Yoonseong Kim

Managing loan customers using misclassification patterns of credit scoring model

A number of credit scoring models have been developed to evaluate credit risk of new loan applicants and existing loan customers, respectively. This study proposes a method to manage existing customers by using misclassification patterns of credit scoring model. We divide two groups of customers, the currently good and bad credit customers, into two subgroups, respectively, according to whether their credit status is misclassified or not by the neural network model. In addition, we infer the characteristics of each subgroup and propose management strategies corresponding to each subgroup.

Optimal bipartite scorecards

In many retail banking applications, scorecards used for assessing creditworthiness must be simple and interpretable. For this reason, the industry has favoured logistic regression models based on categorised variables. In this paper we describe an extension of such models based on an optimal partition of the applicant population into two subgroups, with categorised logistic models being built in each part. Such bipartite models have the merits of yielding improved predictive accuracy, while retaining interpretive simplicity. They have been used in the industry before, but only in an ad hoc way, with no effort being made to find the optimal division. Some examples and properties of the resulting models are described.

Random effects model for credit rating transitions

This paper proposes a random effects multinomial regression model to estimate transition probabilities of credit ratings. Unlike the previous studies on the rating transition, we applied a random effects model, which accommodates not only the environmental characteristics of the exposures of a rating but also the uncertainty not explained by such factors. The rating category specific factors such as retained earning and market equity are included in our proposed model. The random effects model provides less diagonally dominant matrix, where the transition probabilities are over-dispersed from the diagonal elements. Our study is expected to incorporate potential chances of rating transitions due to extra random variations.

Stock fraud detection using peer group analysis

Highlights? We apply peer group analysis to the detection of stock price manipulation. ? We update different weights of peer group members over time. ? We analyze real time series data observed from Korean stock market. This study proposes a method to detect suspicious patterns of stock price manipulation using an unsupervised data mining technique: peer group analysis. This technique detects abnormal behavior of a target by comparing it with its peer group and measuring the deviation of its behavior from that of its peers. Moreover, this study proposes a method to improve the general peer group analysis by incorporating the weight of peer group members into summarizing their behavior, along with the consideration of parameter updates over time. Using real time series data of Korean stock market, this study shows the advantage of the proposed peer group analysis in detecting abnormal stock price change. In addition, we perform sensitivity analysis to examine the effect of the parameters used in the proposed method.

Technology scoring model considering rejected applicants and effect of reject inference

Technology evaluation has become a critical part of technology investment, and accurate evaluation can lead more funds to the companies that have innovative technology. However, existing processes have a weakness in that it considers only accepted applicants at the application stage. We analyse the effectiveness of technology evaluation model that encompasses both accepted and rejected applicants and compare its performance with the original accept-only model. Also, we include the analysis of reject inference technique, bivariate probit model, in order to see if the reject inference technique is of use against the accept-only model. The results show that sample selection bias of the accept-only model exists and the reject inference technique improves the accept-only model. However, the reject inference technique does not completely resolve the problem of sample selection bias.

Technology credit rating system for funding SMEs

Technology evaluation has played a crucial role in selecting and supporting companies with innovative technology. Previous studies have focused on developing technology evaluation methods such as scorecard. However, technology credit rating is rarely applied, despite its convenient usage for technology financing. In this paper, we propose a technology credit rating system, called cross matrix, based on empirical data obtained from the technology scoring model and examine their properties. The proposed rating system is expected to provide valuable information for effective management of the technology credit fund.

DEA based multi-period evaluation system for research in academia

Various types of incentive systems are widely used by many companies and organizations for better performances. However, despite the demand for the fair incentive systems, those systems in academia have not been well established and fairly operated. Using an example of a professor evaluation system, we examine two main problems of the existing incentive systems in academia - ignoring the input aspect and focusing only on the short-term performance. By applying the super-efficiency DEA and considering multi-period output, we show that the input factors and the time trend of outcomes need to be incorporated for the fair evaluation of professors and their research performance.

Economic Evaluation Model for International Standardization of Correlated Technologies

Standardization is becoming increasingly important, and therefore firms need to begin planning for standardization as early as the R&D stage. While the costs of implementing early standardization are high and have a low probability of success, the potential benefits can greatly exceed the initial costs if the project is successfully managed. In cases of correlated technologies, the degree of uncertainty for implementing standardization is greatly increased; however, correlated technologies can result in a larger benefit and lower cost when they are jointly developed with standardization in mind from the beginning. In this study, we propose an economic evaluation model to analyze both the benefits and costs of correlated technology development and standardization, utilizing the concept of cost-of-ownership model. We also apply the proposed model to a realistic case and perform sensitivity analysis to examine the effects of various parameter settings. This study is expected to contribute to a more accurate evaluation of the economic value of standardization.

Demand forecasting of high-speed Internet access service considering unknown time-varying covariates

In order to forecast the demand for information and communication services, it is important to consider not only intrinsic variables representing service characteristics but also unknown time-varying variables such as marketing policy. However, in many cases, information such as a companys internal marketing policy is not available. This study proposes a negative exponential growth curve model that incorporates unobservable time-varying covariates by reversely estimating the unknown covariates. The proposed approach is then applied to technological forecasting of high-speed Internet access services provided by a telecommunication corporation in Korea.

Economic Evaluation Model for International Standardization of Technology

This paper proposes an economic evaluation model for international standardization to support the decision-making process of technology development and standardization. We make use of a cost of ownership (COO) model for analyzing both expected-benefit and cost-of-technology development and standardization. Then, we apply the proposed model to a case of radio frequency identification (RFID) technology by establishing four potential scenarios. From the scenario analysis, the expected profit of the international standardization is evaluated for each scenario. We also perform sensitivity analysis to consider the effect of various parameter settings. This paper is expected to lead companies in the choice of the best strategy of technology development and standardization that would bring maximum profit.