AI-Driven Optimization Techniques for Enhanced Risk Management in Global Banking

EasyChair Preprint 14356

Abstract

In the rapidly evolving global banking landscape, managing risk effectively is crucial for maintaining financial stability and competitive advantage. Traditional risk management approaches are increasingly being supplemented or replaced by advanced AI-driven optimization techniques that offer new possibilities for enhancing risk mitigation strategies. This article explores how AI-driven optimization techniques are transforming risk management in global banking by providing more accurate, efficient, and adaptive solutions. We delve into various AI methods, including machine learning algorithms, natural language processing, and reinforcement learning, and their applications in optimizing risk management processes. The discussion covers the benefits of AI in improving risk prediction accuracy, operational efficiency, and decision-making capabilities. Additionally, the article addresses challenges related to data quality, algorithmic transparency, and regulatory compliance. Through case studies and empirical evidence, we demonstrate the impact of AI-driven optimization on global banking risk management and suggest future research directions for further advancements.

Keyphrases: AI-driven optimization, Global Banking, Natural Language Processing, Reinforcement Learning, algorithmic transparency., financial stability, machine learning, risk management

@booklet{EasyChair:14356,
  author    = {Alakitan Samad},
  title     = {AI-Driven Optimization Techniques for Enhanced Risk Management in Global Banking},
  howpublished = {EasyChair Preprint 14356},
  year      = {EasyChair, 2024}}