Although banks currently manage their assets and liabilities reactively, there has been a push towards a proactive approach. For the latter, they need models to provide accurate cash flow projections and tools to design and test management policies, which they lack.

This thesis puts forward an economic engineering solution. It exploits dynamic systems modeling to provide cash flow projections that are accurate and process control to design and test liquidity and solvency risk management policies in real-time. To ensure that it models the dynamics that govern the bank's cash flows realistically, we integrate several macroeconomic theories into its design.

We demonstrate the potential of the approach through example applications provided by ALM experts at Rabobank. These include scenarios for which they have to rely on their intuition to assess their impacts. Our results show that a competitor bank that disrupts the banking sector with a low interest rate spread becomes unsustainable under solvency constraints. A tariff shock leads to a short-term rise in interest rates, and in hindsight, the model correctly predicts a liquidity surge and a decrease in interest rates during the COVID-19 pandemic.