• Asia-Pacific Journal of Business
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• v.11 no.3
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• pp.63-77
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• 2020

Purpose - The purpose of this study is to examine the effects of personal characteristics, loan characteristics, and interest rate characteristics of 2,653 borrowers on the delinquency possibility. In doing so, this study applies both multiple regression and logistic regression models to the data of credit unions in the city of Daegu. Design/Methodology/Approach - The major results of multiple regression analysis using SPSS are as follows. Findings - As for the results of testing the significance of the regression coefficients, it has been found that among the personal characteristics variables membership, credit rating, credit rating changes, and LTV have significant positive (+) effects on the delinquency possibility. Also it has been shown that among the loan characteristics variables loan amount, loan balance, total debt amount, collateral type, collateral amount, and repayment method have significant positive (+) effects on the delinquency possibility. Furthermore it has been found that among the interest rate characteristics variables both overdue interest rate and interest rate spread have positive (+) effects on the delinquency possibility. However, it has been shown that among the personal characteristics variables equity and membership do not have significant effects on the delinquency possibility, and that normal interest rate among the interest rate characteristics variables also do not have a significant effect on the delinquency possibility. Research Implications - By systematically analyzing the variables affecting delinquency possibility based on the results of this study, credit unions might get positive help in improving the system of managing receivables. Furthermore, the results of this study could be extended and applied to other types of financial institutions, so that financial institutions in general will also get some help to systematically manage the delinquency possibility.

• The Journal of the Korea Contents Association
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• v.18 no.4
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• pp.416-427
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• 2018

As the loan size of real estate PF is huge, its market ripple effect gets bigger when overdue occurs. Accordingly, the management of the delinquency rate and macroeconomic analysis are required. As the preceding research mainly proceeded with microeconomic analysis through the real estate PF data of individual banks to evaluate importance of list or analyzed core factors for delinquency, it lacked research on comprehensive real estate PF size. In order to overcome the limitations of such data, this research studied real estate PF delinquency rate of the entire market and effect relationship by the size. The research utilized the size of real estate PF loans, money supply, interest rate, consumer price index(CPI), and GDP data. Also, it applied the first model of VECM as linear relationship between at least two or more variables, following the result of co-integration test. As a result of Granger-causality test, the real estate PF loans delinquency rate is influenced by their loan size, and as a result of impulse response analysis, the interest rate is shown to be affecting delinquency rate the most. Interest rate could risesomeday and aggravate the delinquency rate of real estate PF. Also, risk exposure could be serious as the loan size increases.Therefore, the management of real estate PF delinquency rate requires continuous monitoring, tracking and observing issued loans from a macro point of view. The plans to prevent delinquency will be necessary.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1293-1305
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• 2016

In this paper, we develop debt collection predictive models for the person in arrears by utilizing the direct loan data of the Korea Student Aid Foundation. We suggest credit risk scorecards for overdue student direct loan using the developed 3 models. Model 1 is designed for 1 month overdue, Model 2 is designed for 2 months overdue, and Model 3 is designed for overdue over 2 months. Model 1 shows that the major influencing factors for the delinquency are overdue account, due data for payment, balance, household income. Model 2 shows that the major influencing factors for delinquency loan are days in arrears, balance, due date for payment, arrears. Model 3 shows that the major influencing factors for delinquency are the number of overdue in recent 3 months, due data for payment, overdue account, arrears. The debt collection predictive models and credit risk scorecards in this study will be the basis for segmented management service and the call & collection strategies for preventing delinquency.

• Journal of Intelligence and Information Systems
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• v.9 no.2
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• pp.135-154
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• 2003

Credit scoring system (CSS) starts from an analysis of delinquency trend of each individual or industry. This paper conducts a research on credit card delinquency of bank customers as a preliminary step for building effective credit scoring system to prevent excess loan or bad credit status. To serve this purpose, we use association rules as a rule generating data mining technique. Specifically, we generate sets of rules of customers who are in bad credit status because of delinquency by association rule mining. We expect that the sets of rules generated by association rule mining could act as an estimator of good or bad credit status classifier and basic component of early warning system.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.149-154
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• 2003

Credit scoring system (CSS) starts from an analysis of delinquency trend of each individual or industry. This paper conducts a research on credit card delinquency of bank customers as a preliminary step for building effective credit scoring system to prevent excess loan or bad credit status. To serve this purpose, we use association rules that ore generating method. Specifically, we generate sets of rules of customers who are in bad credit status because of delinquency by using association rules. We expect that the sets of rules generated by association rules could act as an estimator of good or bad credit status classifier.

• KDI Journal of Economic Policy
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• v.37 no.3
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• pp.55-74
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• 2015

This study examines the effect of a one-on-one outsourced pre-lending consulting service on the repayment behavior of microcredit borrowers in Korea with administrative data from the Smile Microcredit Bank. A random change in the cut-off loan amount for mandatory consulting is utilized as an identification strategy. This three-day pre-lending business consulting service is effective in encouraging repayment behavior of existing businesses but it has no significant effect on start-up loans. The effectiveness of the consulting service in deterring delinquency with regard to existing loans is greater among male borrowers than among females.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.30-45
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• 2024

This study introduces an effective method for predicting individual local tax delinquencies using prevalent machine learning and deep learning algorithms. The evaluation of credit risk holds great significance in the financial realm, impacting both companies and individuals. While credit risk prediction has been explored using statistical and machine learning techniques, their application to tax arrears prediction remains underexplored. We forecast individual local tax defaults in Republic of Korea using machine and deep learning algorithms, including convolutional neural networks (CNN), long short-term memory (LSTM), and sequence-to-sequence (seq2seq). Our model incorporates diverse credit and public information like loan history, delinquency records, credit card usage, and public taxation data, offering richer insights than prior studies. The results highlight the superior predictive accuracy of the CNN model. Anticipating local tax arrears more effectively could lead to efficient allocation of administrative resources. By leveraging advanced machine learning, this research offers a promising avenue for refining tax collection strategies and resource management.

• Information Systems Review
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• v.23 no.1
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• pp.69-91
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• 2021

Credit Bureaus in Korea commonly use financial transaction information of the past and present time for calculating an individual's credit scores. Compared to other rating factors, the repayment history information accounts for a larger weights on credit scores. Accordingly, despite full redemption of overdue payments, late payment history is reflected negatively for the assessment of credit scores for certain period of the time. An individual with debt delinquency can be classified into two groups; (1) the individuals who have faithfully paid off theirs overdue debts(Normal Repayment), and (2) those who have not and as differences of creditworthiness between these two groups do exist, it needs to grant relatively higher credit scores to the former individuals with normal repayment. This study is designed to analyze the factors of normal repayment of Korean financial debt delinquents based on credit information of personal loan, overdue payments, redemption from Korea Credit Information Services. As a result of the analysis, the number of overdue and the type of personal loan and delinquency were identified as significant variables affecting normal repayment and among applied methodologies, neural network models suggested the highest classification accuracy. The findings of this study are expected to improve the performance of individual credit scoring model by identifying the factors affecting normal repayment of a financial debt delinquent.

• KDI Journal of Economic Policy
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• v.43 no.3
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• pp.1-32
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• 2021

With CB data in South Korea, this study examines whether the credit risk of borrowers changes when the regulation on bank mortgage supply is relaxed. We analyze the effect of deregulation on LTV and DTI limits in the Seoul-metropolitan area in August 2014 with a difference-in-difference approach. We find that the probability of delinquency is lower in the Seoul metropolitan area after the deregulation than in other urban areas. The effect is noticeable among low-income and low-credit borrowers. We also find that borrowers change their debt structure to reduce the interest costs utilizing their improved access to bank mortgages. The findings suggest the necessity to consider the burden of the high interest costs of unsecured loans for debtors with low incomes and low credit ratings in designing housing finance regulations.

• Journal of information and communication convergence engineering
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• v.19 no.4
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• pp.228-233
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• 2021

In this study, we analyze the credit information (loan, delinquency information, etc.) of individual business owners to generate voluminous training data to establish a bankruptcy prediction model through a partial synthetic training technique. Furthermore, we evaluate the prediction performance of the newly generated data compared to the actual data. When using conditional tabular generative adversarial networks (CTGAN)-based training data generated by the experimental results (a logistic regression task), the recall is improved by 1.75 times compared to that obtained using the actual data. The probability that both the actual and generated data are sampled over an identical distribution is verified to be much higher than 80%. Providing artificial intelligence training data through data synthesis in the fields of credit rating and default risk prediction of individual businesses, which have not been relatively active in research, promotes further in-depth research efforts focused on utilizing such methods.