• Korean Management Science Review
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• v.33 no.3
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• pp.65-87
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• 2016

Being the FinTech technologies rapidly developed, the non face-to-face private loan market is also growing dramatically. While the real-world interests in this market are keen, the empirical studies on the issue are few compared to its prospective impact on credit loan market. This paper suggests a credit scoring model for the non face-to-face private loan employing the ratings approach (the absolute measurement method) of AHP. Analyzing a sample of data consisting of 460,000 transaction records over an 8-year period in the United States, we develop a scoring model for the non face-to-face private loan screening, and validate the model for the practical usage. Conducting sensitivity analysis, we suggest customized cut-off points for the loan execution to suit each individual loan institution's need.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.52
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• pp.311-322
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• 1999

Major problems involved in an electrical utility expansion planning within a time horizon are how to efficiently deal with objectives considering multiple factors and uncertainty. But justification factors in study these days have considered only quantitative factors except qualitative factors. Therefore, the purpose of this paper is to develop a new model for economic evaluation of nuclear power plants through the scoring model with the quantitative and qualitative factors under uncertainty. The quantitative factors use a levelized generation cost method considering time value of money. Especially, the environmental, risk, and safety factors in this paper have been also explained for the rational economic justification of the qualitative factors under uncertainty. This paper not only proposes a new approach method using the scoring model in evaluating economy of the nuclear power plant in the long term, but also provides the more efficient decision making criterion for nuclear power plants under uncertainty.

• Korean Journal of Cognitive Science
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• v.19 no.3
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• pp.223-255
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• 2008

In this paper, we introduce an automatic system of scoring English essays. The system is comprised of three main components: a spelling checker, a grammar checker and a lexical cohesion checker. We have used such resources as WordNet, Link Grammar/parser and Roget's thesaurus for these components. The usefulness of an automatic scoring system depends on its reliability. To measure reliability, we compared the results of automatic scoring with those of manual scoring, on the basis of the Kappa statistics and the Multi-facet Rasch Model. The statistical data obtained from the comparison showed that the scoring system is as reliable as professional human graders. This system deals with textual units rather than sentential units and checks not only formal properties of a text but also its contents.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.16
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• pp.6819-6822
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• 2014

Background: The aim of this study was to evaluate the accuracy of a new scoring system in Korean patients with hematuria at high risk of bladder cancer. Materials and Methods: A total of 319 consecutive patients presenting with painless hematuria without a history of bladder cancer were analyzed, from the period of August 2012 to February 2014. All patients underwent clinical examination, and 22 patients with incomplete data were excluded from the final validation data set. The scoring system included four clinical parameters: age (${\geq}50$ = 2 vs. <50 =1), gender (male = 2 vs. female = 1), history of smoking (smoker/ex-smoker = 4 vs. non-smoker = 2) and nature of the hematuria (gross = 6 vs. microscopic = 2). Results: The area under the receiver-operating characteristic curve (95% confidence interval) of the scoring system was 0.718 (0.655-0.777). The calibration plot demonstrated a slight underestimation of bladder cancer probability, but the model had reasonable calibration. Decision curve analysis revealed that the use of model was associated with net benefit gains over the treat-all strategy. The scoring system performed well across a wide range of threshold probabilities (15%-45%). Conclusions: The scoring system developed is a highly accurate predictive tool for patients with hematuria. Although further improvements are needed, utilization of this system may assist primary care physicians and other healthcare practitioners in determining a patient's risk of bladder cancer.

• Asian Journal of Innovation and Policy
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• v.6 no.2
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• pp.206-224
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• 2017

This article proposes a logic model for assessing the performance of the outcome of public research as a technology valuation method. It consists of two parts and eight steps. The first part is a scoring system and the second part is a validation process of the performance index derived from scoring by valuation method. The scoring in the first part generally requires a focus group method to find out the value drivers and make an evaluation table. The reason why we call it the technology valuation method is that the first part is derived from the simple evaluation of technology value using checklists for value drive. The second part is the regular technology valuation process. The model is designed for the measurement of unquantifiable outcome. Is knowledge or scientific outcome comparable to the measured outcome? If possible, how big is the unquantifiable outcome? This model is based on financial valuation techniques with clear or acceptable market data. Therefore, it cannot work solely for unquantifiable outcomes without comparable measurable outcomes, unlike economic valuation.

• 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.

• Nutrition Research and Practice
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• v.14 no.1
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• pp.32-44
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• 2020

BACKGROUND/OBJECTIVE: We have previously designed the anti-cancer food scoring model (ACFS) 1.0, an evidence-based quantitative tool analyzing the anti-cancer or carcinogenic potential of diets. Analysis was performed using simple quantitative indexes divided into 6 categories (S, A, B, C, D, and E). In this study, we applied this scoring model to wider recipes and evaluated its nutritional relevance. MATERIALS/METHODS: National or known regional databases were searched for recipes from 6 categories: Korean out-dining, Korean home-dining, Western, Chinese, Mediterranean, and vegetarian. These recipes were scored using the ACFS formula and the nutrition profiles were analyzed. RESULTS: Eighty-eight international recipes were analyzed. All S-graded recipes were from vegetarian or Mediterranean categories. The median code values of each category were B (Korean home-dining), C (Korean out-dining), B (Chinese), A (Mediterranean), S (vegetarian), and D (Western). The following profiles were correlated (P < 0.05) with ACFS grades in the univariate trend analysis: total calories, total fat, animal fat, animal protein, total protein, vitamin D, riboflavin, niacin, vitamin B₁₂, pantothenic acid, sodium, animal iron, zinc, selenium, and cholesterol (negative trends), and carbohydrate rate, fiber, water-soluble fiber, vitamin K, vitamin C, and plant calcium (positive trends). Multivariate analysis revealed that animal fat, animal iron, and niacin (negative trends) and animal protein, fiber, and vitamin C (positive trends) were statistically significant. Pantothenic acid and sodium showed non-significant negative trends (P < 0.1), and vitamin B₁₂ showed a non-significant positive trend. CONCLUSION: This study provided a nutritional basis and extended the utility of ACFS, which is a bridgehead for future cancer-preventive clinical trials using ACFS.

• The Korean Journal of Applied Statistics
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• v.22 no.3
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• pp.489-498
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• 2009

Underwriting is the first step for the administration of an insurance contract, which may result in stable profitability or unexpected loss for insurance company. Adequacy of underwriting criteria determines underwriting result. Generally, quantitative scoring system is used for underwriting. Method of evaluating risk for the scoring system is summing up scores for risk factors of a potential policyholder in consideration. Scores for each risk factor is predetermined. Current business environment for insurance companies makes underwriting profit more important, which means that insurance companies need more efficient underwriting method. This study suggests a reasonable approach to estimate risk relativities based on generalized linear model. Real data were used to quantify risk levels of groups of insureds for the design of underwriting model. Finally, effects in business volume and profitability of reflecting estimated underwriting scoring system are explained.

• The Korean Journal of Applied Statistics
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• v.24 no.4
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• pp.587-595
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• 2011

Credit scoring is an objective and automatic system to assess the credit risk of each customer. The logistic regression model is one of the popular methods of credit scoring to predict the default probability; however, it may not detect possible nonlinear features of predictors despite the advantages of interpretability and low computation cost. In this paper, we propose to use a generalized partially linear model as an alternative to logistic regression. We also introduce modern ensemble technologies such as bagging, boosting and random forests. We compare these methods via a simulation study and illustrate them through a German credit dataset.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.2
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• pp.39-43
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• 1999

We consider generalized exponential regression model with randomly censored data and propose a modified Fisher scoring method which estimates the model parameters. For this, the likelihood equations are derived and then the estimating algorithm is developed. We illustrate the proposed method using a real data.