How Technology is Changing Bank Analytics

By Malcolm Clark and Lisa Getter, Invictus Consulting Group

How do you analyze a bank?

In the old days, it was a case of getting the most recent balance sheet and financials and “running a slide rule” over them.  OK, so we graduated to calculators in the mid-1970s and spreadsheets in the 1980s, but the process did not really change much after that – and still hasn’t, for many community banks, investment banks and advisors to the banks. And that’s short-sighted.

Analyzing a balance sheet is something any bank director or executive should be able to do.   They’re just missing a trick.

After regulators started making bank Call Report data publically available, and the Internet allowed access to market and economic data, traditional bank analysis should have gone the way of the horse and cart.  The cost of getting relevant data and running models has plummeted. Cloud-based computing power and data storage provide inexpensive ways for even community banks to reap the benefits of data and analytics without a huge IT department. The analytics can be far more useful today than was ever conceivable even just a few years ago.

This evolving technology can help bank analysts tap into data to create a whole new genre of analytics that take into consideration changes in the economic environment, regulatory capital adequacy and monetary policy.  These actionable analytics can produce pragmatic, accurate and highly flexible historical and pro forma reports that can re-educate bank directors on how community banks are operating.  They can aid with CECL-readiness, capital requirements and M&A positioning, and provide road maps for effective conversations with regulators.

Yet financial reporting in the community bank market is essentially static: Calculating ratios using historical data from annual reports and Call Reports. Traditional financial reporting offers limited insight into understanding future implications of a bank’s strategic plans. The 2008 financial crisis showed the failure of using legacy analytics. Now that we are years into an unprecedented period of artificially low interest rates, new analytics are needed even more. Regulators have recognized this, and their own pendulum has shifted toward forward-looking risk analytics.  Bank examiners are often armed with an analysis of the bank before they even walk into the door.  Smart banks should have access to those same types of analytics.

Conventional analytics are not only limited, but can lead to the wrong diagnosis.  This is especially true in an M&A context.

Community bank directors should be asking their analytical teams about trends, economic projections and how they will affect the bank, for themselves, their competitors and their acquisition targets.  They should understand how their loan portfolios will behave under stress, so they can document the impact on regulatory capital.

In today’s environment, models can simulate how a bank (or a merger) will perform under various scenarios. With the right technology, multiple scenarios can be run relatively easily on the bank itself, its competitors or an entire short-list of potential targets. Such scenario analysis will give bank directors a much better feel for the risks in their bank, their competitive positioning, or the value of a potential acquisition.

Regulators are encouraging financial innovation, yet financial reporting has generally remained the same for decades. When data was scarce and difficult to come by, checking a bank’s latest financial statements was all anyone could do.  That excuse holds no longer.

Done right, a bank’s analytics should allow for comparisons among state or regional average banks, and even among hundreds of banks in the U.S. They should also be able to show the evolution of the bank over several years, and how that compares to any other bank in the country. The analytics should be easy to understand and actionable for the executive team and the board as well.

Bank analysts must take advantage of the sea-change in data availability and inexpensive processing power – realizing that analyzing that data and building the appropriate models may be beyond the ability or time commitment of many community bank analysis teams.  If they can’t build a model themselves, they can either collaborate with other banks that can, as the Office of the Comptroller advocated in 2015, or hire a third-party to help.