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AI in credit risk management: A friend, not a foe

Kent Kirby
February 26, 2025
Read Time: 0 min

This article covers these key topics:

Viewing AI as an ally in credit risk management

Why loan review professionals should look to AI to improve accuracy, efficiency, and speed.

Reframing AI at your financial institution

I am an avid reader. On the whole, I am not a fan of fiction. However, one notable exception is the Harry Potter series, which had me visiting my (now-defunct) Borders store at midnight each year, awaiting the release of the next book. One of the many plots in the series involved the complex relationship between Harry and one of his teachers, Severus Snape, and left readers wondering whether Snape was a friend or foe of the story's hero.  

Today, I remember the “Is Snape good or evil?” conundrum and relate it to bankers wondering if AI will help or hinder them in the workplace. Take loan review, for example. A robust loan review process is essential for ensuring that banks and credit unions safely and soundly manage their biggest asset—their loan portfolio. Traditionally, loan reviews have been carried out by human teams who analyze documents, assess borrower performance, and identify potential risks at both the individual loan and portfolio segment levels. I like to use the term “portfolio analysis from the ground up.” 

But now, AI in credit risk management has entered the scene, bringing opportunities to improve accuracy, efficiency, and speed. Banking professionals may worry about AI’s ethics or whether AI will eventually replace humans in this process. “Will I lose my job?” It’s a fair question, but the answer is emphatically no. Instead, AI should be viewed as a powerful ally in credit risk management. 

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Loan review: the traditional approach

In traditional loan reviews, a team of highly trained analysts reviews borrower performance and other key indicators to assess a loan's creditworthiness and ongoing administration. They then aggregate this information to form an informed assessment of the portfolio segment under review. 

This process benefits from human intuition, experience, and judgment. It’s like having a trusted advisor—someone who understands the nuances of commercial lending and can apply reasoning in complex situations. However, the traditional approach is often time-consuming and labor-intensive. 

AI-powered loan review: Friend or foe? 

The friend: The benefit of AI in credit risk management is clear—a technology-driven approach can process vast amounts of data in a fraction of the time it would take human analysts. AI systems can identify trends, flag potential risks, and provide data-driven insights. This preemptive approach can prevent costly mistakes, making AI a valuable ally in maintaining a healthy loan portfolio. Machine learning models can even predict future outcomes based on past behaviors, spotting what a traditional loan review approach might miss and enabling financial institutions to take proactive measures. 

For example, AI can analyze a borrower’s historical payment trends and financial statements to detect early warning signs of economic distress. It can also flag discrepancies in borrower data that might go unnoticed in traditional reviews. This predictive capability helps institutions prevent costly mistakes and strengthen their portfolios. 

The foe: AI in credit risk management isn’t without challenges, but these are easily remedied by including a loan review staffer in the process. AI might recommend a risk rating based on patterns in the data when a loan reviewer would consider extenuating circumstances—for instance, if the borrower has a valid reason for a temporary financial setback or has recovered from a longer-term one.   Whenever overreliance on technology undermines human loan reviewers' expertise, financial institutions are liable to miss unique opportunities that only relationship banking can bring about.  

Balancing AI and human expertise in credit risk management 

Relationship banking remains a cornerstone of many financial institutions, and while AI can enhance decision-making, it can’t replace the trust and insights that come from direct human interaction. 

The key to success is integrating AI to complement human loan reviewer expertise rather than replace it. Institutions that leverage AI to handle repetitive, data-heavy tasks free up their teams to focus on strategic decision-making, relationship management, and nuanced credit assessments. 

The future of AI in loan review 

As AI technology evolves, its role in credit risk management will become even more critical. AI-driven models will improve portfolio monitoring, stress testing, and regulatory compliance, helping financial institutions mitigate risk while remaining competitive. The most successful institutions will be those that strike the right balance—using AI to enhance efficiencies while maintaining the human expertise needed to interpret data, build relationships, and make sound credit decisions. 

Just as Severus Snape’s true role was revealed only in the final chapters of Harry Potter, the true potential of AI in loan review is still unfolding. What’s clear so far is that AI in credit risk management is not an adversary to be feared but a tool to be harnessed. When used wisely, it strengthens loan review processes, identifies risks that might otherwise go unnoticed, and allows financial professionals to work smarter, not harder.  

And for the record, I was always in the “trust Snape” camp. 

Learn how Abrigo’s AI-powered Loan Review Assistant seamlessly integrates with DiCOM Loan Review to pinpoint risk with precision and support informed credit decisions.

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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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