Modification of the Random Forest Algorithm for Credit Scoring and Its Comparison with Gradient Boosting, Random Forest and CART

Credit scoring is one of the oldest applications of analytics where financial institutions perform statistical analysis and machine learning models to assess the creditworthiness of potential borrowers. Through its history lots of techniques have been created for creditworthiness assessment, but creating new models and developing old ones still remains important problem, because even one percent growth in scoring models accuracy can significantly increase the profit of financial institutions. Thus the goal of this research paper is to develop one of the well-known machine learning models, called Random forest and compare it with CART, Gradient boosting and original Random forest on two scoring datasets. The developed model demonstrates worse results than other three models in case of one of the datasets, and better results than only CART in case of other. Thus, the use of this model in case of these two certain datasets is meaningless. Nevertheless, more comparisons on more datasets should be done to understand the effectiveness of the implementation of the model in scoring field. Besides, some suggestions have been done in this research paper, concerning further research and development of the New algorithm.