Development of advanced models for banking sector scenario analysis and planning

Posted: Jul 04, 2022

• Dr. Prof. Isaac Singh
• Dr. Prof. Isabella Costa
• Dr. Prof. Isabella Rossi

Abstract

The global banking sector faces unprecedented challenges in an era characterized by rapid technological transformation, evolving regulatory landscapes, and increasing systemic complexity. Traditional approaches to banking scenario analysis, predominantly reliant on statistical methods and conventional computational techniques, have demonstrated significant limitations in predicting and preparing for disruptive financial events. The 2008 global financial crisis, followed by more recent market volatilities and the COVID-19 pandemic economic impacts, have highlighted the critical need for more sophisticated, adaptive, and comprehensive modeling frameworks capable of capturing the intricate dynamics of modern financial systems. Current banking scenario analysis methodologies primarily employ linear regression models, autoregressive integrated moving average (ARIMA) techniques, and various implementations of Monte Carlo simulations. While these approaches have served the industry for decades, they increasingly struggle to account for the non-linear relationships, emergent behaviors, and complex interdependencies that characterize contemporary financial markets. The limitations become particularly evident during periods of market stress, where traditional models often fail to capture the cascading effects and feedback loops that amplify systemic risks. This research addresses these challenges through the development of an innovative computational framework that integrates principles from quantum computing, multi-agent systems, and deep reinforcement learning. Our approach represents a paradigm shift in banking scenario analysis by moving beyond conventional statistical methods to embrace computational techniques that more accurately reflect the complex, adaptive nature of financial systems. The framework is designed to capture both micro-level banking operations and macro-economic indicators through a unified modeling architecture that enables more robust stress testing, capital adequacy planning, and strategic decision-making.