Development of advanced models for credit portfolio optimization in commercial banking

Posted: Jul 15, 2018

• Dr. Prof. Abigail Costa
• Dr. Prof. Abigail Kumar
• Dr. Prof. Amelia M¨ uller

Abstract

This research introduces a paradigm shift in credit portfolio optimization by developing a quantum-inspired hybrid framework that integrates principles from quantum computing with deep reinforcement learning. Our approach fundamentally rethinks the optimization problem from first principles, moving beyond the constraints of classical computational methods. The Quantum Neural Portfolio Optimizer (QNPO) represents a novel synthesis of quantum annealing techniques and neural network architectures specifically designed for the unique challenges of credit portfolio management. Unlike traditional methods that treat optimization as a static problem, our framework incorporates dynamic learning mechanisms that adapt to changing market conditions and evolving risk profiles.