A Comparative Study of Deep Learning and Machine Learning Techniques in Credit Score Classification

Abstract

The field of credit score classification has experienced notable progress through the introduction of deep learning (DL) and machine learning (ML) techniques, empowering financial institutions to make well-informed decisions about assessing creditworthiness. However, existing research often focuses on just a few classifiers pertaining to either ML or DL techniques, lacking a comprehensive comparative analysis between the two. This gap calls for a thorough study that evaluates and compares a wide range of ML classifiers and DL models in the context of credit scoring. Our work aims to address this limitation by presenting an extensive comparative analysis between different ML and DL approaches. We provide novel insights into the strengths and weaknesses of each model, enabling financial institutions to select the most suitable approach for their specific needs. Through conducting extensive experiments on a credit records dataset, we evaluated the accuracy, precision, recall, and F1 score of various ML classifiers, such as logistic regression, decision trees, and random forests. Additionally, we delved into the capabilities of DL models, which included multi-layer perceptron (MLP), convolutional neural networks (CNN), recurrent neural networks (RNN), and hybrid models. Our findings revealed that Random Forest achieved the highest test accuracy of 90.27, while MLP and CNN closely followed with the second-highest accuracies at 87.08 and 87.16, respectively. These results also demonstrated the potential of both MLP and CNN in credit scoring assessment. MLP's strength lies in its capacity to handle non-linear relationships between features, providing a viable alternative to decision tree-based models. On the other hand, CNN excels in capturing spatial patterns and dependencies among features, presenting a distinct advantage in credit score classification. Overall, our study presents a broad-spectrum overview of the analysis, encompassing each model's performance and effectiveness in credit score classification. The findings empower financial institutions to leverage the benefits of DL and ML techniques, optimizing their decision-making processes and enhancing risk management strategies. By selecting the most suitable credit score classification model based on the insights gained from this comparative analysis, institutions can make informed choices and effectively evaluate creditworthiness, leading to improved risk assessment and lending decisions.