How ONCI models AI risk at the borrower level

Most discussions about AI and credit risk focus on technology adoption. That’s not where the real risk sits.

The challenge for banks isn’t knowing which borrowers are using AI. The challenge is understanding how AI-driven shifts in cost structures, pricing dynamics, and competitive pressure will change borrower performance before the financials reflect it.

ONCI models AI risk at the borrower level by connecting industry-level AI exposure with forward-looking borrower forecasts. Not by guessing who is adopting AI faster, but by measuring how AI is reshaping the economic environment each borrower operates within.

Start with industry-level AI exposure

AI does not affect all industries equally. Some face high automation potential. Others face substitution risk, pricing compression, or long-term commoditization. These forces emerge at the industry level and operate independently of individual borrower initiatives.

ONCI’s AI Impact framework evaluates industries based on core AI exposure levers, including:

• Automation potential across core workflows
• Client substitution risk driven by AI-enabled alternatives
• Pricing pressure as AI lowers barriers to entry
• Long-term growth constraints tied to commoditization

This creates a consistent, defensible view of how AI is likely to reshape revenue durability, margins, and competitive dynamics across industries over the next 12 to 24 months.

Importantly, this step avoids subjective assumptions about borrower behavior. The model does not attempt to infer internal AI strategy or adoption maturity. It measures the external forces acting on the industry.

Overlay AI exposure on borrower financials

Industry exposure alone doesn’t determine risk. Financial position determines how well a borrower can absorb change.

ONCI overlays industry-level AI exposure onto borrower financials to assess how those forces may translate into performance outcomes. Two borrowers in the same industry can face different outlooks, not because one is “better at AI,” but because their balance sheets, margins, and cash flow profiles differ.

This approach allows banks to see:

• Which borrowers have financial flexibility to withstand margin pressure
• Which borrowers are more vulnerable to cost or pricing shocks
• Where AI-driven industry stress is most likely to surface first

The differentiation comes from financial resilience, not assumptions about technology adoption.

Translate exposure into forward-looking forecasts

AI risk rarely shows up as a sudden event. It shows up as gradual pressure that compounds over time.

ONCI ties AI exposure directly into forward-looking forecasts across key credit metrics, including:

• DSCR
• Leverage
• Revenue trajectories
• Free cash flow
  
  

This is where AI risk becomes visible. Forecasts surface deterioration months before it appears in reported financials, long before traditional triggers are breached.

For executives and risk committees, this shifts the conversation from reacting to historical outcomes to anticipating directional change.

Consistent insight, not speculation

A common mistake in AI risk analysis is overconfidence, trying to predict which borrowers will “win” based on perceived innovation or management strategy. ONCI avoids that trap.

The framework is intentionally conservative. It does not rely on borrower-level AI initiatives, disclosures, or assumptions. It relies on measurable industry forces and observable financial conditions.

That consistency is what makes the output usable at scale, across portfolios, sectors, and cycles.

Why this matters for executive decision-making

For leadership teams, AI risk is not a technology question. It is a timing and visibility problem.

Without forward-looking insight, banks will continue to see:

• Deterioration identified late
• Reactive downgrades
• Compressed decision windows
• Capital allocated after risk has already shifted
  
  

By modeling AI risk through industry exposure and borrower forecasts, ONCI gives executives early visibility into where performance is likely to change, and where attention is most needed.

The goal isn’t to predict the future with certainty. It’s to remove surprise from the next credit cycle.

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