Financial Stability and the Bottom Line Top of Mind for Bankers

Financial Stability and the Bottom Line for Bankers

A new 54-page working paper from the International Monetary Fund isn’t exactly the kind of thing you’ll catch even the most motivated banker reading on the morning commute. For starters, it is chock full of puzzling references to the Cobb-Douglas production function, quantile regression, operational risk shock – and of course, the ever-popular Arellano-Bover/Blundell-Bond system estimator.

But for those clever enough to circumvent the jargon and methods full of deltas, thetas and sigmas, “Bank Profitability and Financial Stability” has some wise things to say about an issue that concerns everyone in financial services. For instance, that over-leveraging is definitely a bad idea, and relying too much on non-interest income isn’t a great strategy, either. The authors reached those conclusions after they analyzed data on more than 400 publicly traded banks, spanning from 2004 to 2017.

Regardless of what any paper proclaims, profitability and stability exist in a very real tension. For those in financial services, the yearly and even daily task of walking the tightrope is as much about mental as financial stress. Yet those who commit to a proactive approach experience good stress, as in what to emphasize while making smart business decisions and carrying them out.

The Automation-Integration Connection

A financial institution’s portfolio speaks volumes in terms of how equipped the institution is to weather reasonable risk, versus inviting the unnecessary kind. Here, it becomes a question of proactive versus reactive. To be certain, even the best banks and credit unions may struggle to pinpoint and tackle negative issues before problems arise. Yet as lending competition rises and portfolio growth assumes a greater priority, institutions must evaluate their ability to quickly and successfully recognize trends – positive and negative – and act.

That of course poses a monumental challenge, as many financial institutions still manually review deposit accounts, credit line balance changes and declining credit scores. As a result, they simply cannot and will not keep up. However, thoughtful automation and data integration can reverse this liability. For the bank or credit union that finds its data residing in disparate silos and spread-out Excel spreadsheets, a single database is a game-changer. In addition to simpler data access, it gives portfolio managers a holistic view to better manage behavior and more quickly catch potential problem credits.

Looking at Risk and Growth With the Same Lens

Now let's consider exactly what unified data can accomplish. While it is tempting to avoid the daunting task of tying all your data together, the everyday benefits will provide some powerful motivation. One New York state credit union, for example, recently took the big step of moving to a single system that allowed them to view risk and growth with the same lens.

The immediate next move was to combine electronic business loan applications from its branches with an origination system for auto-decisioning. The combination achieved a 60 percent auto-decision rate on loan applications under $100,000 in 2018. Meanwhile, the financial institution saved approximately 1,300 hours in manual labor. Therein came another big win as this time savings bolstered the employee and member experience.

Seeing Into CECL, Setting Sights on the Future

Make no mistake – advancements in data analytics and newer business intelligence tools have given financial institutions the resources to evaluate opportunities and vulnerabilities within their loan portfolios, and do so continuously.

This will prove more important than ever as the Current Expected Credit Loss standard (CECL) takes effect over 2020 and 2021. While CECL reflects the current risk in a portfolio, keep in mind that this includes current and future credit losses. This new model – the biggest change to financial services accounting standards in decades – will significantly change the way banks and credit unions maintain and analyze data. In fact, many institutions are still trying to determine the best way to calculate future expected losses even as the CECL deadlines draw near.

By using data analytics and leveraging insight from portfolio risk management activities, financial institutions can keep an eagle eye on their loan portfolios, navigate the CECL requirements, stay steps ahead of potential risk factors, and in the process, outflank the competition. That leads to maximized profitability and minimized risk: a simple, balanced equation even an IMF researcher can learn to love.