Credit Risk Assessment Model for UAE Commercial Banks: A Machine Learning Approach

TL;DR

This paper develops a machine learning-based credit risk assessment model for UAE commercial banks, aiming to improve accuracy over traditional methods and help prevent financial losses.

Contribution

It introduces a credit risk model using Linear Discriminant Analysis for dimensionality reduction and evaluates classifiers for better credit assessment accuracy.

Findings

LDA effectively reduces data dimensionality for credit risk analysis.

The model outperforms traditional methods in accuracy and efficiency.

It helps banks identify good and bad credit risks more reliably.

Abstract

Credit ratings are becoming one of the primary references for financial institutions of the country to assess credit risk in order to accurately predict the likelihood of business failure of an individual or an enterprise. Financial institutions, therefore, depend on credit rating tools and services to help them predict the ability of creditors to meet financial persuasions. Conventional credit rating is broadly categorized into two classes namely: good credit and bad credit. This approach lacks adequate precision to perform credit risk analysis in practice. Related studies have shown that data-driven machine learning algorithms outperform many conventional statistical approaches in solving this type of problem, both in terms of accuracy and efficiency. The purpose of this paper is to construct and validate a credit risk assessment model using Linear Discriminant Analysis as a dimensionality reduction technique to discriminate good creditors from bad ones and identify the best classifier for credit assessment of commercial banks based on real-world data. This will help commercial banks to avoid monetary losses and prevent financial crisis