Revolutionizing Financial Risk Management: A Quantum Computing Approach for Precision and Efficiency

Abstract

The increase in complexity and volatility in global markets is a penalty that puts great pressure on financial risk management. The use of classical computing-based risk assessment models leads to infeasible solutions with high scalability, accuracy, and computational efficiency risks, thereby failing to provide sufficiently accurate solutions for uncertainty regarding financial risk. This research overcomes this limitation through a Quantum enhanced stochastic risk modeling (QSRM), that unites quantum computing to stochastic differential equations, Monte Carlo simulations, and reinforcement learning. Quantum superposition and entanglement are used in the QSRM framework to do the parallel risk scenario analysis for financial forecasting and portfolio optimization, which dramatically increases its accuracy over conventional methods. QEMC, which means Quantum Enhanced Monte Carlo Simulation, is a quick way to settle the risk; QVRA, which is short for Quantum Variational Risk Assessment, is a method to perform adaptive stress testing and QCRL, which stands for Quantum Classical Reinforcement Learning, is how we develop the dynamic hedging technique. Experimental results show that QSRM offers comparable accuracy, good speed, and flexibility to market fluctuations. Additionally, it is based on a hybrid quantum-classical approach and thus remains practically feasible for guidance on real-time financial decision-making. Due to the maturity in hardware and adoption of QSRM, the risk assessment can provide a sustainable, scalable, and robust solution for global financial stability. In addition to filling a current need in the ongoing transformation of financial analytics by bridging the gap between traditional risk management and emerging quantum technologies, this research has practical implications on the global organization, from micro organizations (i.e., Islamic higher learning institutions) to macro organizations (i.e., the Federal Reserve and Saudi Arabian Monetary Agency).