Historically, banks have been reluctant to pursue agricultural lending, largely because of the complexity with assessing risk. This may be a missed opportunity. Ag lending is expected to rise, which could help banks weather sluggish growth across other markets.
With inflation at 40-year highs and much of the country enduring long-term drought conditions, the need for ag loans is growing. Fertilizer prices increased by as much as 300% during 2021 while the price of farm equipment continues to climb. The U.S. Department of Agriculture has forecasted that 2022’s net farm income will be down from last year when accounting for inflation’s impact.
To cover rising costs, farmers will need to borrow. New and smaller farming operations, however, are reporting challenges with finding banks that will lend them money. Currently, more than half of the ag operating loans are generated by the Farm Credit System, a network of 67 lenders. This group is but a tiny percentage of the roughly 5,000 banks nationwide.
Banks can capitalize on this opportunity, but a different approach is needed.
Assessing agricultural risk is complex but banks can simplify this process by leveraging AI and machine learning. Banks must also have access to reliable and updated data for land appraisals, and then automate processes to speed loan approvals. Finally, digitalizing the borrower experience is critical to compete with savvy lenders and meet borrowers’ expectations, especially as competition increases.
Assessing agricultural risk: Unlike other loans, determining the risk of an ag loan isn’t as simple as pulling FICO scores, verifying income and employment, and reviewing financial statements. Assessing risk involves accessing farm financials along with farm production information, neither of which is standardized nor structured for ease of use.
For many banks, collecting this information is a manual, time-consuming and error-prone process that slows loan decisioning. Data services built on machine learning and artificial intelligence can identify and replicate field-level patterns and generate a risk score specific to an agriculture portfolio.
Variables like weather patterns, historical yield production and management practices also come into play. These risk factors create challenges for calculating the lost cost or interest rate for an operation. Scientifically validated data for climate, crop production and land management practice information are already available, but banks need the technology to aggregate this information and then use it to properly assess risk.
Automation combined with validated data sources is also key. Land is among the most valuable assets of farmers, but its value is the most challenging to accurately assess. This becomes increasingly important as financial institutions seek to properly manage risk across their lending portfolios.
Speeding up loan approvals: Accurate loan-to-value (LTV) ratios are needed to compare the cost of a loan to a fair market value of a property. Land is rapidly being developed across the United States, and that relative scarcity is leading to higher values. The per-acre value of cropland has doubled since 2007, making it critical that banks have access to reliable and updated data for land appraisals, and automated processes to speed loan approvals.
A case in point: An ag lender recently created an automated land classification tool. Data is entered into an internal risk model to allow for more reliable land classification information and expedited LTV ratios. This streamlined the manual reporting process that requires validation from the USDA, Farm Service Agency, county offices and many other decentralized sources. Compared to a manual appraisal report that can take weeks to generate, the automated report generates accurate acreage and land use in minutes.
Digitalizing the experience: Consumers expect digital banking, and farmers are no exception. Today’s farmer is used to the conveniences experienced in all other aspects of their life. They shop on Amazon, they watch Hulu and they bank online. Applying for an agricultural loan shouldn’t be any different.
Unfortunately, digitalizing the ag lending experience hasn’t been prioritized at traditional banks. To remain competitive and gain market share, banks should embrace digital banking practices by sharpening the digital prequalification and preapproval process. This is already a priority for other loans.
As mortgage, auto and other lending markets slow, banks will be challenged with driving growth. Ag lending may offer an answer that allows banks to capitalize on a market historically dominated by a handful of lenders.
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