It happened in only 48-hours: The collapse of Silicon Valley Bank (SVB), a key player in the tech and venture capitalist community, left investors, businesses, and tech players the world-round scrambling for a path forward. For the rest of the financial industry, the SVB collapse triggered a wave of fear, the reverberations of which are still being felt.
On March 12th, just days after SVB’s collapse, regulators closed another bank. Signature Bank, closely connected to SVB, provided services to real estate companies, law firms and cryptocurrency companies. And it isn’t just US banks being affected by inflation. On March 15th, Credit Suisse bank, a systemically important financial institution with deep ties to financial markets all over the globe, plummeted to a record low after years of mismanagement. So how did the 16th largest bank in the US and a major player in Zurich fall so quickly, and could big data have kept this from happening?
Inflation’s Effect on the Financial System
In the back half of 2022, the Federal Reserve Bank, which plays a leadership role in monetary policy, financial supervision, and the payments system, began to increase interest rates to quell inflation. Historically low-interest rates and a rapid increase in the supply of money in the few years preceding slowly drove up inflation in the market. As far as the Fed was concerned, something had to be done.
The Fed has increased interest rates 8 times so far between 2022 and 2023, and continues to anticipate ongoing increases. Borrowing costs are still at their highest since 2007, and as interest rates climb, new liquidity risk is being introduced to regional Banks. The result? Insolvency for the banks that are overexposed to high-interest rate risks, and a dip in consumer confidence, which can trigger what proved to be the death knell for SVB: bank runs.
What Is a Bank Run, and Are They Preventable?
A bank run occurs when a large group of depositors withdraws their money from a bank at the same time. Customers in bank runs typically withdraw money based on fears that the institution will become insolvent. As more people withdraw money, banks use up their cash reserves and are forced to sell their assets below market value to meet their obligations.
In today’s social environment, rumors of insolvency can quickly spiral out of control in a matter of hours through social media. This can create extensive damage and increase the likelihood of bank runs. An increase in bank runs creates public trust issues, which can rattle entire financial systems. With the ecosystem being so interconnected, every bank has exposure to other financial institutions in some shape or form.
In order to prevent bank runs, governments have put several checks and balances, such as establishing reserve requirements and insuring bank deposits through the FDIC, to improve public confidence in U.S. financial systems. The Federal Reserve also conducts a stress test that assesses whether banks are sufficiently capitalized to absorb losses during stressful conditions. However, this stress test is limited to large banks and is only conducted annually.
The reserve requirements, FDIC-insured deposits and stress tests, are great tools to ensure managed risk exposure, but none of them proactively prevent bank runs. And as we have seen, once a bank run is threatened or happens, financial institutions have very little time to stop the hemorrhaging. So how can a bank run be prevented in the first place?
Using Data to go from Reaction to Action
The current and future market conditions demand more structured, integrated data-driven tools to detect credit risk, market risk, and liquidity risk proactively. And not just for large banks, but for small and medium-sized banks as well. These tools enable banks to manage their risks in real-time with ever-changing market conditions.
Data plays a very important role in assessing risks and addressing them. Keeping your data well-managed and accessible in order to make quick data-driven decisions is a key element. However, traditionally, banks suffer from fragmented data sitting in various legacy systems. Without centralized data, banks can’t make timely decisions to quell risks imposed by changing market conditions.
The Importance of Cloud Data Architecture
Without “breaking the bank,” can these challenges be solved? The answer is yes. New cloud technologies like Snowflake offer a centralized location and data governance and architecture frameworks specifically tailored to the needs of a financial institution. Heightened security features like Zero-Copy Cloning and Data Clean Rooms make it possible for banks to not only store and process more data than ever before, but to do it safely and efficiently.
Today, banks and financial institutions can get access to a complete, 360-degree view of the individual they’re serving or prospecting. For banks that hope to continue to grow and remain solvent, the importance of customer 360 cannot be overstated. Organizations will need to marry their transactional data with additional information streams, chief among them, sentiment data. With Customer 360, banks can begin to identify and reinforce sentiment risk, which is not as easily identifiable as credit or market risk.
Playing a Game of “What If”
It’s not difficult to imagine how the SVB crisis might have played out differently, had their data been able to tell them the full story. Understanding the shifts in market sentiment and depositor confidence in real-time by using tools like C360 could have shown them how connected their depositors were through venture firms, and given them the ability to predict a soft run on the bank. With this data, they could have readjusted their risk models based on the market conditions and pushed them towards interest swaps or other means to fix their over exposed assets.
The ability to take a 360 view of their market and track sentiment data streaming in from external sources might have triggered some action from SVB. When your primary foe as a business is a loss of confidence among your consumers, intelligent modeling, risk adjustments, and a well-timed email, app notification, or company press release can be all it takes to shore up customer sentiment and keep your doors open–for good.
Future-proof Your Organization with Hakkoda
Hakkoda, a Snowflake Elite Services partner, is a specialized modern data services provider with deep domain expertise in the financial space. With a team of SnowPro certified data scientists, Hakkoda helps customers implement big data architectures, building systems that enable secure access to information from multiple sources.
With Hakkoda, providers can harness the power of their data to improve outcomes with a 360 customer view. Reach out to one of our experts to learn more.
