Abstract
The Central Bank Credit Registry was established in Ukraine in 2018. The two key functions which are fulfilled by Credit Register are monitoring and credit information sharing. This paper is devoted to applying a scoring approach for monitoring function realization in segments of individuals. The logic of using scoring tools to monitoring is based on an objective to create an effective form which reflects the dynamic of the above-mentioned segment. Data mining procedures for Credit Registry were realized and most significant characteristics were chosen. Correlation analysis for characteristics was applied. Different approaches to construct scoring for monitoring functions were analyzed. Namely, logistic regression, Machine Learning, method grounded on tree created by the XGBoost algorithm. Last method demonstrated the best efficiency for scoring construction and can be developed for implementation. The views expressed are those of the authors and do not necessarily reflect those of the National Bank of Ukraine. JEL classіfіcatіon: G21
Highlights
Introduction and research problemFinancial stability represents one of the most important components of an economy healthy development
One of the main reasons for this instability was the high level of non-performing loans (NPL) in the banking system accumulated over the years
Correlation between indicators According to Credit Registry data, we have several indicators that we explored in the previous section
Summary
The step of the selection procedure is stepwise selection. There are forward stepwise selection and backward elimination selection. We chose first specification, which we call Model 1: Default ~ Dummy_Unproved_income +. According to the stepwise selection, we excluded the variable Dummy_UAH_notUAH. This indicator had a lower information value. To test the best specification, we added a quadratic form for each variable and paid attention to 1) the significance of variables, 2) the AIC of the model. We chose the tuning version of Model 1 with a quadratic form of Interest rate, Proved income, and Credit Risk. In this specification, the model reflects most of our intuitive expectations. The low interest rate could be the reason for overheating
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