The U.S. Federal Reserve’s ambitious rate hike program and subsequent market volatility have exacerbated financial uncertainty for banks, raising questions as to how organizations can bolster their financial resilience. 

Numerous banks, including notably Silicon Valley and First Republic, were unprepared to contend with market forces draining liquidity from their balance sheets, further underlined by sharp price corrections in their tech-oriented available-for-sale portfolio. 

While the Fed’s Bank Term Funding Program backed by $100 billion of liquidity injections stanched the immediate run on vulnerable regional banks, broader concern remains about the adequacy and maturity of bank interest rate management practices. 

Smaller banks found themselves at the mercy of being acquired by larger peers considered “too big to fail” given their systemically important nature. Effective hedging strategies have emerged as a critical solution for banks to fend off market pressures, lest they are forced down the costly path of raising new equity capital meant to top up loss buffers and satisfy regulators.

Speaking with Delfi CEO, Daniel Ahn, however, an industry-wide issue becomes apparent, “There are over 4,700 banks in the United States but only a small fraction of them regularly engage in derivatives contracts that would shield their exposure to adverse and unexpected changes in the yield curve, the basis of bank profitability.”

Ahn continues, “Many institutions do not feel they can effectively employ these sophisticated derivatives. Users need to engage in thorough balance sheet analysis and cross-reference against optimal instruments, duration match and notional position size. That is where our next-generation AI solutions will come into play, by reducing barriers and providing tangible trading recommendations.”  

Hedging has taken on new importance in the face of sometimes contradictory market data that has surprised even veteran economic forecasters. This uncertainty makes bank strategic planning difficult. For instance, one camp of economists argues that more central bank rate hikes are necessary to reach the Fed’s 2% target given persistently high inflation. The other group contends that such historic monetary tightening, which crystallized problems in the banking sector, will prompt a slowdown that would force the Fed to pivot to rate cuts. But in either scenario, the most practical solution for banks is to thread the needle by hedging their exposure and focusing on executing their core mission.

AI Advances Make Hedging Accessible

When married with domain expertise, AI can iterate through the space of possible hedging solutions far quicker than even the most experienced Wall Street teams. It might take a team of 5-10 competent (if pricey) quantitative risk managers days or even weeks to develop a custom hedging strategy depending on the complexity of the balance sheet. By contrast, Delfi’s algorithm can churn through millions of potential strategies within minutes, uncovering and fine-tuning strategies even the most intelligent analysts might overlook.

AI-built hedges are ultimately cheaper, faster and more customized. And so AI will become focal to boosting risk-adjusted returns and reducing the variance of bank earnings. 

Additionally, trading in AI-designed futures offers key advantages compared to more expensive interest rate swaps that require backstop collateral, a drain on bank liquidity. This is also an important opportunity cost of capital that could be redirected toward new lending activity. 

With the click of a few buttons, bank CFOs, CIOs and CROs can trust that their minimum Net Interest Margin is defended from the vagaries of financial markets. In turn, this allows for additional focus on evaluating new loan opportunities, attracting depositors and expanding into new markets.

Banks and credit unions can offer new products at floating rates but have automatic hedging built in to receive stabilized income. Asset-Liability Committees can report their expected margin up to years in advance, meaning growth plans can now be calibrated for longer terms. It’s also important to consider investor attraction to companies with more stable returns, particularly in context of today’s challenging environment. 

Advances in computational analytics have now liberated the domain expertise of Wall Street. The bespoke hedging solutions that AI provides for banks across the size spectrum are a truly democratizing force.

Silicon Valley Bank Case Study

In a demonstration of Delfi’s robust algorithm, we share an application of its risk management solution on Silicon Valley Bank (SVB) as of 2021 - just two years before its collapse. If a modern risk management AI had administered SVB’s interest rate exposure, could the bank have avoided its untimely fate?

The basic facts are as follows: when U.S. interest rates rose rapidly over the course of 2021-23, SVB’s mark-to-market losses on its securities portfolio exceeded its gains from net interest income, sparking a bank run that ended in collapse. SVB’s management had failed to properly manage their risk, instead unwinding what hedges they did have to maximize near-term income and essentially making a speculative bet that rates would fall again. This would cost them dearly.

Ironically, over this time period, Delfi’s AI calculated that the most effective hedge would be a “spread” position coupling a long position in short-term Fed funds futures with offsetting short positions in longer-maturity Treasury futures. That is, the AI successfully identified that SVB’s balance sheet featured risk exposure not just to parallel increases or decreases in the yield curve, but also changes in its steepness, or more colloquially economy-wide expectations of recession. 

The hypothetical success of AI hedging is noteworthy. Delfi’s solution would have converted a -$2bn loss into a +$3.5bn gain in SVB’s performance over 24 months. The net interest income that SVB management was so eager to boost could have been defended. The volatility of SVB’s financial performance would be reduced by 95%. This entire calculation took less than an hour.

The Takeaway

While technological adoption into workflow can seem daunting, new AI solutions can leapfrog institutions beyond yesterday’s cutting edge, fundamentally transforming the competitive landscape.

As AI facilitates more intelligent interest rate management and anchors investor expectations, bank executives can focus their attention and resources on their primary differentiators: people, pricing and promising new investment opportunities. 

For additional information about Delfi, including a full copy of the case study on Silicon Valley Bank, please visit www.delfi.co or register for free trial access.

Disclaimer: This publication is for general information only. It is not a recommendation to buy or sell any security, and it should not be relied upon as advice of any kind.

Delfi is a fintech start-up focused on bringing AI-enabled financial risk management and hedging solutions to businesses driving the real economy.