Not All AgFintechs Are The Same

As banks consider ag lending, they must assess these five critical factors during the data vendor evaluation process


According to Jack Henry’s 2023 Benchmark study, 95% of all financial institutions plan to enhance their lending capabilities. To do so, many of them plan to increase their technology spend over the next two years. Much of the focus has been small business and commercial lending, especially as markets like mortgage see declines amid record interest rate hikes. However, one overlooked area is ag lending.


The value of farmland continues to soar, making it a compelling investment. Last year alone, the average value of farmland across the United States rose by 14%. In states like Iowa and Kansas, the value of an average farm acre increased by 22.4% and 25.2%, respectively.


Additionally, the U.S. Department of Agriculture cites that one-third of the 3.4 million farmers in America are over 65 years old. As these farmers retire, there is a significant opportunity for banks to capture this generational wealth transfer and support the next generation of farmers who will require loans and capital to establish their own operations or continue those they inherit.


Despite the attractiveness of ag lending, many traditional financial institutions are reluctant, primarily because they are unfamiliar with this industry. This market also poses a challenge for most lenders. Unlike traditional risk assessment processes for commercial loans, agriculture lending requires additional farm-specific information such as yield, crop diversity, grain market insights and ratio of owned versus leased farmland, to name a few.


These unique data points are not reported by analytics companies, publicly available to investors, or standardized by any measure—only adding to the complexity of the time-consuming process of assessing risk for agricultural lending.


This means financial institutions must look to fintechs. However, as the space becomes increasingly crowded with tech companies “band wagoning,” the pool of credible agfintechs remains small. In fact, the Independent Community Bankers of America (ICBA) pointed to a growing need among its members earlier this year.


Adding to this challenge, partnering with fintechs in general has its own set of risks. Even federal regulators have noted this, recently releasing guidance for working with third-party companies, particularly as it relates to risk and compliance.


Alas, banks are increasingly turning to fintechs to stay competitive, innovate and deliver enhanced services to their customers – including ag lending. But not all agfintechs are the same. The process of selecting the right partner can be a complex undertaking that requires careful consideration and strategic planning.


To ensure a successful partnership, banks must assess several critical factors during the data vendor evaluation process:


  1. Define Objectives

Before embarking on the search for a fintech, banks must clearly outline its objectives and goals. Having a well-defined vision will guide their search.


It’s also important to set realistic expectations related to data accuracy. Data modeling, especially for natural environments, is inherently complex and ever changing. For instance, weather forecasts are an important metric to assess risk within ag lending, but it’s only 80% accurate on that day. For long term forecasts the numbers are even worse, citing research from the US government National Weather Service Weather Prediction Center showing a steep decline in accuracy from three to seven days with accuracy dropping down to only 0.003%.


For banks entering ag lending, they must move beyond Loss Aversion bias, or the belief that a real or perceived loss is more severe than a real or perceived equivalent gain. Ultimately, there must be a high-level of trust and complete transparency within the bank-fintech relationship. In addition to an acceptance of living with(in) accuracy for data modeling.


  1. Compatibility

Banks must also evaluate fintechs based on their compatibility with their internal culture, values and strategic direction. Look for alignment in terms of mission, vision and commitment to innovation. Banks must also assess the compatibility of the fintech’s technology with its existing infrastructure. Seamless integration and scalability are crucial for ensuring a smooth implementation process.


Open and transparent communication is also key. Banks should discuss expectations, deliverables, timelines and support services before entering into a partnership. And finally, banks should choose a fintech company that demonstrates a long-term vision and commitment to building a sustainable, mutually beneficial relationship.


  1. Expertise and Track Record

Banks must also research the fintech’s expertise within agricultural lending. It’s not enough to demonstrate a knowledge of data science; fintechs must have a deep understanding of the industry.


Banks must also examine their track record of successful implementations, client relationships and industry recognition. A proven history of delivering solutions that align with the bank’s needs is a strong indicator of their suitability.


And don’t be fooled by large sums of venture capital raised. Some of the largest agtech companies as reported by AgFunderNews have been hounded by massive drops in valuation despite high hopes and media hype to the contrary. Instead, seek conversations with the CEO or founders to ensure they are building a company for the long haul and are willing to invest their time and energy into partnerships and clients.


One obvious question will be price. Anyone willing to offer value-based solutions will only do so with a paid pilot. Free or low-cost projects should raise questions to whether the fintech even values its own service. It may be tempting to say yes to ‘free’ but when have you accepted free services and gotten long term value in that product?


  1. Data Quality and Security

Given the highly regulated nature of the financial industry, banks must ensure that the fintech complies with relevant regulatory standards and possesses a robust data security framework. As mentioned earlier, even the federal regulators are providing guidance.


Does the vendor implement best practices for securely transferring data, validating authentication and safeguarding from potential attacks?


Not only does the data need to be compliant and secure, but reliable. Banks should seek out partners who have a proven tenure in providing validated, and even published, metrics to clients.


  1. Innovation and Future-Readiness

Banks must also choose a fintech that is committed to continuous innovation and adapting to emerging technologies. This ensures that the bank can stay ahead of industry trends and market needs.

Ag lending offers tremendous opportunity for banks, but it does require a unique set of skills and expertise. Fortunately, agfintechs can fill these knowledge and data gaps. The challenge is differentiating among the many partner choices. By considering these five critical factors, banks can better evaluate partners and create a solid path forward that generates greater profitability and helps solve a need within their community. 

About Author:
Jim O’Brien, CEO of Agrograph

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