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AI Portfolio Risk & CECL Loan Review and Due Diligence

A guide to modernizing loan review

Kent Kirby
April 24, 2026
0 min read

This article covers these key topics: 

Why and how the loan review function needs an update 

Modernizing loan review is more than adding technology to existing bank and credit union processes. Improved credit risk management requires a better workflow and oversight. This guide is an overview of what to do and how to do it.

The case for modernizing loan review

When I started in loan review, the toolkit was simple: an eleven-column ledger, a yellow pad, an HP12C, and a .7mm pencil. Data moved manually from one piece of paper to another, with all the limitations that implies.

Yellow legal pad on deskToday’s tools are unrecognizable: systems, dashboards, and more data than we know what to do with. But in many ways, the mindset hasn’t kept pace. We’ve upgraded the tools, without really changing how we think.

The biggest obstacle is the most familiar: “The way we’ve always done it.” That mindset shows up in three places: schedules, thresholds, and spreadsheets.

You may like this webinar: Building the case for loan review software's ROI

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Schedules drive reviews based on fixed cycles rather than where risk is actually emerging. Thresholds become targets, tempting teams to revisit the same large, well-known credits instead of where risk truly lies. This is the equivalent of searching under the streetlight because it’s easier to see. And spreadsheets, while an improvement over paper, remain limiting. They require constant manipulation, obscure trends, and isolate data into disconnected “islands.”

All of this is happening as portfolios grow more complex. Products once reserved for large institutions—trade finance, FX, derivatives, for example—are now commonplace. Private equity ownership of your larger customers is more routine (and will only grow as the Boomers retire and business ownership transfers). Meanwhile, data remains fragmented across multiple systems with inconsistent governance. We still cling to the idea of a single source of truth while ignoring the reality of multiple, unmanaged sources.

This combination of outdated mindset and increasing complexity is no longer sustainable. Financial institutions need modern loan review workflow and reporting. Technology, specifically artificial intelligence (AI), can help, and institutions can get started using the roadmap below.

Modernizing the credit risk review workflow

The debate between continuous monitoring and point-in-time review of credit is settled: you need both.

Continuous monitoring identifies emerging risk across the credit portfolio. Point-in-time reviews provide depth once those risks are identified. The bridge between the two is a clear set of triggers, such as (not exhaustive):

  • Risk rating migration
  • Loan covenant breaches or near misses
  • Deteriorating financial trends
  • Payment behavior and line utilization
  • Concentration growth
  • Industry stress

Most of this data already exists. The issue is usability. And that starts with data quality.

Loan review too often “plays the hand it’s dealt” instead of challenging bad data.  But if data is missing or unreliable, it is itself a risk—and should be escalated as such.

From there, workflow needs to be reexamined with a simple question: Why are we doing this? If the only answer is tradition, it’s time to stop.

At a strategic level:

  • Replace rigid schedules with risk-driven cadence
  • Prioritize forward-looking indicators (hint: see the above examples)
  • Focus on meaningful issues, not scorecards
  • Identify patterns and root causes across the portfolio (not limited to any given review)
  • Ensure recommendations lead to real remediation

Once that work is done, it’s time for loan review technology to enter the picture.

Start with data. Identify a small set of critical elements (risk rating, collateral code, call code, NAICS, etc.), ensure they are granular and accurate for the majority of exposure, and governed going forward.

Next, use the analytical tools already in the institution. Independence does not require isolation. Use what’s available—aggressively.

Finally, automate—but only after redesigning the process. Automating a flawed workflow just accelerates mediocrity. Done correctly, automation should reduce redundancy, retain prior review insights, and keep teams focused on risk rather than on process.

Modern reporting provides actionable oversight

Most loan review reports I’ve read over the years weren’t useful. Many ended up in the trash. They might have identified issues, but they didn’t drive action.

That has to change.

  • Keep reports concise—3 to 5 pages. Use appendices for detail.
  • Focus on effective challenge, not passive assurance
  • Highlight emerging risks and portfolio-level patterns
  • Prioritize adequacy of policies—not just adherence

It’s easier to check compliance than to question whether the rules themselves make sense. But history shows that poorly designed frameworks—not just poor execution—cause failure. Everyone can follow the rules and still head off a cliff.

Loan review’s role is to call that out.

A loan-review specific approach to artificial intelligence

AI is moving fast. The right approach for financial institutions is neither avoidance nor overreaction—it’s discipline. Start small and build.

  1. Eliminate low-value work
    AI in loan review excels at summarizing documents, reviewing agreements, and accelerating routine analysis. What used to take hours can take minutes.
  2. Enhance individual reviews
    AI can scan large document sets and highlight patterns quickly—often ones you might miss.
  3. Improve portfolio risk assessment
    It helps identify “gray rhinos” (risks hiding in plain sight) and refine loan review scoping decisions.
  4. Integrate across the process
    AI won’t replace human judgment, but it can significantly improve speed and consistency.

One caution: AI is only as good as the question. Poor prompts yield poor answers. Always require it to show its work. Treat it as a tool, not a conclusion.

Measuring success of modernized loan review

Transformation needs a roadmap. Here’s an idea for one to help loan review:

Phase 1: Data governance and segmentation
Define critical data elements and achieve meaningful coverage (not perfection). Build out from there.

Phase 2: Workflow redesign and monitoring
Rebuild the process from first principles—with stakeholder input. Then implement, using automation to turbocharge the effort.

Phase 3: Advanced analytics and AI
Incorporate AI iteratively, aligned with institutional readiness.

Phase 4: Continuous improvement
Formalize feedback loops at every level. Improvement should be ongoing, not periodic.

Now you have something to measure.  Measurement is both quantitative and qualitative.

Quantitative:

  • Time to complete reviews
  • Throughput per cycle
  • Recommendation tracking and resolution
  • Regulatory and audit outcomes

Qualitative:

  • Trusted advisor status with the board
  • Credibility with credit administration
  • Value recognized by the line

Not everything that matters can be measured, but enough can be measured to know if you’re improving.

Guard the institution’s credit culture

Loan review is not a back-office function. It is central to credit risk management.

A modern loan review function is proactive, judgment-driven, and focused on actionable, portfolio-level outcomes. It is grounded in data, enabled by technology, and committed to continuous improvement.

Most importantly, loan review is the guardian of the institution’s credit culture. That responsibility demands rigor, independence, and a willingness to challenge—not just process, but assumptions.

The goal is to stay ahead. Because when loan review stops challenging assumptions, risk doesn’t disappear. Instead, it compounds, quietly, until it overwhelms. And then, it may be too late.

 

Learn more about modern loan review software.

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About the Author

Kent Kirby

Senior Consultant, Portfolio Risk
Kent Kirby is a retired banker with over 39 years of experience in all aspects of commercial banking: lending, loan review, back-room operations, credit administration, portfolio management and analytics and credit policy.  As Senior Consultant in the Portfolio Risk practice, Kirby assists institutions in the review and enhancement of commercial

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About Abrigo

Abrigo enables U.S. financial institutions to support their communities through technology that fights financial crime, grows loans and deposits, and optimizes risk. Abrigo's platform centralizes the institution's data, creates a digital user experience, ensures compliance, and delivers efficiency for scale and profitable growth.

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