CONSTRUCTION AND EVALUATION OF COLLECTOR SCORING MODELS BASED ON DEDUCTOR ANALYTICAL PLATFORM
This article deals with collector scoring intellectual models constructing and evaluating. It states that currently a major problem for banks is the problem of loans repayment. To improve collector scoring efficiency, we actualize the need in modern information technology use based on data mining methods and algorithms and the collection scoring models construction. The paper considers the theory of credit scoring and provides a scoring model types analysis. It also describes the stages of collector scoring intelligent models building. The Deductor analytical platform was chosen as the modeling environment. The initial data for the scoring models construction was a sample containing data on bank borrowers who became debtors and had arrears. The source data file contained the following fields: borrower code, loan amount, loan period, monthly payment, borrower's age, borrower's gender, overdue debt period, etc. In total, the sample contained 14 different fields characterizing borrowers. The sampling included 1,724 entries. Based on the initial data analysis, we built the models of decision tree, neural network, and logistic regression. The accuracy of the decision tree model at the training stage was 88.92%, the neural network model - 98.78% and logistic regression - 96.39%. To test the models built, a sample of 575 entries was formed. At the testing stage, the constructed models accuracy was estimated, and the first and second kind errors were calculated. The decision tree model accuracy at the testing stage was 70.09%. At the same time, the first kind error of this model was 10.78%, and the second kind error - 19.13%. Accordingly, the neural network model accuracy when testing was 85.28%, the first kind error was 5.57%, the second kind error was 9.22%. The logistic regression model accuracy o on testing data was 64.35%, the first kind error was 12.17%, the second kind error was 23.48%. Scoring models testing in general has shown good results. However, the neural network model showed the lowest error rate of the first and second kind and, accordingly, the highest classification accuracy. This model accuracy can be improved through the use of a team of models, in particular, through the development of a model of a team of neural networks. This determines the advanced research direction of in the considered subject area.