Application Analysis of Machine Learning Models in Credit Risk

Abstract

Traditional credit risk assessment models frequently depend on historical data and predefined rules, a limitation that may fail to fully encompass the intricate and dynamic characteristics of credit risk. With the rise of machine learning (ML), credit risk can be assessed more easily according to historical data, and it offers a more dynamic and data-driven approach to credit risk assessment. This essay will provide an overview of significant research advancements made in the last five years. It demonstrates consumers and financial institutions have different preferred ML models. Moreover, using different criteria such as AUC and Brier to evaluate accuracy yields different results. And there is a greater prospect for deep learning. Overall, the advent of machine learning has revolutionised the field of credit risk modelling, offering new insights and methodologies for predictive analytics. These advancements utilise statistical, machine learning, and deep learning methods to tackle credit risk issues. In addition, this essay will explore several obstacles and forecast future trends.