Machine Learning for Credit Risk Assessment in Banking: AnOverview

Abstract

In 2019, A European bank named Deutsche Bank adopted a machine learning model into its system. This resulted in 20% within the first year, showcasing the transformative potential of artificial intelligence in the credit risk department.

The evolution of machine learning models from simple statistical models to complex machine learning algorithms capable of analyzing vast amounts
of datasets with high accuracy. Early machine models relied upon logistic and linear regression, but the modern approach utilizes decision trees, neural networks, and ensemble methods to enhance prediction power and reliability.

This paper will talk about advancements in machine learning techniques for credit risk assessment, the benefits and challenges of integrating these models in traditional banking systems, and the emergence of these technologies in the future. It explores various algorithms, highlighting their applications and effectiveness in our daily lives. Additionally, regulatory and ethical implications are examined to provide a comprehensive overview of the post.