How AI, Machine Learning & Digital Processes Can Allow Banks to Help Farmers Through Tough Times

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From rising inflation to long-term drought, the USDA is forecasting that 2022 net farm income will decline by nearly $10 billion, or 8 percent from the prior year.

Soaring inflation is also driving up costs. As an example, The American Farm Bureau reported in December that some fertilizer prices had increased up to 300 percent during 2021 and warning that these increases would play a huge role in the future success of the upcoming season. With fertilizer roughly representing 15 percent of farmers’ operating budgets, the increased price will impact producers’ profits.

Farm equipment costs are also skyrocketing yet demand remains high. Ongoing supply chain issues are making it difficult to find new tractors or planters while the price for used equipment is up 30 percent, according to experts.

As a result, American Banker reported that ag bankers believe farmers will need to borrow more to cover higher costs. While this will fuel loan demand, it also means risk for banks. Even then, new and smaller farming operations are reporting challenges with accessing capital. In a recent report from WFYI Public Media, a member of NPR, many farmers are struggling to find financing, with one saying that while new equipment is scarce, banks are unlikely to lend him money on used equipment.

Farmers need access to capital quickly, presenting an opportunity for banks to tap into a new market – This is especially important for banks as mortgage and auto loan applications continue to plummet. But to be competitive, banks must speed loan decisioning while also balancing risk – two things banks have struggled with in ag lending.

A different approach to this market is critical.

Standardizing Agricultural Risk

Determining the risk of an agricultural investment isn’t as straightforward as running a FICO credit score or just looking at financial statements of the grower. There are also unique challenges and added complexity driven by the need to calculate and compare farm financials with farm production information, neither of which is standardized nor structured for ease of use. Collecting all the relevant information needed to adequately assess farm risk is often a manual, time-consuming and error-prone process, and for most traditional agricultural lenders data they never request.

Additionally, variables like weather patterns, historical yield production and management practices, impact risk and create unique challenges for calculating the lost cost or interest rate for an operation.

Banks must have access to scientifically validated data like climate, crop production and land classification  information, combined with scalable and accurate machine learning and artificial intelligence solutions to help identify and replicate field-level patterns and generate a risk score that is unique and specific to an agriculture investment.

Without this, they are essentially flying blind and unable to appropriately assess risk at the field or the portfolio level.

Embracing Digital Prequalification & Pre-Approval Processes

Farmers are also becoming more tech-savvy and – just like everyone else – apply for and manage loans online. According to a 2022 study from PYMNTS, 72 percent of borrowers manage their loans digitally.  This will only continue to increase. The Digital Lending Market was valued at over $311 billion in 2020 and is expected to eclipse $587 billion by 2026.

To remain competitive among farmers, banks must embrace digital banking practices by sharpening the digital prequalification and preapproval process. By offering an easier and more convenient way to apply for ag loans, banks can attract more business.

As it stands, they are currently losing out to competitors who do offer these tools. In fact, less than half of ag lending goes through community banks – most of that business is with the Farm Credit System and their 71 lenders. With fewer lenders, this creates limitations on the industry as a whole.

By digitizing this process, banks not only offer a better experience for the borrower, but they are able to speed the process from application to funding.

Farmers need to move quickly, and banks need the right technology to accelerate lending decisions without added risks. By automating the onboarding process and standardizing underwriting and risk assessment, banks can approach ag lending with speed, efficiency and lower risk. In turn, they can quickly provide farmers with needed financing, all while tapping into a new market segment to boost portfolio growth without unnecessary risk. It’s a win for everyone.

About Author:
Jim O’Brien, CEO of Agrograph