“JPMorgan Chase plans 8,000 layoffs”
“Bank of America announces another round of layoffs” 
“Barclays to cut up to 12,000 jobs”
“Wells Fargo announces 6,400 layoffs” 

Headlines like these reflect a trend that metrics have been indicating for some time: with a shift in customer behaviors and a fall-off in mortgage demand, some segments will become less profitable.

This has not been unexpected by the top banks in the country, nor should it come as a surprise to anyone in the industry who has been following mortgage trends over time. The need for staff to close loans in a demand environment leads to frantic hiring and training. At the end of the cycle, the ramped-up staffing can result in layoffs, particularly if the staff cannot shift into other business lines.

The fact is that all the big banks have extensive metrics to let them know when they need to hire and train in addition to scenario planning to project resource requirements as the business curtails.  Metrics and modeling are becoming even more important as customer behaviors continue to influence service needs.  Analytics projected on a macro level can calculate staffing needs using the rate environment to inform; on a micro level, they can indicate whom to retain in a down cycle based on individual performance factors.

On a macro level, Nolan’s annual Bank Performance Study shows that there is a performance gap of 415% in total mortgage line of business between the top quartile and the average banks. (The line-of-business efficiency ratio ranges from 7.62% to 39.13% for mortgage banking.) This indicates that many banks were concentrating on top-line revenue and not line-of-business profitability.  As demand falls off, as it has in the past 10 months, those banks with the higher efficiency ratio will see profits eliminated and losses incurred, resulting in dramatic layoffs.

We are also seeing banks beginning to shift their focus to commercial lending to replace revenue that is drying up in the residential mortgage sector. Modeling will help them better understand how to compete and what level of performance they should expect.  The gap in commercial lending between the top quartile and the average banks is currently 58% (the line-of-business efficiency ratio ranges from 14.31% to 22.61%). The detailed level of individual performance can help a bank determine the differences between their lenders regarding new relationships per lender, average loan per lender, and new loans as a percentage of portfolios.

Analytics, when integrated into regular business reporting, provides a current and forward look at performance, which so many community banks need as the competitive landscape changes. Understanding how to apply analytics is of paramount importance in today’s dynamic banking marketplace. They will help you get an objective understanding of what is possible and of realistic goals for your professionals to adopt as banks become increasingly mobile and competitive.