The commercial equipment finance industry entered 2026 with cautious optimism. Demand fundamentals remain intact, supported by continued capital expenditure, infrastructure investment, and the ongoing buildout of AI-related capacity. However, beneath this growth narrative lies a more complex and shifting risk environment—one that requires a sharper, more adaptive risk management framework.
From my vantage point as Chief Risk Officer, the challenge is not simply identifying risk, but understanding how multiple forces—macroeconomic pressure, technological disruption, and behavioral shifts in borrowers—are converging in new and sometimes unpredictable ways.
Current State of Risk: Stable, But Underlying Stress is Building
At a headline level, credit performance in equipment finance remains relatively stable. Declining interest rates and continued investment activity are supporting originations and portfolio growth. However, several leading indicators suggest growing pressure beneath the surface:
- Private credit stress is rising: Market participants are increasingly concerned about weakening underwriting standards and higher-than-expected losses in leveraged lending.
- Refinancing risk is increasing: Higher borrowing costs and tighter capital markets—particularly in technology sectors—are contributing to elevated default expectations.
- Opacity in non-bank lending is making risk harder to monitor, especially as exposure spreads across insurers and asset managers rather than traditional banks.
In short, equipment finance providers would be well advised to be vigilant regarding delinquencies and losses—lagging indicators that may not reveal forward risk.
Borrower Behavior: The Growing Impact of Consumer and SME Leverage
One of the more subtle but important trends is the increased reliance on consumer revolving debt and short-term liquidity solutions, particularly among small and mid-sized businesses. While not always directly visible in commercial underwriting, these pressures show up in reduced borrower resilience to revenue volatility, increased payment prioritization risk, and greater dependence on refinancing rather than amortization.
This creates a correlation risk across portfolios that may appear diversified by industry or asset class, but are increasingly linked through borrower balance sheet stress.
From a risk management standpoint, this reinforces the need to incorporate cash flow-based underwriting enhancements, monitor external credit signals beyond traditional credit bureaus, and stress test portfolios for liquidity-driven default scenarios, not just asset performance.
Fraud: A Structural, Not Cyclical, Risk
Fraud has evolved from an episodic loss driver into a persistent, structural risk—and one that is accelerating. Fraud losses can reach ~5% of annual revenue for organizations in extreme cases, according to a report from the Association of Certified Fraud Examiners.
Recent data shows that fraud tactics are becoming more sophisticated, including AI-generated invoices, identity spoofing, and deepfake-enabled social engineering. Coordinated attacks using real identity data are also increasing across lending markets.
This is particularly relevant in equipment finance, where vendor relationships can be exploited, documentation-heavy processes create opportunities for manipulation, and speed-to-decision pressures may weaken controls
The key takeaway from these situations is to embed fraud risk into core credit processes.
AI in Risk Management: Opportunity and Exposure
AI is rapidly reshaping underwriting, portfolio monitoring, and collections. The benefits are real and measurable, including faster credit decisions and reduced operational cost, improved fraud detection and anomaly identification, and enhanced portfolio analytics and early warning systems.
However, AI introduces its own set of risks:
- Model risk and explainability challenges
- Bias and fairness concerns in automated decisioning
- Adversarial threats, where fraudsters exploit weaknesses in AI systems
- Overreliance on automation, reducing human judgment at critical points
Additionally, AI is a dual-use technology—the same tools enhancing detection are also enabling more sophisticated fraud.
The recommended approach is a combination of AI and human oversight that includes active management, validating models and auditing.
Strategic Priorities for Risk Leaders
Given this environment, the role of the Chief Risk Officer is evolving from gatekeeper to strategic enabler. The following areas are top priorities:
- Dynamic Credit Risk Frameworks - Static underwriting models are no longer sufficient. Risk teams must incorporate real-time data feeds, forward-looking macro indicators, and scenario-based stress testing.
- Integrated Fraud and Credit Risk Management - Fraud and credit losses are increasingly intertwined. Leading organizations are embedding fraud detection into underwriting workflows, using identity and behavioral analytics at origination, and continuously monitoring counterparties post-funding
- AI Governance and Risk Controls - As AI adoption accelerates, so must governance. Best practices include model validation and monitoring frameworks, clear accountability for AI-driven decisions, and regulatory alignment and documentation.
- Portfolio Transparency and Early Warning Systems - The ability to identify emerging risk early is a competitive advantage realized through enhanced portfolio segmentation, industry and obligor-level monitoring, and trigger-based intervention strategies.
Looking Ahead: A More Complex—but More Manageable—Risk Environment
The equipment finance industry is not facing a single systemic threat, but rather a convergence of smaller, interconnected risks—credit normalization, borrower leverage, fraud evolution, and technological disruption.
At the same time, the industry has more tools than ever to manage these risks effectively, including using AI, data analytics and improved risk infrastructure.
For risk leaders, the mandate is clear: anticipate, adapt, and integrate. Because in today’s environment, the biggest risk is not what we see—it’s what we assume will behave the way it always has.
