Advanced methodologies for stress testing banking institutions under various economic scenarios

Posted: Oct 28, 2025

• Prof. Sofia Kimura
• Prof. Victor Mehta
• Dr. Amelia Wang

Abstract

The global financial crisis of 2008 exposed fundamental weaknesses in traditional banking stress testing methodologies, prompting regulators worldwide to seek more sophisticated approaches to assess financial institution resilience. Conventional stress testing frameworks predominantly rely on historical data and linear statistical models, which increasingly fail to capture the complex, non-linear dynamics of modern financial systems. This research addresses these limitations by developing an integrated computational framework that combines quantum-inspired optimization, advanced machine learning techniques, and multi-agent simulation to create a more comprehensive and forward-looking stress testing paradigm. The paper introduces three key innovations: quantum annealing algorithms for portfolio optimization under stress conditions, neural ordinary differential equations to model dynamic evolution of financial systems, and a multi-agent reinforcement learning framework that simulates heterogeneous bank behaviors during stress periods.