Enhancing Credit Risk Management in Banking Through AI and Machine Learning

EasyChair Preprint 14355

Abstract

Credit risk management is a fundamental component of the banking industry, aimed at minimizing potential losses arising from borrowers defaulting on their loans. Traditional credit risk assessment methods, such as credit scoring models and financial statement analysis, often rely on historical data and can be limited by biases and inconsistencies in judgment. The advent of Artificial Intelligence (AI) and Machine Learning (ML) presents a transformative opportunity to enhance credit risk management by leveraging advanced data processing and predictive capabilities. This article explores the integration of AI and ML technologies with traditional risk assessment methods, highlighting their ability to process vast amounts of data with greater accuracy and speed. We discuss the benefits of these technologies, including improved predictive accuracy, efficiency, and the ability to capture complex, non-linear relationships within the data. Additionally, the article examines the challenges associated with implementing AI and ML in credit risk management, such as data quality issues, model interpretability, and regulatory considerations. Through detailed analysis and case studies, we demonstrate how AI and ML are revolutionizing credit risk assessment and shaping the future of banking. The paper concludes with insights into future directions and the potential of AI-driven innovations to further enhance credit risk management in the financial sector.

Keyphrases: Artificial Intelligence, Banking, Credit Risk Management, Financial Technology, Predictive Analytics, default prediction., machine learning, risk assessment

@booklet{EasyChair:14355,
  author    = {John Owen},
  title     = {Enhancing Credit Risk Management in Banking Through AI and Machine Learning},
  howpublished = {EasyChair Preprint 14355},
  year      = {EasyChair, 2024}}