Determinants of Credit Ratings and Comparison of the Rating Prediction Performances of Machine Learning Algorithms

Abstract

In the literature, new machine learning algorithms are dynamically produced in the field of artificial intelligence engineering and the algorithms are constantly updated with new parameter estimations. The performance of existing algorithms in various business areas is still an important topic of discussion. Also, machine learning algorithms are frequently used in long-term credit ratings, which is an crucially important sub-branch of finance. This study was conducted to determine which popular machine learning model performs better in credit scoring. Artificial Neural Network, Random Forest, Support Vector Machine and K Nearest Neighbor were used to determine the algorithm that is suitable for the structure, attribute content and distribution of the data, and the operating logic of the models. In the study, the long-term credit rating is the target variable and the remaining variables are the features, the prediction performances of these 4 algorithm, which are frequently used in previous studies such as credit rating, credit risk, fraud analysis were compared. After data preprocessing, a classification study was carried out using the features included in the model. The metrics used in the comparison are MSE, RMSE, MAE and accuracy. According to the metrics, RF algorithm showed the best performance in the credit scoring.