Another great year has gone by, the stock market notwithstanding. With the number of banks and credit unions continuing to shrink, the cream is rising to the top. The quality of the remaining institutions is getting better.
Looking ahead, regulation is still a major concern. A primary regulatory focus remains interest rate risk with rates rising after a long period of relatively flat rates. Liquidity risk has also become a concern, especially after this past weekend’s turmoil with Treasury Secretary Mnuchin’s call to the major banks asking about their liquidity. Then there is the lingering concern about CECL which is due to be enforced in 2019 and 2020. Finally, the suggestions of recession on the horizon could spell difficulties for everyone.
It continually amazes me that after almost 40 years of interest rate risk methodologies coming into play, how many bankers are still confused about the risk assumptions and the meaning of the results. Most of the technologies ignore the value of basic projection modeling as opposed to the somewhat inaccurate static shock method. Simulation scenarios build in alternative conditions with multiple projections and provide a very good estimate of not just rate change impact, but also local economics and even behavioral conditions. Whereas static rate shocking lacks these essential elements. I foresee that artificial intelligence (AI) will provide a far better assessment of risk because it will encompass both basic numerical modeling and behavioral aspects of the issues involved.
Liquidity has always been an issue. I remember when liquidity was an interest rate management tool. When rates were on the rise, you wanted more liquidity to take advantage of lending at higher rates. When rates were falling, less liquidity meant that deposit rates would fall while higher loan rates were held up. Now, with so many variable and adjustable rate products, that’s less often the case. Bank executives thought their liquidity was in good shape but became concerned when the Secretary raised the issue. Apparently, they thought they might be missing something. Even so, liquidity and cash flow modeling are important to keep a finger on the pulse of the issue.
CECL is a new attempt to forecast potential credit risk. It is designed to examine every loan and determine its potential to fail. This requires a long-term statistical analysis of past activity to develop a model that forecasts prepayments and defaults. That forecast then dictates the bank’s Loan Loss Reserve level. Predicting future performance from past performance is not always the best methodology. However, as a friend of mine in the car business used to say, a bad ride is better that a good walk.
Looks like 2019 will be another interesting year in the banking industry. In my almost 50 years in the business, there has never been a dull moment. It’s been fun.
Craig Hartman
Founder & CEO