• The Korean Journal of Applied Statistics
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• v.25 no.2
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• pp.261-267
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• 2012

In constructing a credit scorecard, each characteristic variable is divided into a few attributes; subsequently, weights are assigned to those attributes in a process called coarse classification. While partitioning a characteristic variable into attributes, one should determine appropriate cutpoints for the partition. In this paper, we propose a cutpoint selection method via penalization. In addition, we compare the performances of the proposed method with classification spline machine (Koo et al., 2009) on both simulated and real credit data.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.549-561
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• 2021

This study aims to identify the role of organizational behavior and intellectual capital on risk management implementation and Village Credit Institutions (called LPD) performance. The LPD population is 1,256 units spread across nine districts/cities in Bali. This research was conducted at the LPD as the only microfinance institution based on local wisdom in traditional villages in Bali Province, Indonesia. Based on sampling using the Slovin method, there were 139 LPD as sampled in this study. The respondent in this study was the Head of the LPD. LPD performance measurement is using the balanced scorecard method that combines financial and non-financial aspects. This study also investigates risk management's role as a mediator in the relationship between organizational behavior and intellectual capital on the LPD performance. Methods of data collection using a survey. The questionnaire was given to 139 LPD chairman who was respondents in this survey. The data analysis technique used SEM-PLS. This study succeeded in confirming Resource-Based View Theory that organizational behavior and intellectual capital affect risk management and organization performance. These results also prove risk management's role as a mediation for the relationship between organizational behavior and intellectual capital on organizational performance.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1225-1235
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• 2010

The coarse classifying procedure in credit scoring splits the values of a continuous characteristic into bands and the values of a discrete characteristic into groups of values. Also, the scorecard degrades over time and thus we should adjust the cut-off score being used. However, the coarse classifying and the adjustment of cut-off score in credit scoring are very complicate and troublesome procedure. Thus, in this paper, we develop a software for the coarse classifying and the model evaluation by using Visual Basic Language. By using the developed software, we can find the best split in the coarse classifying and the optimal cut-off score in the model evaluation.

• 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.