Credit Risk Model Based on Central Bank Credit Registry Data

Abstract

Data science and machine-learning techniques help banks to optimize enterprise operations, enhance risk analyses and gain competitive advantage. There is a vast amount of research in credit risk, but to our knowledge, none of them uses credit registry as a data source to model the probability of default for individual clients. The goal of this paper is to evaluate different machine-learning models to create accurate model for credit risk assessment using the data from the real credit registry dataset of the Central Bank of Republic of North Macedonia. We strongly believe that the model developed in this research will be an additional source of valuable information to commercial banks, by leveraging historical data for all the population of the country in all the commercial banks. Thus, in this research, we compare five machine-learning models to classify credit risk data, i.e., logistic regression, decision tree, random forest, support vector machines (SVM) and neural network. We evaluate the five models using different machine-learning metrics, and we propose a model based on credit registry data from the central bank with detailed methodology that can predict the credit risk based on credit history of the population in the country. Our results show that the best accuracy is achieved by using decision tree performing on imbalanced data with and without scaling, followed by random forest and linear regression.

Financial revolution: a systemic analysis of artificial intelligence and machine learning in the banking sector

“…Other authors also develop and compare different models for credit risk assessment: SVM, DTs, AdaBoost, RF, logistic regression, ANN, and XGBoost. Of these, the different models with greater predictive accuracy are obtained by each author: AdaBoost and RF [35], DT [36]- [40]. On the other hand, Putri et al [41] propose to analyze credit risk using SMV.…”

Credit Risk Prediction Based on Psychometric Data

“…Mhlanga [26] discusses the role of machine learning and artificial intelligence in enhancing financial inclusion in emerging economies through more accurate credit risk assessment. Doko et al [27] proposed a credit risk model utilizing central bank credit registry data, reflecting the potential of institutional data in risk modeling. Meanwhile, Ganbat et al [6] explored the impact of psychological factors on credit risk, particularly within the context of microlending services.…”

Banks Credit Risk Prediction with Optimized ANN Based on Improved Owl Search Algorithm

“…Finally, a comparison of the existing methods is presented. In 2021, Doko et al [15] utilized different machine-learning models for generating precise models for credit risk valuation based on the North Macedonia Central Bank. ey compared the results with five machine-learning models including decision tree, logistic regression, artificial neural network, and support vector machines for categorizing the credit risk data.…”

“…(1) Existence or nonexistence of account with a check handle (four various modes) (A1) (2) Monthly turnover rate (A2) (3) Credit history (five various modes) (A3) (4) Target (eleven various modes) (A4) (5) e amount of credit received (A5) (6) Savings account and its amount (five various modes) (A6) (7) e Duration of employment by occupation (five various modes) (A7) (8) Installment ratio in return for assets seized by the bank (A8) (9) Gender and marital status (five various modes) (A9) (10) Debt or previous warranty (three various modes) (A10) (11) Residence status (A11) (12) Assets (four various modes) (A12) (13) Age (A13) (14) Other installments (three various modes) (A14) (15) Housing status (three various modes) (A15) (16) e number of credits in the bank (A16) (17) Job status (four various modes) (A17) (18) e number of Guarantors (A18) (19) e availability of landline phones (A19) (20) Foreign employee (A20)…”