Managing credit concentration risk

Overcoming barriers to concentration risk insight

As noted earlier, one challenge with keeping an eye on any concentration of credit risk is that credit accumulations tied to a characteristic can build quietly. Loan review cycles that are segmented or siloed by lending team or product type can make it difficult for decision-makers to fully recognize how that exposure is aggregating—or deteriorating—until it’s too late.

Time-consuming, manual processes and turnover in loan review staff or constrained staff can also hamper the institution’s ability to recognize increasing loan exposure or deteriorating trends in a particular category. And it’s clear that many loan review departments are facing such challenges in their day-to-day work.

A recent Abrigo loan review webinar found that 56% of respondents consider time-consuming manual processes their biggest challenge in the loan review process. Another 24% said the top challenge is limited staff resources, and 12% said it’s identifying emerging credit risk.

That’s where standardized, software-enabled loan review processes become critical. Most institutions have a firm grasp on their largest credit relationships. But understanding aggregate exposure across similar loans—especially when loans span business lines, geographies, or periods—requires the ability to extract structured insights from the credit data already being collected.

Centralizing review data and applying consistent risk rating methodology across the portfolio helps credit risk teams:

• Tag and analyze loans by concentration categories (e.g., NAICS codes, property types, geographic zones)
• Identify sector-specific risk trends in risk ratings, exceptions, or performance metrics
• Compare actual exposure to risk tolerances set by policy or board-level limits
• Detect participation layering or overexposure in sectors that have experienced recent stress
• Surface actionable insights for management and the board before concentrations create systemic vulnerability

Using some loan review software, users can leverage concentration tracking dashboards and reporting features to drill down into high-risk segments and flag others for investigation. They can evaluate risk rating consistency and assess borrower performance within those concentrations. Abrigo’s DiCOM Loan Review Software has these features.

Abrigo Senior Consultant Kent Kirby, a retired banker with experience in all aspects of commercial lending, loan review, and portfolio management, has said that one reason identifying emerging trends can be difficult is that loan review analysis is resource-intensive, even when it’s automated.

Kent Kirby

Many loan reviewers, he said, are “so tired just doing the analysis, you never get around to doing the root causes and patterns.”

The newest feature available with DiCOM is aimed at addressing that. An AI-powered loan review assistant analyzes loan data and automatically generates insights. It uses generative AI to build narratives to quickly inform credit risk decisions aligned with an institution’s policies.

“Artificial intelligence allows you to do that initial analysis, to cut that time down so you can focus on root causes and patterns,” Kirby said.

Asking the right questions before the regulators do

Risk reviewers equipped with data-driven tools can provide insights beyond file-level observations. They provide strategic intelligence that can inform loan policy, underwriting standards, and capital planning.

Notably, such tools also allow loan review officers and credit analysts to communicate quantified risk trends clearly across the organization. Highlighting to leaders insights tailored to the institution’s unique risk profile will make it easier for senior management to make timely, well-informed decisions about credit concentration limits, capital buffers, and loan participation strategies.

That’s especially important in an environment where CRE vacancy rates are shifting, farmland values are responding to commodity volatility, and interest rates and the economy are placing pressure on debt service coverage ratios.