Wednesday, August 12, 2015

Index Based Crop Insurance and Big Data

 There is some interesting work going on currently in relation to risk management in the agriculture space as it relates to 'big data.'

"Agriculture risk management is about having access to ‘big data’ since growth conditions, risk types, climate and insurance terms vary largely in space. Solid crop models are based on large databases including simulated weather patterns with tempo-spatial correlations, crop planting areas, soil types, irrigation application, fertiliser use, crop rotation and planting calendars. … Similarly, livestock data need to include livestock densities which drive diseases, disease spread vectors and government contingency plans to address outbreaks of highly contagious diseases…Ultimately, big data initiatives will support selling agriculture insurance policies via smart phones based on highly sophisticated indices and will make agriculture insurance and risk management rapidly scalable and accessible to a large majority of those mostly affected – the farmers." - from The Actuary, March 2015

Similarly, in another issue of The Actuary there is more discussion related to this:

"In more recent years IBI (Indemnity Based Insurance) has received a renewed interest, largely drivenby advances in infrastructure (i.e., weather stations), technology (i.e., remote sensing
and satellites), as well as computing power, which has enabled the development of new statistical and mathematical models. With an IBI contract, indemnities are paid based on some index level, which is highly correlated to actual losses. Possible indices include rainfall, yields, or vegetation levels measured by satellites. When an index exceeds a certain predetermined threshold, farmers receive a fast, efficient payout, in some cases delivered via mobile phones. "

The article notes several benefits related to IBI products, including decreased moral hazard and adverse selection as well as the ability to transfer risk. However some challenges were noted related to 'basis' risk, where the index used to determine payments may not be directly linked to actual losses. In such cases, a farmer may recieve a payment when no loss is realized, or may actually experience loss but the index values don't trigger a payment. The farmer is left feeling like they have paid for something without benefit in the latter case. The article discusses three types of basis risk; variable, spatial, and temporal. Variable risk occurs when other unmeasured factors impact a peril not captured by the index. Maybe its wind speed during pollination or some undocumented pest damage or something vs measured items like temperature or humidity. An example of spatial risk might be related to cases where index data may be data generated from meteorological stations too far from the field location to accurately trigger payments for perils related to rain or temperature.  Temporal risk is really interesting to me in terms of the potential for big data:

"The temporal component of the basis risk is related to the fact that the sensitivity of yield to the insured peril often varies over the crops’ stages of growth. Factors such as changes in planting dates, where planting decisions are made based on the onset of rains, for example, can have a substantial impact on correlation as they can shift critical growth stages, which then do not align with the critical periods of risk assumed when the crop insurance product was designed." 

It would seem to me that the kinds of data elements being capture by services offered by companies like Climate Corp, Farmlink, John Deere etc. in combination of other aps (drones/smartphones/other modes of censoring/data collection) might be informative to creating and monitoring the performance of better indexes to help mitigate the basis risk associated with IBI related products.


New frontiers in agricultural insurance . The Actuary. March 2015. DR AUGUSTE BOISSONNADE

Lysa Porth and Ken Seng Tan

See also:

Copula Based Agricultural Risk Models
 Big Ag Meets Big Data (Part 1 & Part 2)

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