• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.239-256
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• 2020

Health is an important issue due to increased life expectancy. As a result, the demand for industry and services associated with individual health, health-related programs and services will be facilitated by a method to evaluate and classify the health level of an individual based on various factors. This study suggests a methodology to measure and score an individual health level. A credit scoring model was introduced to implement the categorization of variables, construct a prediction model, and to score individual health level. Cohort DB provided by National Health Insurance Service was used to illustrate overall procedures. It is expected that the suggested model can be utilized in designing and managing health care services as well as other health-related programs.

• The Journal of Fisheries Business Administration
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• v.39 no.1
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• pp.43-61
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• 2008

Excessive public loan with low interest and other tax benefits have been provided for fishermen, but much of them turned out to be little performed. There were the moral hazards of Suhyup in the process of executing the public loans. As the government gave the reimbursement on the financial loss of Suhyup resulting from the public loans, Suhyup had no responsibility of the bad debt loss. Therefore, Suhyup gave little efforts to reduce the non-performing. The government perceived this problem and tried to reduce the under-performing loans. Thus, the government decided to take limited responsibilities. Suhyup made the progress to reduce the under-performing public loans. Suhyup dealt with these situation and made the credit evaluation model of the fisherman's public loan. This paper is for the credit evaluation model in the fisherman's public loan, which explains the model development methodology and the model characteristics in detail. This evaluation model is composed of two sub-component model. the one is the quantitative model and the other is the qualitative model. The quantitative sub-model is for the identification of fishermen financial status and is based on the financial transaction information. Its development methodology is the CSS modeling for the consumer market. The qualitative sub-model is for the evaluation the business prospect and is based on the business information such as fisherman's management skills, technology, equipment. Its development methodology is the AHP. It provides the detailed information in the model development methodology, which is the ideal example such as the public loan. In addition it gives the information to the interest parties such as policy makers, suhyup and fishermen.

• Journal of the Korean Data and Information Science Society
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• v.23 no.2
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• pp.235-245
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• 2012

Logistic discrimination is an useful statistical technique for quantitative analysis of financial service industry. Especially it is not only easy to be implemented, but also has good classification rate. Generalized additive model is useful for credit scoring since it has the same advantages of logistic discrimination as well as accounting ability for the nonlinear effects of the explanatory variables. It may, however, need too many additive terms in the model when the number of explanatory variables is very large and there may exist dependencies among the variables. Mixtures of factor analyzers can be used for dimension reduction of high-dimensional feature. This study proposes to use the low-dimensional factor scores of mixtures of factor analyzers as the new features in the generalized additive model. Its application is demonstrated in the classification of some real credit scoring data. The comparison of correct classification rates of competing techniques shows the superiority of the generalized additive model using factor scores.

• 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 Information Systems
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• v.30 no.4
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• pp.203-226
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• 2021

Purpose The purpose of this study is to enhance the accuracy score of personal credit scoring using the convolutional neural networks and secure the transparency of the deep learning model using eXplainalbe Artifical Inteligence(XAI) technique. Design/methodology/approach This study built a classification model by using the convolutional neural networks(CNN) and applied a methodology that is transformation of numerical data to imaged data to apply CNN on personal credit data. Then layer-wise relevance propagation(LRP) was applied to model we constructed to find what variables are more influenced to the output value. Findings According to the empirical analysis result, this study confirmed that accuracy score by model using CNN is highest among other models using logistic regression, neural networks, and support vector machines. In addition, With the LRP that is one of the technique of XAI, variables that have a great influence on calculating the output value for each observation could be found.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.399-406
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• 2011

Various construction bonds and warranties critically burden the general contractor. Also, sporadic or cumulative delays of progress payment by the owner can further trap the contractor in a financial quagmire. Facing the possibility of cash flow deficiency and callous response from the banks, most construction firms may become financially incapable of market competition, and attractive project tenders become a bidding game among few deep-pocket players. The downside of such market environment is that the depth of pocket, rather than that of professional competency dictates the choice of market winners. In Taiwan, this has been a potential crisis to the construction industry after the financial crisis which started out since 2008. To encounter this problem, this research will examine the means to better manage the construction industry. Essentially, a credit guarantee system (CGS) is the prime solution to strengthen a bank's confidence in any particular construction firm. Thus establishing a national platform which evaluates and rewards a construction firm's overall credibility is pivotal, and this third-party rated credit can help a bank to render a loan more wisely. Finally, this paper will propose the ideal operating schemes of construction-specific CGS in Taiwan and a credit scoring prototype model for construction industry, as reference for the government and banks, respectively.

• Journal of the Economic Geographical Society of Korea
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• v.19 no.2
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• pp.265-285
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• 2016

This paper analyses the regional financial market of Kyungbuk-East Coast region. The result shows that the credit unions do not do much for easing the credit constraints of small firms in the region. Many papers suggest that it is necessary for them to adhere closely to the regional economy. But they do not do their best to collect borrowers' private informations. Instead they rely on the credit scoring system to assess their creditworthiness and require collaterals to reinforce their weak credits. That is the real root of weak competitiveness of credit unions. To overcome such a problem they need to actively participate in the development of the regional economy, bearing in mind the cooperative principles, especially commitment for the community. On the other hand the government should contrive plans to foster them. When they function actively the regional financial market will become efficient and the regional economy grow smoothly.

• The Journal of Information Systems
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• v.16 no.1
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• pp.1-21
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• 2007

신용평점을 위한 부도예측의 분류 문제를 다루는데 있어서 통계적 판별분석 및 인공신경망 및 유전자알고리즘 등을 이용한 데이터 마이닝의 방법들이 일반적으로 고려되어왔다. 이 연구에서는 수리계획법을 응용하여 classification gap을 고려한 이단계 수리계획 접근방법을 신용평가에 적용하는 방법론을 제안하여 수리계획법을 통한 신용평가모형 구축의 가능성을 제시한다. 1단계에서는 선형계획법을 이용해서 대출 신청자에게 대출을 허가할 것 인지의 여부를 결정하게 되는 대출 심사 filtering으로의 적용단계이고, 2단계에서는 정수계획법을 이용하여 오분류 비용이 최소가 되도록 하는 판별점수를 찾는 과정으로 모형을 구성한다. 개인 대출 신청자의 데이터(German Credit Data)에 대하여 피셔의 선형 판별함수, 로지스틱 회귀모형 및 기존의 수리계획 기법들과의 비교를 통해서 제안된 모델의 성능을 평가한다. 이단계 수리계획 접근법의 평가 결과를 통하여 신용평가모형에의 적용가능성을 기존 통계적인 접근방법 및 수리계획 접근법과 비교하여 제시하고 있다.

• 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.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.509-515
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• 2005

Recently, support vector machines (SVMs) are being recognized as competitive tools as compared with other data mining techniques for solving pattern recognition or classification decision problems. Furthermore, many researches, in particular, have proved it more powerful than traditional artificial neural networks (ANNs)(Amendolia et al., 2003; Huang et al., 2004, Huang et al., 2005; Tay and Cao, 2001; Min and Lee, 2005; Shin et al, 2005; Kim, 2003). The classification decision, such as a binary or multi-class decision problem, used by any classifier, i.e. data mining techniques is cost-sensitive. Therefore, it is necessary to convert the output of the classifier into well-calibrated posterior probabilities. However, SVMs basically do not provide such probabilities. So it required to use any method to create probabilities (Platt, 1999; Drish, 2001). This study applies a method to estimate the probability of outputs of SVM to bankruptcy prediction and then suggests credit scoring methods using the estimated probability for bank's loan decision making.