Extreme weather has hammered the accuracy of traditional insurance and lending risk models. Insurers and lenders whose profit margins have been shrinking year over year given the severity of weather and its negative impact on crop yields are painfully aware of the problem.
Insurers and lenders are now looking to implement more accurate datasets that consider what happens before, during and after a growing season. Such datasets, paired with sophisticated AI (Artificial intelligence) and analytics, will allow these businesses to be more efficient and predictive, produce better risk scores, and ensure they make the best decisions during the underwriting phase of a loan or policy.
Farmers will benefit from the AI-assisted results by getting better rates and policies that mirror what they are actually doing, as insurers and lenders embrace these new finely-tuned data. When farmers do the right things in their fields, such as limiting water and fertilizer applications, and are great stewards of the earth, they should get rates that better align with their farming practices.
Slow and inaccurate methods of assessing damage from severe weather are outdated
Traditionally, when extreme weather leads to a hailstorm, heavy wind or flooding,, a farmer will call their agent, who logs the call and tries to get an adjuster to the area.
Sometimes getting an adjuster to inspect a damaged area can take several weeks. Often the adjuster is a team of one trying to assess the damage from the perimeter or using a low-precision drone.
Once that inspection occurs, it may take several additional weeks to understand a weather event’s total impact on a crop several thousand acres in size.
Adjusters may also struggle to make accurate assessments given their limited access to the fields or regions. For example, a peripheral evaluation may determine that only 28% of the field was damaged. However, a massive downdraft may have left a giant hole in the center of the field, obliterating part of the crop. So, the actual exposure might be more like 42%.
Better, faster information from AI and data analytics enables quicker claims processing
Getting a complete view of what’s happening and measuring damage down to the plant level quickly and accurately enables adjusters and financial services to understand the true impact on the field. This allows growers to know how much they may have lost and lenders and insurance companies to know their exposure so that they don’t overpay or underpay on claims and can process the claims 30%-40% faster.
Imagine Florida had a severe hurricane. Insurance companies share with growers they plan to fly over citrus orchards likely to have been harmed by the storm.
This is just what Ceres AI did last year following a recent Florida hurricane. A team of experts quickly flew over about 11,000 acres of citrus trees, using computer vision to assess the damage. The team precisely identified over 92,000 trees that had sustained damage. Ceres AI was able to asses which trees were damaged or totally destroyed and the exact location within the vast orchards.
Using AI computer vision provided all the stakeholders with detailed information in just 24 to 48 hours, accelerating and increasing the accuracy of claims processing. When the soil dried out, the grower had the information they needed to quickly find, treat and replace the damaged trees for near immediate results!
Extreme accuracy and anomaly identification are valuable year-round
With traditional approaches, insurers and lenders also might not have accurate data on the field size or the planted crop that emerged. The existing document might indicate that a farmer owns 1,000 acres on which to grow corn – even though only 800 acres of the land is arable. The farmer may plant soy rather than corn to get a better price. Yet those details are not automatically updated in the insurance or loan application process.
Surveying and measuring a field quickly, and then generating a digital image that denotes the field’s footprint with the exact field boundaries down to 15 centimeters is extremely valuable.
With new digital datasets, farmers, insurers and lenders can validate the total size of the field, how much of the acreage actually gets planted and what crop ultimately emerges from it.
Computer vision makes it possible to spot anomalies emerging in some parts of the field weeks before the human eye or other sensors detect them. The AI then makes recommendations to farmers, who can act fast to remedy such situations.
For example, a farmer could add a fungicide or use more or less water to prevent further damage to those plants and ensure an optimal yield. The farmer’s bank and insurance company benefit, too, because they don’t have to cover a payment shortfall or a claim.
Maintaining sustainable and regenerative practices through AI and data analytics
Understanding what's happening in their fields at any given time allows farmers to precisely adjust how much water, fertilizer and chemicals to use for optimal growth and minimal waste.
Leveraging AI to understand exactly what a crop needs – and when and where it needs it –empowers farmers to run their operations more sustainably. This benefits farmers, the health and well-being of people who consume the food that they produce, and the world at large. Such fine-tuning is critical given the importance of maximizing yield while not impacting farmer’s income.