How Is AI Optimising Insurance Payout For One Of The World’s Largest Crop Insurance Programs?
Close to 1.36 billion people reside on India’s 2,973,190 square kilometres, making it the second-most populous country on Earth. There is a widespread concern regarding feeding and clothing the ever-increasing global population — expected to reach 9.7 billion within the next three decades — and the apprehension is ever more heightened in India.
Among many questions that plague the state and central governments, one that tops the list year after year is “are the Indian farms capable enough to provide for the 1.36 billion people?”.
In the given backdrop, how does the government know if the country’s farmlands, 80% of which are smallholdings and traditionally farmed, produce enough food for all?
Crop Cutting Experiments (CCE) and How they Help
Crop Cutting Experiments or CCE, refer to an assessment method employed by governments and agricultural bodies to accurately estimate the yield of a crop or region during a given cultivation cycle. The traditional method of CCE is based on the yield component method where sample locations are selected based on a random sampling of the total area under study. Once the plots are selected, the produce from a section of these plots is collected and analysed for a number of parameters such as biomass weight, grain weight, moisture, and other indicative factors. The data gathered from this study is extrapolated to the entire region and provides a fairly accurate assessment of the average yield of the state or region under study.
In addition to conducting CCEs to estimate the total yield, the Central Government uses this data for its PradhanMantri Fasal Bima Yojana (PMFBY), which is a scheme that assists insurance companies to disburse payment for farmers’ crop insurance claims seamlessly. The PMFBY requires each state to carry out at least four CCEs in every village panchayat for each crop and submit the yield data to insurance companies within one month of harvest. Digital platforms like SmartFarm® and SmartRisk® ensure that these claims and payment processes are backed by data, and are as accurate as they can get.
The data gathered from CCE is useful to multiple stakeholders in the agricultural value chain. While governments use it for planning agricultural policies and programs for the future, the information helps financial institutions with all the inputs they need before offering loans or insurance coverage if there is a poor harvest or crop failure.
The biggest drawback of this traditional method towards CCE is that it is dependent on a number of variables such as administrative setup, type and size of the field staff, farmer cooperation, and harvest conditions. Especially in a scenario where there are nearly 2.5 lakh gram panchayats or village councils in India that are scattered, along with inadequate trained human labour or the time to facilitate these experiments effectively, there needs to be a more efficient way of utilising the resources and obtaining an accurate yield estimation within the short harvesting window.
How Technology Makes It Easier
The use of technology in agriculture has made the practice of farming a lot more predictable and efficient. When compared to the traditional method of CCE using random sampling, the use of remote sensing and other technological advancements in agriculture provides a far more accurate and timely estimation of yield. But the impact of technology starts much earlier. For conducting CCE experiments, products like CropIn’s SmartFarm® capture the precise location and size of the farm, details of the farmer, and the crop details right from the pre-harvest stage. This ensures that the field data is accurate, enabling authorities to easily use relevant data at the appropriate time.
This makes the process of sample selection for CCE a lot more scientific and less arbitrary. SmartRisk®, an AI- and ML-powered digital platform by CropIn, makes use of both ground-level data and satellite imagery to identify the plots that are apt for these experiments. A dedicated and highly skilled data science team analyses millions of data points and runs them through numerous criteria to zero in on the farm plots that will provide the most accurate sample for the region. With the help of this data, authorities can easily identify the right plots that are to be included in the study, removing all ambiguity from the process of selection. The benefits of technologically-aided CCE are not just limited to choosing the right sample for the study. Apart from offering a more optimised method of plot selection, many of these products also help in providing the government and insurance companies with scientific, scalable, and accurate reports for future processing.
Impact on the Stakeholders
The use of data and technology in agriculture has a far-reaching impact on agribusinesses and enables a more efficient and accurate decision making throughout the cultivation cycle. The smart approach towards CCE is beneficial on multiple levels:
The use of technology helps address the issue of conducting a large number of CCEs with limited manpower during a short harvesting window. By using a digital platform such as CropIn’s, the paperwork and subsequent possibilities of human error are considerably reduced. Additionally, CropIn provides the trained field managers to oversee the process and gather the required data, thus reducing the burden on the government’s side. With the help of these scientific methods, the government can improve overall efficiency by utilising their resources in the best possible way.
The data provides a more accurate report of the crop in question and allows for the timely settlement of claims in a more just manner. The reports derived using CropIn’s platform are data-driven and accurate, hence removing possibilities of fraudulent claims or inaccurate payment disbursals. It also allows insurance companies to customise crop insurance schemes and products based on real-time data gathered from each individual farm.
Digitisation of the CCE process enables impartial settlement of claims, which is of immense benefit for farmers. It cuts down the stress on the farmer to provide proof of his/her claims, thereby also reducing the effort and time spent in the process.
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