What Every Bank Should Know About AI in Lending

Artificial intelligence (AI) and automation are transforming commercial lending at a rapid pace. From application intake to credit decisioning and servicing, modern platforms are using machine learning, workflow automation and real-time data to streamline loan operations. For banks navigating competitive pressures and evolving borrower expectations, the benefits are clear: faster turnaround times, greater consistency in underwriting and better customer experiences.

But these efficiencies come with new risks — ones that don’t always show up on a balance sheet. The reality is simple. The faster the system moves, the more important it becomes for the board to understand how it works.

Oversight in the Age of Intelligent Lending
Legacy loan origination systems (LOS) were largely linear — rule-based, manual and built around internal policies. But today’s lending platforms are dynamic and data-driven, relying on algorithms to analyze borrower data, calculate risk scores and trigger downstream actions in seconds. The shift has fundamentally changed how decisions are made, and how they should be governed.

As automation takes on more responsibility, banks must ensure accountability keeps pace. Without strong oversight, institutions risk relying on models they can’t fully explain, losing control over compliance obligations, or inadvertently introducing bias into decision-making.

This is no longer a hypothetical concern. Regulators are signaling increasing scrutiny over third-party risk, data use and explainability in credit decisions — especially when AI is involved.

Where Automation Is Gaining Ground
Modern lending platforms are applying automation across the full lifecycle, including:

• Origination. AI supports prequalification, fraud detection and intelligent document collection, reducing friction for borrowers and staff alike, often through AI assistants that guide applicants and help with forms and document uploads.
• Decisioning. Credit models are being enhanced by machine learning, which identifies patterns beyond traditional scorecards. These tools can flag risk, recommend terms or render approvals based on institutional parameters, with some leveraging generative AI to help draft underwriting narratives or suggest loan structures.
• Servicing. Automation helps monitor portfolio performance, flag exceptions and initiate follow-ups, leveraging AI-powered analytics to identify risk concentrations and predict potential defaults.

For banks, this creates clear value and an imperative. Boards should understand both what the platform can do and how it’s making decisions.

Key Oversight Questions for Directors
Boards need to ask the right questions and ensure governance frameworks are in place. Questions to ask include:

1. Is the AI explainable and audit-ready?
Whether a loan is declined or priced differently, banks must be able to explain the logic behind the decision. This is especially critical under the Equal Credit Opportunity Act and fair lending standards. Boards should ensure the bank’s platforms can produce clear, regulator-ready documentation, not just opaque model outputs. This often involves unifying and clearly presenting the AI-driven aspects of different credit scores and highlighting the specific factors that influence the AI’s assessment in a single view.

2. How is bias being tested and mitigated?
AI models can unintentionally reinforce bias if trained on incomplete or skewed datasets. Directors should understand how fairness is being evaluated at launch and on an ongoing basis.

3. How do platforms align with our credit policy and risk appetite?
Technology should reinforce policy. Boards should confirm that decisioning engines are aligned with the bank’s defined credit parameters, and that overrides or escalations are tracked and governed.

4. Are servicing and risk functions integrated with origination data?
One of the strategic advantages of modern lending platforms is the ability to connect origination data with servicing activity, creating a full borrower lifecycle view, often presented through AI-powered dashboards — aggregating data from application, underwriting, pricing, closing and servicing stages. Directors should ask whether servicing workflows are benefiting from the same intelligence applied at the front end.

5. Who owns the technology and the outcome?
Vet third-party platforms for both technical capabilities and for their alignment with the bank’s compliance and risk expectations. Directors should expect clarity on who is responsible for performance, data protection and issue remediation, especially when vendors manage critical decisioning functions.

Technology as Strategy, Not Just Infrastructure
The pace of change in AI and automation is accelerating. For banks to compete effectively and govern responsibly, boards must elevate lending technology from a line-item expense to a core strategic asset. That means viewing platforms not as tools to process loans faster, but as systems that shape risk, reputation and customer trust.

With the right oversight, automation in lending can drive growth without sacrificing accountability. But it starts at the top with the board asking the right questions and receiving clear answers.