Development of comprehensive frameworks for managing concentration risk in banking portfolios

Posted: Mar 13, 2020

• Dr. Jack Kowalski
• Dr. Laura Laurent
• Dr. Layla Tanaka

Abstract

The management of concentration risk represents one of the most challenging aspects of modern banking portfolio management. Traditional approaches have predominantly focused on establishing concentration limits based on regulatory requirements and historical data analysis. However, these methods often fail to capture the complex, multi-dimensional nature of concentration risk in contemporary financial systems. The 2008 global financial crisis and subsequent market disruptions have highlighted the limitations of conventional concentration risk management frameworks, particularly their inability to anticipate systemic interactions and emergent risk patterns. Concentration risk manifests across multiple dimensions including sectoral exposure, geographic concentration, counterparty dependencies, and temporal clustering of risks. Traditional metrics such as the Herfindahl-Hirschman Index and Gini coefficient provide useful but incomplete pictures of concentration dynamics. These measures typically assume linear relationships and fail to account for the complex network effects and feedback loops that characterize modern financial markets. Furthermore, existing frameworks often treat different types of concentration risk in isolation, neglecting the critical interactions between various risk dimensions. This research addresses these limitations by developing a comprehensive computational framework that integrates quantum-inspired optimization algorithms with advanced machine learning techniques. Our approach represents a fundamental departure from traditional concentration risk management by incorporating dynamic, adaptive mechanisms that respond to changing market conditions and emerging risk patterns. The framework employs graph neural networks to model the complex interdependencies within banking portfolios and uses quantum annealing principles to optimize risk distributions across multiple objectives. The novelty of our approach lies in its ability to capture non-linear dependencies, tail risks, and systemic interactions that conventional methods overlook. By developing the Quantum-Enhanced Concentration Index (QECI), we introduce new possibilities for understanding and managing concentration risk in banking portfolios.