Because of a historical concentration on credit risk management, as Donald van Deventer sees it, “U.S. regulators have ignored interest rate risk-related regulations for more than 30 years.”
“Regulations are lagging both best practice and emerging best practice by several decades,” contends van Deventer, the Kamakura Corp. founder who is managing director of risk research and quantitative solutions at SAS. “It’s the real risk of failure – not regulation – that’s driving financial institutions to abandon common-practice risk systems in favor of best-practice enterprise risk management solutions.”
The spotlight shining on interest rate risk (IRR) – intensified by conditions that brought about the March 2023 Silicon Valley Bank failure and heightened regulatory concerns – is also bringing into view new data, decision-support and workflow tools capable of improving IRR management.
Van Deventer references a 2018 article in Central Banking, co-written by the late Bank for International Settlements official Philip Turner, warning that interest rate risks could spark the next great financial crisis. That was prescient, in that the October 2023 Basel Committee on Banking Supervision report on that year’s banking turmoil highlighted the vulnerability of traditional IRR models to abrupt market shifts.
Silicon Valley Bank Financial Group had “open supervisory findings” in core areas “such as governance and risk management, liquidity, interest rate risk management and technology,” said the BCBS report. “Despite widespread evidence of foundational governance and risk management issues, supervisors were slow to downgrade supervisory ratings or to ensure that SVBFG’s board and senior management took sufficient and immediate steps to compensate for those widespread weaknesses.”
Modern fintech solutions are stepping in to fill the gaps with advanced simulations and analytics.
Donald van Deventer of SAS
Van Deventer explains that effective risk management hinges on robust enterprise solutions that integrate Monte Carlo simulations, multi-factor models of yield curves and exchange rates, and credit risk analysis using default term structures, all while ensuring comprehensive reporting and regulatory compliance. Tools leveraging these capabilities capture complex risk scenarios with greater accuracy.
Artificial intelligence is at the core of IRR-related advancements. AI can make analytical techniques more accessible to risk managers in a quick and efficient manner.
Chintan Shah, global head, buy-side treasury ALM and finance at Bloomberg, places emphasis on techniques including AI, machine learning (ML), natural language processing (NLP), information retrieval, generative models, and time-series analysis to enable faster, more dynamic risk assessments.
Shah points to Bloomberg’s Multi-Asset Risk System (MARS) for managing balance sheets on an intraday basis while driving down funding and risk hedging costs.
“Clients are able to bring together their funding portfolios, assets and liabilities, subject them to a variety of customizable interest rate and other stresses on a real-time basis, and project liquidity that the bank needs to fund short- and long-term obligations,” Shah says.
This combination of granularity and automation helps treasury teams transition from compliance-driven tasks to proactive risk management.
Could well-integrated, fintech-aided risk management have sounded IRR warnings in time to have prevented Silicon Valley Bank’s collapse?
“Modern fintech solutions are far more advanced than regulatory pronouncements – and they have fully exposed the risks taken by financial institutions like Silicon Valley Bank,” says van Deventer.
"Best-practice interest rate risk systems would have made it clear that SVB had a very high level of default risk back in 2021,” he adds. “Had regulators and the bank used more sophisticated, enterprise-wide risk management solutions, there were 12 full months to resolve that risk position before rates began to rise in February 2022.”
A 2023 International Monetary Fund working paper, Good Supervision: Lessons from the Field, stressed the importance of robust supervisory frameworks amid continuing critical deficiencies.
A June 2024 McKinsey & Co. “playbook for the new era of volatility” identified “levers” for more proactive and effective interest rate risk management including AI-driven behavioral modeling, dynamic hedging strategies and improved stress testing.
Banks’ increasing interest-rate hedging activity: European interest-rate derivatives traded notional, quarterly, $ trillions (source: McKinsey).
Mid-sized banks and corporates are among the early adopters of fintech IRR management solutions. Lacking the in-house resources of larger banks, they are more open to third-party innovations.
Chintan Shah of Bloomberg
According to Shah at Bloomberg, from 2008 to 2022, interest rate monitoring and quantification was relatively stable and standardized, so tech investments tended more toward regulatory compliance. The subsequent rate surge and volatility required a more active approach: frequent assessments, dynamic hedging and balance-sheet visibility.
“More frequent assessments of interest rate risks, faster and dynamic IR hedging capabilities, and detailed look-throughs into the balance sheet so that risks can be managed by exception are the most impactful ways for fintechs to assist firms in their treasury workflow,” Shah says.
Validus Risk Management, a software and services provider specializing in interest-rate and other market risks, says its platform manages over $500 billion in exposure for large institutional investors, fund managers and corporations across the globe. Validus recently secured a $45 million growth equity investment from FTV Capital. Mike Cichowski, an FTV partner, said Validus fills a gap in the “massive and growing” alternative assets market where risk management had been “largely overlooked.”
While technology promises greater agility, challenges remain. Regulatory frameworks are still catching up, as van Deventer pointed out, and the new systems can be expensive to deploy.
With demand for automation and real-time analytics on the rise, Shah expects that tools capable of calculating Net Interest Margin (NIM) and Economic Value of Equity (EVE) under multiple and changing scenarios will become essential. What’s more, the anticipated implementation of Basel III capital rules will call for intraday Liquidity Coverage Ratio (LCR) and Net Stable Funding Ratio (NSFR) metrics.
“Bankers’ instinct for self-preservation, not regulation, will drive the adoption of best-practice enterprise risk management systems in 2025 and beyond,” van Deventer predicts. This is in keeping with the broader trend towards proactive, tech-driven risk management across the financial sector.
For fintech providers, the challenge may lie not just in innovating, but also in gaining the trust of institutions accustomed to legacy systems. Technology innovations can potentially redefine the treasury toolkit and make IRR management better prepared to meet the challenges of complexity and volatility.