Pioneering AI Approaches to Managing Bank Credit Risk

EasyChair Preprint 14308

Abstract

This article explores pioneering artificial intelligence (AI) approaches in the management of bank credit risk. Traditional credit risk assessment methods often fall short in addressing the complexities of modern financial environments, necessitating the adoption of advanced AI techniques. This study examines various AI methodologies, including machine learning algorithms, neural networks, ensemble methods, and deep learning frameworks, to evaluate their efficacy in predicting credit defaults and assessing borrower creditworthiness. Through a comparative analysis of these techniques, the research highlights their strengths, challenges, and potential for revolutionizing credit risk management. The findings indicate that AI-driven approaches offer significant improvements in predictive accuracy and decision-making efficiency, although challenges related to data quality, model interpretability, and regulatory compliance remain. This article provides valuable insights into the future of AI in credit risk management and outlines directions for further research.

Keyphrases: Artificial Intelligence (AI), Credit Risk Management, Financial Risk Management, Predictive Analytics, credit default prediction, deep learning, ensemble methods, machine learning, neural networks, risk assessment

@booklet{EasyChair:14308,
  author    = {Adeoye Ibrahim},
  title     = {Pioneering AI Approaches to Managing Bank Credit Risk},
  howpublished = {EasyChair Preprint 14308},
  year      = {EasyChair, 2024}}