AIR Worldwide releases multi-peril crop insurance model for India
Model contains 28 years of historical data and over 10,000 simulated crop years
AIR Worldwide releases multi-peril crop insurance model for India
INSURANCE NEWS
By Gabriel Olano
06 Nov 2019

Catastrophe risk modelling firm AIR Worldwide (AIR) has released a multiple peril crop insurance (MPCI) model for India.

According to AIR Worldwide, the model will help support probabilistic assessments of potential losses caused by yield shortfalls for 11 major crops across the two main India crop-growing seasons. It is an event-based model, with an event defined as an individual crop year made up of a kharif season, which is the ‘summer’ period from late spring into autumn, and the following rabi season, or the ‘winter’ period from autumn into the following spring.

Read more: Crop insurance proves fertile ground for India’s non-life insurance sector

The model, which is currently available in the Touchstone Re catastrophe risk management system, contains a stochastic catalogue of 10,000 simulated crop years, each containing a kharif season and the following rabi season. It describes a wide range of possible crop loss scenarios, both common and rare, in the two seasons, AIR Worldwide said.

It also features a historical catalogue of losses based on a recast of the years 1979 through 2017. Both the stochastic and historic recast yield and loss catalogues reflect current crop ‘technology’ levels (for example, current crop genetics, farmer skill, availability of chemical inputs), insurable exposure by district, and PMFBY (Pradhan Mantri Fasal Bima Yojana) policy conditions, as revised in late 2018.

“Despite the long and extensive history of agriculture in India, it is only recently that multiple-peril crop insurance has been widely available to farmers,” said Dr Praveen Sandri, executive vice president and managing director, AIR Worldwide India. “One of the most valuable components of this model is that reinsurers in the India crop market can have their exposure data analysed at the district level or aggregated to the cluster and/or state level for individual crops or all crops combined. Additionally, the model can provide guidance toward putting together a balanced book of business that considers geographic correlations in a complex agricultural market.”

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