Testing model simulations are essential for helping banks and financial institutions test different scenarios to evaluate how changes in market conditions, such as interest rates and inflation assumptions, can impact a bank’s investment decisions, balance sheet, income statement, or liquidity. Stress testing takes scenario analysis a step further and simulates extreme scenarios that can test a bank’s risk profile and resilience to extreme economic conditions, such as a recession, market crash, or dramatic changes in interest rates.
The recent collapse of Silicon Valley Bank has highlighted the importance of testbed simulations to understand a bank’s true picture of risk. While mid-size and smaller banks are not strictly required to perform stringent stress testing, the case for using more intensive simulations to minimize risk is stronger than ever.
When markets are volatile, banks need a complete picture of their total risk profile. A robust testing simulation, where market variables are tested at different levels to understand the impact on the bank’s total financial picture can have multiple benefits:
Scenario analysis can highlight potential areas of weakness in a bank’s portfolio and evaluate the impact of those risks. For example, interest rates rising to a certain level may highlight risk in a commercial loan portfolio and a different level of interest rate changes may have a bigger impact on a bond portfolio or overall liquidity.
Larger banks are required to conduct stress tests to assess risk under more extreme economic shocks. With bank regulations constantly changing, banks of all sizes should be prepared for potential new regulations that require higher levels of visibility and control.
Testing simulations that demonstrate robust risk management practices and financial stability can build investor confidence that leads to long-term growth and prevents withdrawals due to risk concerns.
Creating a meaningful simulation environment requires two key components:
Financial institutions need to be able to access data on all elements of their portfolios, including loans, securities, and derivatives to assess their exposure to different types of risk.
To simulate hypothetical scenarios on different economic and financial variables, such as GDP, growth, inflation, interest rates, etc., banks must build statistical models that evaluate the impact on balance sheets, profitability, and liquidity.
Many banks rely heavily on spreadsheet applications to create these risk management models. However, these spreadsheets often exist in silos and lack control, auditability, and governance. One model may simulate the impact of interest rate changes on a loan portfolio while a completely separate model tests the impact of inflation on securities.
If those models live in silos that don’t speak to each other, it impacts a bank’s visibility into seeing the full picture of risk. It also limits a regulator’s ability to evaluate risk. Given the recent shocks to the banking industry, it’s natural to assume that new regulations will require more transparency.
Your risk team is probably highly dependent on Excel because of Excel’s flexibility to create complex business logic. Excel’s strengths for individual users contribute to its weaknesses as an enterprise solution. Complex models residing on individual laptops are not scalable or connected, leading to a lack of visibility and transparency.
You can add control, integration, and visibility to your siloed risk models without an intensive enterprise software implementation. Platforms like Coherent Spark convert complex spreadsheet-based logic into no-code outputs, models, and data flows in seconds. Your team can build, model, and test directly in the Coherent platform or connect the workflows to a broader, integrated solution.
To gain visibility, scalability, and control, you need integrated cloud-based processes. But that doesn’t mean you need to abandon Excel or invest in expensive enterprise software. Coherent Spark enables you to use Excel for its intended purposes while eliminating its inherent limitations. Spark turns your complex Excel spreadsheets into enterprise-wide code in minutes – without any programming language required.