With three midsize U.S. banks boasting combined assets of nearly $550 billion having gone under in span of a couple of months — and short-sellers putting huge pressure on others — risk management is an understandably hot topic at the moment.
As we’ve seen with Silicon Valley Bank’s failure in particular, risk management is a life-or-death proposition. But with the right technologies, data, and people in place, bank leaders can do much more with risk management than avoid catastrophe. They can gain efficiencies and insights that help chief financial officers enjoying increasingly proactive roles in their banks exploit new business opportunities while keeping the wolves at bay.
The CFO role has been evolving for some time now. While financial reporting is still a big part of the job, CFOs increasingly operate at the nexus of finance and risk management, acting as key advisors in formulating and vetting a bank’s strategic moves. Doing so has meant looking beyond the traditional focus on the past to taking in the present and divining the future. That, in turn, puts new demands on banking technology, the data that technology creates and consumes, and the people involved in deploying technology in the best interests of the business.
Here and now
Chief among those demands is an ability to support banking leaders’ and risk managers’ need for an immediate grasp of the bank’s state of financial state of affairs. That requires an ability to run simulations and forecasts based on fresh and reliable data. Banks no longer have the “luxury” of spending days wrestling with data from multiple sources to understand the impact of an interest-rate increase, a commercial real estate downturn, or other impactful business scenario. If the CEO asks these and other questions, the CFO should be able to deliver results within minutes or hours — not days or weeks.
The technologies exist today, and the biggest banks use them almost universally — largely because they’ve been indispensable to dealing with the regulatory stress-testing rolled out after the 2008 banking crisis. They include in-memory techniques that, combined with optimized reporting queries, let a CFO’s team slice and dice data from operational systems (as opposed to having to extract it from a data warehouse).
Data warehouses still come into play in supporting sophisticated, analytics-driven what-if scenarios and predictive capabilities increasingly powered or augmented with AI capabilities. The best of these are cloud based, automating data management tasks in an open data ecosystem that supports data integration, cataloging, semantic modeling, data federation, and more. The result is a continually refreshed pool of well-defined, highly reliable data upon which you can base reliable status checks and against which you can run simulations that help banking leaders make decisions quickly and confidently. Remember, too, that investments in data quality are inherently long-term and forward-thinking: We may not know what the future holds for AI and advanced analytics, but it will most certainly hinge on huge volumes of high-quality data.
From risk to reward
That said, U.S. banks with less than $250 billion in consolidated assets — that’s all but 13 of them — face lesser regulatory demands. Their systems are typically less capable, their data more siloed. Smaller banks’ settling for this status quo is a mistake for a couple of reasons. First, as the failures of Silicon Valley Bank, Signature Bank, and First Republic Bank have shown all too clearly, poor visibility into risk posts the same mortal threat to smaller banks as it does to big ones. Second, and just as importantly, exploiting real-time information, predictive capabilities, scenario analysis and other modeling will make your bank better — and a better place to work.
People matter, especially when it comes to high-end technologies. Like a Formula One racecar, the difference between victory and being schlepped off by a tow truck lies in the people involved. We give this thought relatively short shrift because banking is a people business, and we know all that already. But talented people want to have an impact on their organizations, and they recognize that it takes the right tools to be able to do it. It happens that the tech industry’s recent troubles are making it easier for banks to land the sorts of data scientists, digital-engagement specialists, and others who can get the most out of the latest financial-services technologies.
Now, regardless of bank’s investments in risk management technology, data, and people, they can’t guarantee protection against short sellers — even if, given banks’ crucial economic roles, you can count us among those who argue for limiting the practice with this industry.
But banks and the CFOs playing increasingly vital roles in leading them can do a lot to shield themselves from short-selling pessimism. By using technology to unearth festering risks, avoid missteps, and exploit new opportunities, banks can lead the shorts to darker corners. At the same time, these technologies help banks improve efficiency and competitiveness, boost customer service, attract and retain great employees, and improve their bottom lines. Risk management opens the door to opportunity management. Yes, there will be costs, though they pale in comparison to a bank run. And conversely, there are great gains to be won — in terms of revenue and, not least, reputation.
Falk Rieker is global head of banking and Kris Kowal is global head of retail banking at SAP.