AI and Machine Learning in Credit Risk Assessment: Enhancing Accuracy and Efficiency

EasyChair Preprint 14352

Abstract

Credit risk assessment is a critical process in banking, aimed at evaluating the likelihood of a borrower defaulting on their obligations. Traditional methods, such as credit scoring models and manual financial analysis, often face limitations in terms of accuracy, efficiency, and the ability to handle large and diverse datasets. The integration of Artificial Intelligence (AI) and Machine Learning (ML) into credit risk assessment processes has the potential to revolutionize the field by enhancing predictive accuracy, automating repetitive tasks, and uncovering complex patterns within data. This article provides an in-depth exploration of how AI and ML are being applied to credit risk assessment, discussing the benefits and challenges of these technologies. We present various AI and ML techniques used in credit risk modeling, including supervised and unsupervised learning algorithms, ensemble methods, and natural language processing. Additionally, we review case studies that highlight successful implementations of AI and ML in credit risk assessment. The article concludes with a discussion on future trends and the implications of AI-driven credit risk assessment for the banking industry, emphasizing the need for ethical considerations and regulatory compliance.

Keyphrases: Artificial Intelligence, Banking, Data Science., Default Prediction, Financial Technology, Predictive Analytics, credit risk assessment, machine learning, risk modeling

@booklet{EasyChair:14352,
  author    = {Kayode Sheriffdeen},
  title     = {AI and Machine Learning in Credit Risk Assessment: Enhancing Accuracy and Efficiency},
  howpublished = {EasyChair Preprint 14352},
  year      = {EasyChair, 2024}}