Risk is Life: Static Models Are Risky, But Life Follows Patterns

(This article written by Khrystyna Voloshyn and Scott Nelson was published in MonitorDaily on January 9, 2026)

Static credit models freeze risk in time, but Scott Nelson and Khrystyna Voloshyn argue that profitable equipment finance comes from tracking how borrowers adapt in herds, spotting behavioral patterns early and managing risk as the living, shifting force it really is.

Danny Rojas’ exuberant declaration, “Fútbol is life!” from the Ted Lasso series, could be a theatrical metaphor for the leaders of independent equipment finance companies. “Risk is life!” would be their phrase, but the smiles, passion and enthusiasm would be the same. Small and mid-sized independent lessors are as entrepreneurial, optimistic and creative in how they play the equipment finance game as Rojas is playing fútbol. They play hard underwriting more difficult credits and finding the right risk appetite matches with their capital partners for the niche market segments they serve. But the metaphor has more than just entertainment value; there is insight for managing, modeling and monetizing the risks these companies embrace in their pursuit of profitable growth. “Risk is life,” and life requires special methods to predict what might happen.

It has been said so many times now that even Grok AI returns human behavior as the principal risk in equipment finance:

“The principal risk in equipment finance (also called equipment leasing or asset-based lending) is credit risk — the risk that the borrower (lessee or debtor) will default on their payment obligations.” – Grok AI

For the underwriter, the question “Will this borrower deliver on their promise and pay their bills?” is intrinsically organic, one that changes and even grows over time as the decision context evolves. Risk in equipment finance is more a life form than a mechanical process or mathematical model. Life forms and organic systems are nonlinear, adaptive, contextual and constantly changing. As a result, the challenge for lenders is that traditional credit models attempt to freeze this living, adaptive behavior into static numbers and models: a score, a ratio or a Probability of Default estimate. But the numbers age quickly, and the models fail to adapt as the ecosystem shifts as it always does.

During COVID, consumer credit scores, for example, counterintuitively rose alongside delinquencies after the end of quantitative easing reduced payment capabilities. This wasn’t a mystery; it was a result of static models failing in a changing ecosystem. Excess liquidity from the easing changed borrowing behavior in ways that artificially nudged scores upward. When the stimulus ended, delinquency returned quickly and was magnified by those still making credit decisions based on the artificially higher scores. Traditional credit models captured the wrong behavior at the wrong time.

Data from the “Herd” Reveals Behavior Patterns of Success

The problem with predicting an individual borrower’s behavior isn’t a lack of data, it’s using incomplete data and static analysis that measures what happened rather than how borrowers responded to challenges that life presented along the journey of the contract. Herein lies the opportunity. When presented with challenges — both new and familiar — life forms will group, form herds so to speak and follow behavioral patterns learned over time, sometimes over generations, to make sure the herd survives.

Predicting the behavior of individuals is hard, nearly impossible, as any one animal moving with the herd can stray widely as the herd migrates smoothly and deliberately from the summer birthing grounds to winter stay overs. But data from the herd averages out noisy individual decisions, especially bad decisions and erratic behavior, revealing behavioral patterns well defined by statistical models that make predicting who will succeed much easier.

Credit teams understand and assess the risk of “herds” via concentration analysis. An industry concentration is basically a herd, and if that herd has encountered a new challenge, e.g., COVID, and is heading the wrong direction, e.g., increasing delinquency or bankruptcy, that concentration becomes a risk for those who depend on that herd. But herd data and dynamic analysis of movements within the herd can identify behavior patterns of the successful.

Behavior Patterns Identify Winners and Losers

The COVID lockdowns in 2020 drove default rates on restaurant equipment (NAICS 722) from < 2% to more than 28% in 90 days. Similarly, during the post-COVID ecommerce freight recession from 2023 to 2025 long-haul tractor assets owned by NAICS 484 Truck Transportation carriers saw residual values crash and load demands became erratic. NAICS 484 companies struggled.

But not all restaurants suffered equally during the COVID lockdowns, and not all transportation fleets struggled after quantitative easing ended and e-commerce reset. Indeed, a post-game analysis of industry data shows how some members of each herd survived, both their characteristics and their behavior patterns, and some members thrived through acquisition and share capture.

Restaurants that avoided bankruptcy during COVID lockdowns had several common traits:

• Chains or larger scale: Better access to capital and diversified locations/jurisdictions.
• Strong cash reserves & low debt:  Past financial performance covered fixed costs when lockdowns drove revenue to zero.
• Delivery/takeout-ready model: Pre-existing diversification with online ordering, drive-thru or packaging-friendly menus enabled compliant pivots.
• Suburban and non-center city geographic deployment: Access to outdoor dining, parking and less severe restrictions eliminated concentration risk.

Similarly, freight transport (NAICS 484) firms that avoided bankruptcy in the 2023-2025 “Great Freight Recession” (overcapacity, low rates and soft demand) also had common traits:

• Larger scale or national chains: Access to capital, acquisitions (e.g., Knight-Swift buying U.S. Xpress) and market share gains post-exit to capture share.
• Strong pre-recession finances: Cash reserves held from 2021 boom protected fixed costs and low debt buffered rate drops to ~$2.40/mile.
• Diversified operations: Mix of contracts (70%+ stable loads), LTL, intermodal, e-commerce, or cross-border focus hedged against concentrations in market segments most negatively impacted, e.g., local day delivery as e-commerce softened.

These characteristics show where the members of the herd all started. But dynamic analysis reveals behavior patterns wherein survivors and winners took advantage of preparation, luck and learning when the lockdowns waned:

Successful restaurants:

• Leveraged high pre-pandemic ratings and loyal customers to maintain demand through the lockdowns with a variety of delivery and menu choices that customers preferred.
• Adapted quickly by implementing contactless tech, streamlined menus, ghost kitchens and promotions to maintain customer contact.
• Used government aid (PPP loans, EIDL, Restaurant Revitalization Funds) effectively and efficiently to bridge operational gaps and invest in the “new norm.”
• Led by experienced owners with both time in business and diversity in operational backgrounds, e.g., fast food, counter-serve, sit-down service, that made smarter, faster pivots as both regulations and customer preferences changed.

Similarly, the successful freight transportation operators followed common behavior patterns that took advantage of their preparation and luck:

• Used tight cost controls and operating efficiency to implement fleet right-sizing, tech for routing/fuel savings, and driver retention amid shortages that kept cash flowing to make payments on assets.
• Leveraged technology and innovation like AI/telematics for optimization and load matching to boost utilization, enabling faster response to both economic and geographic market demand changes.
• Leveraged strong balance sheets for strategic M&A/consolidation to capture market share and distressed assets for growth, e.g., J.B. Hunt, Old Dominion.
• Used industry experience to make quick pivots to resilient segments/lanes e.g., nearshoring (Mexico) and short hauls, while avoiding over-saturated spot markets to maintain and grow revenue.

Identifying Characteristics and Behavior Patterns of Success

Note the similarities in both company characteristics: size, financial strength, leadership experience, operational diversification and customer focus; and the behavior patterns: quick operational adaptation, technology-enabled innovation, market/customer pivots across the two very different industries/herds. Successful behavior patterns can be both general and specific to a herd, so data and analysis must be planned accordingly.

The right data is necessary, but not sufficient. Static analysis misses behavior patterns because it will:

• Rely on past outcomes rather than forward behavior
• Assume stability in environments that are inherently unstable
• Compress complexity into one or two summary metrics
• Recalibrate too slowly to detect regime shifts
• Fail to capture the adaptive responses that differentiate survivors from failures

More dynamic analysis needed to individuals following successful behavior patterns:

What This Means for Lenders 

“Risk is life” is more than a call to play in equipment finance; it describes the reality of risk. When the risk is behavioral, as it is in equipment finance, risk management must adapt and learn. Risk managers must update their data and analysis to examine herd behavior and identify behavioral patterns. Herds adapt and survive the challenges of the ecosystem and members of the herd survive by following the patterns of those who adapt best to the challenge.

Properly designed dynamic data analysis of a “herd” can provide a major advantage by shifting from “static borrower analysis” to “successful behavior pattern detection”:

• Identify the behavioral indicators of survival early
• Use herd-level signals to guide pricing, exposure and portfolio strategy
• Recognize when a herd is heading into danger (e.g., restaurants in COVID)
• Identify the members of that herd most likely to adapt and survive
• Reallocate capital based on dynamic, not static, patterns

This is how lenders can model and manage risk as it truly behaves as an adaptive, organic process. The great thing about herd data is that it is vast and statistically strong. Similarly, the great thing about behavior patterns is that they have defined progressions that enable the risk manager to predict and influence endpoints.

Danny Rojas was the Ted Lasso lesson in how playing the game with enthusiasm, energy and optimism is both a fun and winning strategy. When lenders focus on behavioral patterns, their enthusiasm and optimism engaging risk will be rewarded with a better understanding that survival is not random; it follows recognizable trails. The key is shifting from predicting individual borrowers to predicting the herd behaviors that lead some borrowers to succeed while others perish.