlide23
Thank You
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