For years, commercial loans have been granted based upon a single number, typically ranging from one-to-eight, which defines the risk based on an amalgamation of borrower data.
While this approach does employ real, tangible facts, relying on a single number may not be the most accurate way to evaluate risk.
Just look at the percentage of your own commercial loan portfolio that’s rated between “three” and “four.” Are all of these companies—and the loans they carry—really that similar? Chances are they’re not.
Although traditional risk-rating models worked 30-years ago, they don’t provide the insight needed to succeed in the financial services world today. Slowly but surely, models are beginning to change, bringing new transparency to loan portfolios and enabling institutions to make better lending decisions.
Let’s look at the emerging trends driving this risk rating evolution.
Trend No. 1: Dual risk ratings
Instead of relying on one rating based on multiple data points, many institutions are using a dual risk rating system for greater consistency and better decisioning.
Using this methodology, institutions separate out the variables used to rate the loan into two distinct segments—the borrower’s risk and the potential loss the institution would realize if the loan defaulted—and assign a rating to each one.
Institutions assess the risk of the borrower by looking at personal assets, management style, track record, and experience. They also take into account the loan officer’s personal judgment, which will always be a part of the lending process. That’s one rating.
The second rating looks at the loan itself, evaluating the potential loss that extension of credit could bring if things go bad. This evaluation is done for each individual loan the borrower requests.
So, lenders may have cash collateral for one loan and rate it as a one, while on another loan, the loan-to-value may be a policy exception resulting in a rating of five—so the lender may request additional collateral or other safeguards to secure that line of credit.
In a single rating system, these numbers would have been combined for a rating of three. So, both loans probably would have been granted without any adjustments for the riskier credit extension.
Dual risk ratings give institutions the granularity they need to more accurately pinpoint risk.
Trend No. 2: Always document the ‘why’
Institutions are also beginning to document the reasons why a specific borrower or a specific loan was rated in a certain way, with supporting data.
In the past, if you pulled a commercial borrower’s file, you’d find the risk rating number but no information about how that number was derived. If the loan was originated six months or a year ago, even the loan officer who assigned the rating may not completely remember the thought process that went into that decision.
The answer is not asking loan officers to jot down a quick explanation on a post-it note. The best approach is to simplify and standardize the process by creating a short, yet thorough, questionnaire that captures both judgmental and data-driven rationale.
The questionnaire adds transparency to your lending program and ensures your individual loan officers document their reasoning in a consistent way. That way, when examiners visit, you have the information they need at your fingertips.
When you analyze your loan portfolio, you have the insight you need to understand why decisions were made and to recognize if anything has changed enough to impact the risk profile.
Trend No. 3: Expanded rating scales
When you’re adding detail and expanding the parameters of a risk rating analysis, it only makes sense that the rating scale will get broader as well.
Whereas, in the 1980s, a rating scale of one through eight may have been enough, lenders today are expanding that scale to one through 10 or one through 12.
They’re also adding variables. For example, if the bulk of their portfolio is rated as a “three,” they’re expanding those values by adding three-A, three-B, and three-C ratings.
This process enables the institution to distinguish between a “good” three versus a three that needs to be monitored.
It’s also important to identify what the criteria for these ratings should be and set specific policies around the valuations. For example, some institutions may categorize lines of credit with loans-to-value at 80% or higher as an automatic “three.”
The idea is not to limit your loan officers’ influence but to set policy to make those ratings consistent.
Why? If you give the same borrower to three different loan officers, you don’t want to get three different risk ratings. It’s important—particularly now—to get consistency while still leveraging your loan officers’ insight.
Making it happen
The good news: You don’t have to make this transformation alone. Technology is a great enabler.
Solutions exist that automate analytics, standardize the rating process, and support dual risk rating. The great benefit to taking this automated approach is that you can archive the data electronically. That makes pulling reports or doing monthly reviews of ratings and risk quick and relatively easy.
Institutions can also segment portfolios by risk rating, monitor potential troubled loans more closely, and work with the borrower sooner in the process to prevent default.
In this new economic environment, the risk rating approach that worked in the 1980s is no longer feasible today. Institutions need a more granular, more accurate risk rating model that pinpoints accounts needing more attention and gives credit unions a clearer view of their loan portfolios, while still leveraging loan officers’ insight and knowledge.
These risk rating advancements weren’t created to block loans from being written. They really do just the opposite.
The more accurately institutions can assess risk, the more good loans they can make—while taking the necessary steps to secure additional collateral or guarantees.
A 30-year-old risk rating system just isn’t viable anymore. Greater transparency, dual risk ratings, documented justifications and consistency throughout the process will empower financial institutions to make better decisions and manage their portfolios with greater accuracy.