Credit Scoring on Cash Flow Table with Machine Learning: XGBoost Approach

Machine learning methods have started being used with greater momentum in the banking and finance sectors alongside modernization and globalization. The ability to distinguish between good and bad customers has become extremely important, especially with the increase in credit products offered in the banking sector. This ability to distinguish not only increases banks’ profitability but also increases their competitiveness in the market. In this context, banks put companies through a credit evaluation process before loaning to them, and the most important leg of this process is undoubtedly the credit score analysis. Considering that one of the most important risks banks carry is credit risk, the importance of correctly, reliably, and quickly completing the balanced scorecard study during the credit evaluation process cannot be denied. Whether the company undergoing a scorecard study is an independent company or part of a group of companies may change how the company or firms are evaluated. In a group of companies, no matter how good a rating one company has in regard to its status within the parent company, if the other companies have low ratings, this may affect and reduce the consolidated rating. In this context, the current study focuses on groups of companies. The aim of the study is to try to develop a scorecard model using the cash flow statements of consolidated companies. In this study, eXtreme Gradient Boosting (XGBoost), Gradient Boosting and Artificial Neural Network algorithms which are machine learning techniques and Python program were used. These three methods were compared, and the extreme gradient boosting method was shown to be the preferred model with an accuracy rating of 80%.