Many financial institutions are sitting on heavy concentrations of commercial real estate (CRE) portfolios that are going to need workout-like assistance in the next six to 12 months. Without significant help, borrowers that were on the brink before COVID-19 will not survive. Borrowers that require a banner summer season to make their books tint green for the year are also in serious trouble.
Importantly, credit challenges will become a “Defcon 1” data analytics priority. The impending credit storm was evident when large banks like Chase, Wells Fargo and Bank of America added billions to their loan loss provisions.
As banks and credit unions look at their loan portfolios, they will be smart to begin assessing whether they have all the coding necessary to identify the highest risk segments. Troubled Debt Restructuring (TDR) guidance relaxation from regulators does not mean borrowers that are given help now will not end up classified as TDRs down the road.
Here is a quick checklist to help institutions weather the storm that’s brewing:
A data scan will provide the institution with a way to segment its loan portfolio. Since loan data is all over the place, the institution might need to look in its commercial loan origination, Allowance for Loan and Lease Losses (ALLL) and core systems. In addition to standard CRE groupings of office properties (owner-occupied, multi-tenant, multi-family, retail/restaurant, industrial, land, etc.), NAICS is the most common way to segment. All the institution’s highest impact businesses should be classified with NAICS or SIC – consistently and immediately.
Tellers, customer service reps and other team members would likely jump at the chance to help the institution in this new remote workforce environment.
Organizations such as Moody’s and the credit bureaus can provide insights that will help ensure the institution is getting frequent risk ratings and credit score information.
Difficult times increase risky behaviors by owners to save their businesses. It’s important that the institution understands which businesses might be labeled as high risk (e.g., those with onsite private ATMs, pawn shops, jewelry stores, and other cash-intensive businesses). Also, leverage the AML team’s adverse media or negative news searches. Many providers catalogue news articles by looking for dirty words (bankruptcy, default, embezzlement) and action verbs (accused, convicted, indicted) to help cull the endless results of a Google search. If the institution is using this service in the AML and fraud departments, it might be wise to open these searches to loan officers managing their portfolios.
In this unprecedented time, regulatory agencies are working more closely with institutions to ensure the quickest recovery for our country. These agencies are likely to have insights into how they view the institution’s relief efforts and forbearance plans and the data points for regulatory oversight that will be required.
The scenarios and data an institution used in a prior stress test will help it find potential problems in unemployment, vacancy rates, collateral values, capital rates and property value trends. If the institution is not getting the right markets that are indicative of its geography, now is the time to find a provider that can provide at least quarterly updates.
While there are no existing standard models from an ALLL perspective (unless the institution has something documented from the Spanish Flu for historical loss), the institution can begin to stress test its loans with older scenarios to identify which ones might move into workout status the quickest. Studies show that institutions that more rapidly pushed workouts in 2009-2012 came out healthier than those that chose to wait.
Because time is of the essence and the potential risks so high, executives should not hesitate to mobilize a team that includes outside credit and data analytics resources. Credit teams are outstanding at engaging borrowers and portfolio management activities, but they may struggle spinning up an integrated data strategy on the fly.
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Tom Berdan from DadeSystems used to tell me that when he was a banker, he did not like loaning money to painters, plumbers and professional athletes. Knowing that the first two groups were likely cash-poor and running lean businesses, I asked him why the professional athletes. His response: “Have you ever tried to repossess a Lamborghini from a Tampa Bay Buccaneer lineman? I have.” Enough said, Tom.
It’s been a nice decade of growth and shareholder value expansion, but now bank shareholder value will hinge very much on how well the coming storm of credit risk and capital is managed. Financial institutions that mobilize credit risk data analytics quickly and effectively may have the best shot at successfully navigating the storm.