Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/14685
Title: Credit Risk Model Based on Central Bank Credit Registry Data
Authors: Fisnik Doko
Kalajdziski, Slobodan 
Mishkovski, Igor 
Keywords: credit risk; credit registry; data science
Issue Date: 23-Mar-2021
Publisher: Multidisciplinary Digital Publishing Institute
Project: Разбирање на природни јазици во информации од вести преку користење на Трансформер архитектурата и каузални декодери
Journal: J. Risk Financial Manag.
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
URI: http://hdl.handle.net/20.500.12188/14685
DOI: 10.3390
Appears in Collections:Faculty of Computer Science and Engineering: Journal Articles

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