December 4, 2025
The risk is not just the rise of AI. The risk is that banks don’t have a way to measure how AI will impact borrower performance over the next 12 to 24 months. AI driven disruption shows up in forecasts first, not historicals. AI risk won’t show up in historicals—because the disruption happens ahead of the data—so unless you’re looking forward, you’ll be hit by deterioration with no warning.
This article explains why AI exposure matters, why it won’t impact every borrower the same, and how banks can protect their portfolios before the next wave of surprises.
Every lender knows that financial statements are backward looking. They show you where a borrower has been, not where they are going. In a stable economy, that lag is manageable. In an AI driven economy, that lag becomes dangerous.
AI adoption can reshape a borrower’s cost structure, margin profile, or competitive position months before those shifts appear in reported numbers. A borrower can look stable on paper while the ground is moving under them.
This creates the first major problem. Risk begins forming long before your current models can detect it.
AI won’t reshape the economy evenly. Each industry absorbs AI through its own mix of automation potential, substitution risk, pricing pressure, regulatory friction, and growth constraints. Our AI Impact Assessment models these industry-level levers to show where disruption is most likely to accelerate.
This avoids the blunt assumption that AI raises or lowers risk across an entire portfolio. Instead, it pinpoints which industries face rising competitive pressure, which have expanding margin potential, and which may experience pricing compression or commoditization over the next 2-5 years.
When that industry exposure is overlaid on borrower financials, lenders gain a far clearer view of which borrowers are more resilient and which are more vulnerable—not because of their internal AI strategies, but because of the external forces acting on their industry.
Most credit processes are built around point-in-time financials, annual reviews, and historical trends. But AI does not wait for review cycles. It does not follow reporting schedules. It accelerates the pace of change.
Borrower deterioration will show up in forecasts long before it appears in the financials.
By the time DSCR slips.
By the time leverage rises.
By the time cash flow weakens.
The underlying issue has already been taking shape.
Backward looking models can’t protect banks from forward moving risk.
AI doesn’t transform all companies in an industry at the same speed, it’s how industry-level AI levers interact with each borrower’s current financial position. That’s where surprises emerge.
Industries with high automation potential, substitution risk, and pricing pressure will see faster performance divergence. Borrowers already operating with tight margins or weak cash flow will feel the strain earlier, while stronger borrowers may benefit from the same industry shift.
What credit teams should expect:
You don’t need insight into each borrower’s internal AI adoption. Industry-level AI exposure—combined with up-to-date financials—already signals where performance may strengthen or deteriorate. That forward visibility is what prevents unexpected surprises.
Core AI Risk Levers:
Without aligning forecasts to these industry-level AI forces, the next 12–24 months will feel volatile. With them, banks can see the shift before it hits the financials.
AI adoption is accelerating across enterprise, manufacturing, logistics, professional services, and consumer sectors. The next wave of credit losses won’t come from recession alone, it will come from borrowers who can’t adapt to AI driven dynamics.
Banks that rely on historicals will see:
More unexpected downgrades
More late identified deterioration
More strained review cycles
More urgent decisions with incomplete visibility
In a world that is changing this quickly, risk can’t remain reactive.
To manage credit effectively in an AI influenced economy, banks need three capabilities.
1. Borrower level AI exposure scoring
Not sector level. Borrower specific. A measurement of how AI is likely to impact cost structure, margin durability, or competitive pressure.
2. Forecasting that incorporates AI exposure
Risk becomes visible only when AI exposure is tied to forward looking metrics like DSCR, leverage, revenue, and free cash flow.
3. Alerts and workflow paths that translate forecast signals into action
Seeing the change is not enough. Credit teams need a reliable way to prioritize reviews, escalate emerging issues, and adjust exposure while there is still time.
This turns AI disruption from a threat into something measurable and controllable.
ONCI is the first platform that connects borrower level AI exposure with forward financial forecasting and credit action pathways.
ONCI helps banks:
See borrower specific AI risk before financials move
Forecast the impact across DSCR, leverage, and profitability
Identify which borrowers will strengthen and which will weaken
Prioritize reviews based on forward signals
Take action with clear, consistent workflows
In short ONCI shows where risk is heading, not where it used to be.
AI will define the next credit cycle. Some borrowers will emerge stronger. Some will slip faster.
The banks that see the shift early will stay ahead of losses, allocate capital intelligently, and support the right clients at the right time. The banks that rely on backward looking models will feel the impact.
If you want visibility into how AI will reshape your borrowers, the time to prepare is now.
ONCI
445 Park Avenue
Floor 6
New York, NY 10022
