Loan Decisioning and Its Geospatial Risk Data Core

Loan decisioning is the process by which banks or lenders decide whether or not to approve a loan. This process is becoming increasingly automated, as new technologies make it possible to analyze data to a greater degree than ever before. McKinsey calls this a “golden age” of risk analytics, allowing financial institutions to “measure and mitigate risk more accurately and faster” than traditional underwriting methods.

But in some industries, especially the agricultural industry, relying solely on financial factors such as the credit risk of the borrower isn’t enough. Loan decisioning must take geospatial factors into account because the risks that loan decisioning deals with are geospatial in nature. Aggregating multiple data sets and viewing them in a geospatial, map-based format can improve lenders’ ability to identify loan risk.

This article will explore the importance of effective loan decisioning in ag lending, and how geospatial tools can help to streamline and speed up this process.

The Importance of Proper Loan Decisioning

The process of approving a loan goes through several stages, which often include a pre-approval, application, and underwriting process. Loan decisioning is the stage at which a loan is approved, denied, or sent back for more information.

Although loan decisioning can be automated for faster, more predictable results, this process is only as good as the data that financial institutions have access to. As more and more banks turn to advanced risk analytics to improve the loan decisioning process, it’s important for businesses in agricultural finance to ensure their tools and methods are up-to-date. According to McKinsey,

“Banks that are leading the analytical charge are exploiting both internal and external data…. Furthermore, they are getting strong results by combining internal and external data sets in unique ways, such as by overlaying externally sourced map data on the bank’s transaction information.”

A better loan decisioning process allows for greater financial security because lenders can make educated decisions about borrower risk across their entire lending portfolio. 

Without an effective loan decisioning process and the appropriate tools, lenders increase the risk of their borrowers defaulting on loans due to unseen financial, environmental, or regulatory factors.

In an age of climate uncertainty and ensuing ESG reporting requirements, it will be especially important for lenders to take risk factors into account, recognizing that they are geospatial in nature. 

This will enable financial institutions in all industries, but especially the agricultural sector, to build financial resilience in the 21st century.

Geospatial Risk Factors in Loan Decisioning

Geospatial risk factors in ag lending come in many forms, from purely financial to questions of social and natural capital. These include:

Equipment: 

Does a farming operation have the equipment, infrastructure, and people necessary to keep the operation afloat? This could include factors such as the type of irrigation method used and the quality of wells on the property. For example, lenders could review a pump test report to assess well quality before approving a loan.

Cash reserves: 

Does a borrower have enough cash in the bank to withstand long-term challenges such as a megadrought? The Environmental Defense Fund points out that “federally subsidized crop insurance remains an important shock absorber for farmers and their financial partners, [but] it is not sufficient to protect farmers, lenders or the broader agricultural economy from climate risk.”

Crop yield: 

Will enough product be produced to earn back operating expenses? Crop yields can vary widely in times of water stress, and farmers may be forced to let fields go fallow or plant less water-intensive crops, which can hurt their bottom line. As the National Science Foundation explains, “As water shortages and higher temperatures drive down crop yields in regions that depend heavily on seasonal snow for water, the choice to use more drought-tolerant crop varieties comes at a cost.”

Environmental risks: 

Many of these risk factors are linked to water availability, which can determine whether or not a farming operation is viable in the long term. Borrowers who are low-risk now could become high-risk if changing weather patterns or regional regulations limit their ability to pump, store, or transfer water.

The loan decisioning process should be informed by data for each parcel of land, but also by a financial institution’s overall risk tolerance and their existing loan portfolio. If too many loans are concentrated in a high-risk water district, then a lender may not want to take on another risky investment before diversifying their portfolio.

How Geospatial Tools Can Improve Loan Decisioning

Geospatial tools allow lenders to aggregate and analyze all of this data in one place and discover insights that would otherwise go unnoticed. By presenting the risk factors listed above in a geospatial format, a financial institution’s own internal data begins to make more sense.

Adding layers of risk insight to financial data is the future of water and climate risk analytics, which can provide context to loan decisions and be used to reduce portfolio risk. 

GIS platforms also allow for more data transparency, helping financial institutions comply with ESG reporting requirements and disclose climate risks to regulators and investors. Banks and lenders who take the initiative to incorporate geospatial data into their loan decisioning process now will be ahead of the curve and better able to navigate the transition risks associated with climate change.

Financial institutions can incorporate publicly available data, such as that released by the Federal Financial Institutions Examination Council (FFIEC), as well as proprietary data collected by drones, field sensors, and borrowers themselves. This will help ag lenders make faster, better loan decisions, and keep the agricultural sector stable in times of financial volatility and increased water stress.

The Bottom Line

Loan decisioning is a multi-stage process that is increasingly being automated in order to improve efficiency and reduce portfolio risk. But in order to make the right decisions, ag finance professionals should incorporate GIS tools that can better illuminate the dynamic, geospatial nature of risk and unlock their data.

AQUAOSO’s tools are uniquely suited to empower informed loan decisioning in the agricultural sector. GIS Connect is a map-based SaaS platform that helps ag professionals integrate third-party data sets using bank-grade security and a secure API. Plus, the Water Security Platform allows lenders to monitor water risk on an ongoing basis, factoring it into individual loan decisions and their overall lending strategy.

Contact the team at AQUAOSO to request a demo, or sign up for the newsletter to stay informed about this topic and other water security issues.

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