Building Resilient Banking Systems: AI-Driven Risk Management and Crisis Response

EasyChair Preprint 14353

Abstract

The resilience of banking systems is paramount to maintaining financial stability and preventing systemic crises. Traditionally, banks have relied on a combination of regulatory frameworks, manual risk assessments, and crisis management protocols to ensure stability. However, the increasing complexity of financial markets and the rapid evolution of risk factors necessitate more sophisticated approaches. Artificial Intelligence (AI) and Machine Learning (ML) are emerging as powerful tools for enhancing the resilience of banking systems. This article explores the application of AI and ML in risk management and crisis response, highlighting how these technologies can predict potential risks, automate response strategies, and support decision-making processes. We delve into the specific AI-driven techniques used for risk assessment, such as anomaly detection, predictive analytics, and stress testing, and discuss their integration with traditional banking systems. The article also examines real-world case studies where AI has successfully mitigated risks and managed crises, providing practical insights into its efficacy. Furthermore, we address the challenges of implementing AI in banking, including data privacy concerns, regulatory hurdles, and the need for robust model governance. Through a comprehensive analysis, this paper aims to demonstrate how AI-driven risk management can build more resilient banking systems capable of withstanding future financial disruptions.

Keyphrases: AI-driven risk management, Anomaly detection., Predictive Analytics, Resilient banking systems, crisis response, financial stability, machine learning, stress testing

@booklet{EasyChair:14353,
  author    = {Kayode Sheriffdeen},
  title     = {Building Resilient Banking Systems: AI-Driven Risk Management and Crisis Response},
  howpublished = {EasyChair Preprint 14353},
  year      = {EasyChair, 2024}}