Artificial intelligence is helping to solve data fragmentation within the agriculture industry, removing technological barriers to integrating and standardizing data across the ecosystem. For insurers, AI enhances risk management by improving claims processing and underwriting accuracy as well as reducing the chances of fraud.
That is critically important now that climate change is deeply affecting the agricultural sector.
More extreme weather events more often, shifting precipitation patterns, and increasing temperatures have upended old models. That’s prompting growers to rethink their operations and leading financial firms to find ways to adapt insurance offerings to reflect this volatility.
The Data Challenge
Data can inform the decisions organizations make and the actions they take, and we’ve seen an explosion of field-level data as drones have begun to collect imagery and in-field sensors gather information. But, until recently, scalability and limited processing power have been a technical challenge, and the agriculture sector (among others) has struggled with diverse and sometimes siloed data sets, various data formats and a lack of standardization.
Regional land survey data, crop production reports, weather data, and market reports may differ slightly between counties or states. Sometimes the same data are presented in different formats or reported over different intervals, such as hourly vs. daily. Even essential information like crop types and weather events can be coded differently across data sources.
Additionally, different data sources might keep reporting bad data when a sensor is broken.
All that has left organizations to compile data sets individually, update data using manually intensive processes, and sometimes base decisions on incomplete or bad data, leading them to the wrong outcomes. If a company or operation doesn’t understand historic weather or yield trends, for example, it might miscalculate regional risk or adopt a less-than-optimal management practice.
See also: Risk Management Strategies for Agribusinesses
How AI Helps
But AI platforms and the powerful, scalable computing resources that now underpin them, provide advanced tools to integrate, standardize, and analyze data from diverse sources.
AI can generate uniform, complete data sets that have the same set of data for all fields in an entire portfolio and incorporate new data at regular automated intervals. AI also can identify and remove outliers or interpolate when data is missing for a period or region.
By defragmenting data, the ag ecosystem for the first time can compile, access, and benefit from complete data sets about cropping health and history, management practices such as planting dates, soil health practices like cover cropping and reduced tillage, and more. People can also compare crops with crops from other years or benchmarked across other fields in the region.
Organizations have access to more data than ever. With AI, companies can evaluate data quality and quickly combine data for real-time decision-making.
This benefits the entire ag value chain – growers, landowners, lenders, and insurers.
Trending insurance use cases
For insurers, enhancing the claims process and improving underwriting accuracy are two use cases where AI shines. Here’s how AI works and adds value in these two scenarios.
Enhancing claims processing
One way that AI drives claims processing improvement is via automated damage assessments. AI leverages remote sensing and machine learning to identify crop anomalies or damage patterns. That makes it easier to measure and model specific parts of the field or plants damaged by a weather event and to precisely quantify those details. As a result, insurers can reduce their reliance on in-field inspections and do faster, more accurate claims assessments.
AI can allow for faster claims determinations, too. That can shave the time it takes for growers to get paid from months to days or weeks, so everyone across the ecosystem benefits.
Additionally, AI can analyze historic data such as claims data, cropping history, and satellite imagery to identify unusual patterns. It can then flag claims with inconsistencies or identify behaviors that indicate a potential for fraud. An inconsistency might involve discrepancies between reported and observed damage. A behavior might be not planting the reported crop.
See also: Risk Management for Agriculture
Improving underwriting accuracy
AI also can fuse and analyze historic and in-season data such as crop yields, management practices, soil conditions, and weather patterns, to help with risk assessments. That helps underwriters predict the severity or likelihood of an event, which can provide the intelligence they require to set appropriate premiums.
Insurers can also leverage AI for dynamic or customized policy development. This could involve using historic and current-year data to create a detailed risk management profile for a policyholder or region and establish customized insurance products to allow an insurer to reach new markets, for instance. A dynamic policy might adjust premiums based on real-time data. This could also be used as an incentive to encourage regenerative practices like cover cropping or reduced tillage that have gradual but long-term effects on soil health and land resiliency.
Improved risk management through early alerts is another area in which AI can add value. By using AI to build predictive models, a grower could get an early alert about a pest or disease outbreak and take preventative action, like applying a costly pesticide to the most at-risk fields or parts of fields. In this kind of scenario, both the grower and the insurer could limit their losses.
The bottom line is that AI can improve financial partners’ loss ratio through more efficient and accurate claims processing, establishing more accurate risk assessments and targeted policies, and enhancing and automating fraud detection. In the process, AI benefits growers, who can enjoy better risk management, more targeted interventions, and optimal use of resources.
