• Journal of the Korean Data and Information Science Society
• /
• v.26 no.6
• /
• pp.1207-1216
• /
• 2015

This study aims to compare the model power in developing corporate credit rating models and to suggest a good way to build models based on the characteristic of data. Among many measurement methods, AR is used to measure the model power under various conditions. SAS/MACRO is in use for similar repetitions to reduce time to build models under several combination of conditions. A corporate credit rating model is composed of two sub-models; a credit scoring model and a default prediction model. We verify that the latter performs better than the former under various conditions. From the result of size comparisons, models of large size corporate are more powerful and more meaningful in financial viewpoint than those of small size corporate. As a corporate size gets smaller, the gap between sub-models becomes huge and the effect of outliers becomes serious.

• The Korean Journal of Applied Statistics
• /
• v.20 no.3
• /
• pp.573-584
• /
• 2007

A well estimated probability of default is most important for constructing a good credit scoring process. The maximum expected utility (MEU) model has been suggested as an alternative of the traditional logistic regression model. Because the MEU model has been constructed using financial data arising from North America and European countries, the MEU model may not be suitable to Korean private firms. Thus, we propose a Korea-specific MEU model by estimating the parameters involved in kernel functions. This Korea-specific MEU model is illustrated using 34,057 private firms to show the performance of the MEU model relative to the usual logistic regression model.

• Journal of Intelligence and Information Systems
• /
• v.28 no.3
• /
• pp.259-278
• /
• 2022

With the spread of Artificial Intelligence (AI), various AI-based services are expanding in the financial sector such as service recommendation, automated customer response, fraud detection system(FDS), credit scoring services, etc. At the same time, problems related to reliability and unexpected social controversy are also occurring due to the nature of data-based machine learning. The need Based on this background, this study aimed to contribute to improving trust in AI-based financial services by proposing a checklist to secure fairness in AI-based credit scoring services which directly affects consumers' financial life. Among the key elements of trustworthy AI like transparency, safety, accountability, and fairness, fairness was selected as the subject of the study so that everyone could enjoy the benefits of automated algorithms from the perspective of inclusive finance without social discrimination. We divided the entire fairness related operation process into three areas like data, algorithms, and user areas through literature research. For each area, we constructed four detailed considerations for evaluation resulting in 12 checklists. The relative importance and priority of the categories were evaluated through the analytic hierarchy process (AHP). We use three different groups: financial field workers, artificial intelligence field workers, and general users which represent entire financial stakeholders. According to the importance of each stakeholder, three groups were classified and analyzed, and from a practical perspective, specific checks such as feasibility verification for using learning data and non-financial information and monitoring new inflow data were identified. Moreover, financial consumers in general were found to be highly considerate of the accuracy of result analysis and bias checks. We expect this result could contribute to the design and operation of fair AI-based financial services.

• Journal of the Korean Data and Information Science Society
• /
• v.22 no.6
• /
• pp.1017-1028
• /
• 2011

When it is not easy to decide the credit scoring for some loan applicants, credit evaluation is postponded and reserve to ask a specialist for further evaluation of undecided applicants. This undecided inference is one of problems that happen to most statistical models including the biostatistics and sportal statistics as well as credit evaluation area. In this work, the undecided inference is regarded as a missing data mechanism under the assumption of MNAR, and use the bivariate probit model which is one of sample selection models. Two undecided inference methods are proposed: one is to make use of characteristic variables to represent the state for decided applicants, and the other is that more accurate and additional informations are collected and apply these new variables. With an illustrated example, misclassification error rates for undecided and overall applicants are obtainded and compared according to various characteristic variables, undecided intervals, and thresholds. It is found that misclassification error rates could be reduced when the undecided interval is increased and more accurate information is put to model, since more accurate situation of decided applications are reflected in the bivariate probit model.

• The Korean Journal of Applied Statistics
• /
• v.24 no.2
• /
• pp.425-433
• /
• 2011

Reject inference in credit scoring is a statistical approach to adjust for nonrandom sample bias due to rejected applicants. Function estimation approaches are based on the assumption that rejected applicants are not necessary to be included in the estimation, when the missing data mechanism is missing at random. On the other hand, the density estimation approach by using mixture models indicates that reject inference should include rejected applicants in the model. When mixture models are chosen for reject inference, it is often assumed that data follow a normal distribution. If data include missing values, an application of the normal mixture model to fully observed cases may cause another sample bias due to missing values. We extend reject inference by a multivariate normal mixture model to handle incomplete characteristic variables. A simulation study shows that inclusion of incomplete characteristic variables outperforms the function estimation approaches.

• Proceedings of the Korea Society of Information Technology Applications Conference
• /
• 2007.05a
• /
• pp.190-197
• /
• 2007

외환위기 이후 우리나라 금융기관은 상대적으로 위험성이 높은 기업대출보다, 높은 수익성을 가지는 가계 대출에 관심을 기울이게 되었다. 가계대출이 증가함에 따라 개인신용평가의 중요성이 부각되고, 이에 많은 신용평가시스템이 개발되어 왔다. 하지만 기존의 신용평가시스템은 대출 신청 당시의 데이터 및 과거의 데이터를 가지고 개인의 신용을 평가하기 때문에, 미래 상황에 대한 예측은 고려하지 못한다. 시스템 다이나믹스는 시간의 흐름에 따른 각 요인의 변화를 살펴봄으로써 미래 상황에 대한 예측이 가능한 분석 방법이다. 이에 본 연구에서는 시스템 다이나믹스 방법론을 활용하여 개인 신용 상태에 대한 미래의 동태적인 변화를 예측하여, 그 결과를 반영한 신용평가모델을 개발하고자 한다. 이를 위하여, 먼저 신용평점 영향을 주는 변수들을 선정하고, 이 변수들 간의 인과관계를 밝혀낸 후, 인과관계를 토대로 분석 모델을 구축한 뒤, 컴퓨터 시뮬레이션을 실행함으로써, 대출 희망자의 미래의 신용상태 변화 모양을 예측해 본다. 이러한 시뮬레이션 결과를 신용평가에 반영하게 되면, 금융기관의 신용 대출의 위험을 줄이는 데 기여할 것으로 기대된다.

• Journal of Information Science Theory and Practice
• /
• v.6 no.3
• /
• pp.45-60
• /
• 2018

Research performance evaluation in Korean universities follows strict guidelines that specify scoring systems for publication venue categories and formulas for co-authorship credit allocation. To find out how the standards differ across universities and how they differ from bibliometric research evaluation measures, this study analyzed 25 standards from major Korean universities and rankings produced by applying standards and bibliometric measures such as publication and citation counts, normalized impact score, and h-index to the publication data of 195 tenure-track professors of library and information science departments in 35 Korean universities. The study also introduced a novel impact score normalization method to refine the methodology from prior studies. The results showed the university standards to be mostly similar to one another but quite different from citation-driven measures, which suggests the standards are not quite successful in quantifying the quality of research as originally intended.

• Asia Marketing Journal
• /
• v.1 no.2
• /
• pp.56-76
• /
• 1999

금융시장에서의 경쟁이 치열해지면서 이제 국내 소비자금융기관들에게 수익성위주의 내실경영은 피할 수 없는 지상과제로 부상하고 있다. 이러한 목표를 성취하기 위해서는 우량고객을 위주로 한 기반강화와 철저한 사후관리를 통한 수익성향상이 이루어져야 한다. 특히, 자금운용처로 부상하고있는 개인고객들을 대상으로 하는 효과적인 대출마케팅의 수행은 소매금융기관들의 수익성제고에 절대적이라고 할 수 있다. 즉, 수익성을 지향하기 위해서는 고객관리를 보다 더 철저하게 하여야 하며 이를 위해서는 신용 및 수익성에 근거해서 산출된 평점에 따라 개인별 관리를 차별화하는데 있다고 할 수 있다. 본 연구에서는 우량고객들을 대상으로 대출마케팅을 활성화시키기 위한 고객평점모형의 효과성에 대해서 고찰해 보고자 하였다. 이를 위해서 신용평점모형에 대해서 자세히 알아보고 이어서 수익성에 근거한 평점모형에 대해서도 이론적으로 살펴보았다. 그리고 두 모형의 효과성을 비교하기 위해서 판별분석을 사용하여 우량 및 불량고객에 대한 예측력을 분석해 보았다. 분석결과 제1종오차에 대해서는 신용평점모형이, 제2종 오차에 대해서는 수익성평점모형이 보다 정교한 예측력을 나타냈다. 결론적으로 두 모형의 사용이 병행되는 통합적인 고객평점모형의 적용이 제안되어 졌다.

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

• The Korean Journal of Applied Statistics
• /
• v.29 no.2
• /
• pp.309-319
• /
• 2016

For binary classification models, we consider a risk score that is a function of linear scores and estimate the coefficients of the linear scores. There are two estimation methods: one is to obtain MLEs using logistic models and the other is to estimate by maximizing AUC. AUC approach estimates are better than MLEs when using logistic models under a general situation which does not support logistic assumptions. This paper considers imbalanced data that contains a smaller number of observations in the default class than those in the non-default for credit assessment models; consequently, the AUC approach is applied to imbalanced data. Various logit link functions are used as a link function to generate imbalanced data. It is found that predicted coefficients obtained by the AUC approach are equivalent to (or better) than those from logistic models for low default probability - imbalanced data.