Advanced techniques for optimizing banking capital allocation across business units

Posted: Mar 25, 2018

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

Abstract

The optimization of capital allocation across business units represents one of the most critical challenges facing modern financial institutions. Traditional approaches to capital allocation in banking have predominantly relied on risk-weighted asset calculations and linear programming techniques that fail to capture the complex, dynamic nature of financial markets and the intricate interdependencies between different banking operations. These conventional methods, while providing a foundation for regulatory compliance, often result in suboptimal capital deployment, reduced profitability, and inadequate risk management. The limitations of existing capital allocation frameworks become particularly apparent during periods of market stress, where the static nature of these models prevents rapid adaptation to changing conditions. This research addresses these limitations by introducing a revolutionary quantum-inspired optimization framework that fundamentally transforms how banks allocate capital across their business units. Our approach moves beyond the constraints of classical optimization methods by incorporating principles from quantum computing and deep reinforcement learning to create a dynamic, adaptive capital allocation system. The novelty of our methodology lies in its ability to model the complex entanglement of risks across business units, a phenomenon that conventional models systematically overlook.