CatBoost model with synthetic features in application to loan risk assessment of small businesses

Loan risk for small business has long been a complex problem worthy of exploring. Predicting the loan risk approximately can benefit entrepreneurship by developing more jobs for the society. CatBoost (Categorical Boosting) is a powerful machine learning algorithm that is suitable for dataset with many categorical variables like the dataset for forecasting loan risk. In this paper, we identify the important risk factors that contribute to loan status classification problem. Then we compare the the performance between boosting-type algorithms(especially CatBoost) with other traditional yet popular ones. The dataset we adopt in the research comes from the U.S. Small Business Administration (SBA) and holds a very large sample size (899,164 observations and 27 features). We obtain a high accuracy of 95.74% and well-performed AUC of 98.59% compared with the existent literature of related research. In order to make best use of the important features in the dataset, we propose a technique named "synthetic generation" to develop more combined features based on arithmetic operation, which ends up improving the accuracy and AUC of original CatBoost model.