SYSTEMATIC ANALYSIS OF CREDIT SCORING MODEL DEVELOPMENT USING PYTHON

Abstract

The study topic is credit scoring system in Kazakhstan and the role of personal credit reports. Credit scoring is a statistical method used by banks and financial institutions to evaluate a borrower’s creditworthiness. The research includes an analysis of individual and corporate credit reports in Kazakhstan, a comparison of international FICO and VantageScore models, and their impact on credit decisions. The aim of the study is to identify the role of personal credit reports in the lending process, examine borrowers’ financial behavior, and analyze credit score evaluation methods. Objectives: 1) to collect and systematize statistics on credit products and personal credit report usage in Kazakhstan; 2) to compare FICO and VantageScore models; 3) to evaluate the impact of personal credit reports on lending conditions; 4) to apply automated analysis to improve credit decision quality. Results show that most borrowers pay insufficient attention to personal credit reports when obtaining credit. Using a personal credit report improves credit terms and cooperation with banks. Python programming language enabled automated data analysis. In conclusion, personal credit reports are crucial for improving lending efficiency, and the study highlights prospects for increasing financial literacy among the population.