Jul 15, 2025
The Credit Score Got Smarter. Can Agriculture?
As of July 8, 2025, the Federal Housing Finance Agency officially approved VantageScore 4.0 for use in loans sold to Fannie Mae and Freddie Mac. It’s a historic move: the first time a non-FICO credit scoring model has been sanctioned for the backbone of the U.S. mortgage market.
The headlines are right to call it a win for inclusion. VantageScore estimates that over 30 million people once deemed “unscorable” by traditional models could now gain visibility. But this shift isn’t just about access to scores. Changing the inputs means rethinking the architecture of judgment, not just the output.
Finance is rewriting its gatekeeping code. Agriculture should be next.
From Judgment to Models: A Short History of Credit Scoring
Before the 1980s, lending decisions were more art than science. Bank officers made case-by-case judgments based on inconsistent data, personal relationships, or vague heuristics. The process was inefficient, opaque, and vulnerable to bias.
That changed with the rise of Fair, Isaac and Company. Founded in 1956 by engineer William Fair and mathematician Earl Isaac, the firm’s early work applied statistical modeling to business risk. Their breakthrough came in 1989, when they introduced the first general-purpose consumer credit score—what would become the FICO Score.
It was simple, standardized, and data driven. Using a borrower’s credit history—including payment timeliness, total debt, credit mix, and new inquiries—it distilled complex behavior into a single risk score. The effect was transformative. By the late 1990s, FICO had become the de facto tool for underwriting mortgages, car loans, credit cards, and beyond.
Its rise paralleled the growth of mass consumer finance in the U.S. The score made it easier to scale risk decisions, bundle loans, and build large portfolios with predictable performance. FICO didn’t just quantify credit risk—it restructured the entire financial system around what it could measure.
The Problem of the Thin File
But FICO’s power came with limitations. Because it relied primarily on traditional credit activity—credit cards, installment loans, mortgages—it couldn’t score people who didn’t use those products. This excluded a wide swath of the population: the underbanked, recent immigrants, gig workers, younger people, and millions of low-income households.
Not risky. Just invisible.
Over the past two decades, fintech innovators began probing this blind spot. Microlending platforms, Buy Now Pay Later models, and digital wallets—particularly in emerging markets—developed alternative risk signals from mobile phone data, rent payments, e-commerce history, and even social networks. In countries like Brazil, Kenya, India, and Indonesia, these models became the foundation for first-time access to credit, allowing individuals with no formal history to build a financial footprint.
In the U.S., startups like Petal and Upstart pushed a similar approach. By layering in nontraditional data sources—bank account activity, cash flow, utility bills, employment history—they offered credit access to people long locked out by FICO’s narrow lens.
This wave of inclusion wasn’t just social policy. It was a better prediction strategy.
Enter VantageScore 4.0
The VantageScore model, launched jointly by the three major credit bureaus in 2006, was built to challenge the FICO monopoly. But only with Version 4.0 did it move decisively beyond the incumbent logic.
VantageScore 4.0 integrates trended data (showing how behavior changes over time), expands access to traditionally “unscorable” consumers, and uses machine learning techniques to better calibrate risk across population groups. Its predictive performance exceeds FICO’s in many cases, especially for consumers with sparse files.
With the recent Federal Housing Finance Agency (FHFA) ruling, the new model isn’t just a competitor. It’s now part of the mortgage machinery. Institutional acceptance has arrived.
The message to markets is clear: broader data leads to better scores. And better scores unlock both inclusion and precision.
Agriculture Has a Scoring Problem, Too
The agricultural analog to FICO is the actuarial underwriting table.
In the U.S., crop insurance and ag credit are largely built on fixed models—historical yield data, multi-year production records, static zoning. These inputs make sense for corn in Iowa or soy in Illinois. But what about almonds in California’s Central Valley? Regenerative livestock-and-vegetable systems in Kentucky? Mixed-use farms in tribal lands? Or urban market gardens in Detroit?
Many of these producers are effectively “thin file.” Not because they lack insight, but because the system doesn’t read their signals.
Just like with FICO, if your data doesn’t match what the system is built to read—like standardized yield histories from corn or soy—it doesn’t really see you. Or worse, it offers coverage so misaligned it may as well not exist.
A Decade of Data Growth
Yet over the past 10–15 years, the informational landscape in agriculture has exploded. Remote sensing, IoT sensors, satellite analytics, digital agronomy platforms, carbon monitoring tools, water stress indices, pest risk maps, farm management software—the breadth and resolution of data is unprecedented.
But despite this surge, most agricultural risk frameworks remain frozen in outdated logic. They might use a weather index or satellite NDVI for a specific insurance product, but they don’t incorporate a layered, multi-dimensional risk profile the way fintech models now do.
Just as VantageScore challenged the narrow definition of creditworthiness, agriculture needs to challenge the outdated proxies it still leans on—yield history, acreage size, standard deviation of revenue—as the primary gatekeepers.
Scoring the Full Farm
Imagine a farm risk score that integrates:
Five-year precipitation variability
Forward-looking climate exposure
Local market infrastructure
Supply chain chokepoints
Soil degradation trends
Adoption of regenerative or climate-smart practices
Proximity to labor pools or transport corridors
Disease or pest exposure risk
Input volatility
Historical payment behavior to suppliers or co-ops
This isn’t science fiction. Every one of these inputs is now technically feasible. While data quality and coverage still vary, the real challenge is no longer availability—it’s model construction and institutional adoption.
The Vantage Lesson
What VantageScore 4.0 reveals is that the scoring model is not a neutral tool. It defines access. It determines who gets seen, funded, and supported. And once embedded in institutional systems, it shapes the very structure of the market.
This is why the farm finance world should be paying attention. Risk scoring isn’t just about better underwriting—or shaving a few points off the loss ratio in corn and soy. It’s about systemic design.
An inclusive farm risk score isn’t charity. It’s a smarter modeling approach that makes the system more resilient, more precise, and more aligned with the realities of farming today.
From Mortgage to Farm
Inclusion in credit didn’t come from tweaking FICO. It came from building a better model—and then fighting for its adoption. That same playbook now needs to be applied in agriculture.
Because if we want a future where regenerative farms, smallholders, specialty growers, and climate-vulnerable producers can access insurance and finance on fair terms, we can’t just subsidize the old system. We have to upgrade the signal—what data is captured, how it’s interpreted, and what it’s allowed to influence—especially in how risk is scored, insurance is priced, and finance is allocated.
And as the VantageScore breakthrough shows, sometimes all it takes to rewrite a system… is to redefine what we measure.
Roberta Leão, | Layer 1 Agriculture
