• 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 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.

• 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.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.489-503
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• 2000

Data mining means data analysis and model selection using various types of data in order to explore useful information and knowledge for making decisions. Examples of data mining include scoring for credit analysis of a new customer and scoring for churn management, where the customers with high scores are given special attention. In this paper, scoring is interpreted as a modeling process of the conditional probability and polyclass scoring method is described. German credit data, a PC communication company data and a mobile communication company data are used to compare the performance of polyclass scoring method with that of the scoring method based on a tree model.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.624-626
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• 2023

Credit scoring is a technique used by financial institutions to assess the creditworthiness of potential borrowers. This involves evaluating a borrower's credit history to predict the likelihood of defaulting on a loan. This paper presents an ensemble of two Transformer based models within a framework for discriminating the default risk of loan applications in the field of credit scoring. The first model is FinBERT, a pretrained NLP model to analyze sentiment of financial text. The second model is FT-Transformer, a simple adaptation of the Transformer architecture for the tabular domain. Both models are trained on the same underlying data set, with the only difference being the representation of the data. This multi-modal approach allows us to leverage the unique capabilities of each model and potentially uncover insights that may not be apparent when using a single model alone. We compare our model with two famous ensemble-based models, Random Forest and Extreme Gradient Boosting.

• The Korean Journal of Applied Statistics
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• v.20 no.3
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• pp.423-440
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• 2007

Although various models have been developed to establish the enterprise credit scoring, no model has utilized the enterprise human resource so far. The purpose of this study was to build an enterprise credit scoring model using enterprise human resource factors. The data to measure the enterprise credit score were made by the first-year research material of HCCP was used to investigate the enterprise human resource and 2004 Credit Rating Score generated from KIS-Credit Scoring Model. The independent variables were chosen among questionnaires of HCCP based on Mclagan(1989)'s HR wheel model, and the credit score of Korean Information Service was used for the dependent variables. The statistical method used for data analysis was logistic regression. As a result of constructing a model, 22 variables were selected. To see these specifically by each large area, 6 variables in human resource development(HRD) area, 15 in human resource management(HRM) area, and 1 in the other area were chosen. As a consequence of 10 fold cross validation, misclassification rate and G-mean were 30.81 and 68.27 respectively. Decile having the highest response rate was bigger than the one having the lowest response rate by 6.08 times, and had a tendency to decrease. Therefore, the result of study showed that the proposed model was appropriate to measure enterprise credit score using enterprise human resource variables.

• Information Systems Review
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• v.1 no.1
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• pp.51-61
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• 1999

• Journal of the Korea Society of Computer and Information
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• v.10 no.6 s.38
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• pp.279-286
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• 2005

The objective of this study is to suggest a credit scoring model of a capital company's customers by integration of multiple classifiers using genetic algorithm. For this purpose , an integrated model is derived in two phases. In first phase, three types of classifiers MLP (Multi-Layered Perceptron), RBF (Radial Basis Function) and linear models - are trained, in which each type has three ones respectively so htat we have nine classifiers totally. In second phase, genetic algorithm is applied twice for integration of classifiers. That is, after htree models are derived from each group, a final one is from these three, In result, our suggested model shows a superior accuracy to any single ones.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.1044-1051
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• 2002

This paper proposes a two-phase mathematical programming approach by considering classification gap to solve the proposed credit scoring problem so as to complement any theoretical shortcomings. Specifically, by using the linear programming (LP) approach, phase 1 is to make the associated decisions such as issuing grant of credit or denial of credit to applicants. or to seek any additional information before making the final decision. Phase 2 is to find a cut-off value, which minimizes any misclassification penalty (cost) to be incurred due to granting credit to 'bad' loan applicant or denying credit to 'good' loan applicant by using the mixed-integer programming (MIP) approach. This approach is expected to and appropriate classification scores and a cut-off value with respect to deviation and misclassification cost, respectively. Statistical discriminant analysis methods have been commonly considered to deal with classification problems for credit scoring. In recent years, much theoretical research has focused on the application of mathematical programming techniques to the discriminant problems. It has been reported that mathematical programming techniques could outperform statistical discriminant techniques in some applications, while mathematical programming techniques may suffer from some theoretical shortcomings. The performance of the proposed two-phase approach is evaluated in this paper with line data and loan applicants data, by comparing with three other approaches including Fisher's linear discriminant function, logistic regression and some other existing mathematical programming approaches, which are considered as the performance benchmarks. The evaluation results show that the proposed two-phase mathematical programming approach outperforms the aforementioned statistical approaches. In some cases, two-phase mathematical programming approach marginally outperforms both the statistical approaches and the other existing mathematical programming approaches.

• Korean Management Science Review
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• v.15 no.1
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• pp.97-115
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• 1998

This study aims to develop the methodology based on the AHP(Analytic Hierarchy Process) of evaluation for commercial paper. commercial paper is the ma product of merchant banks. commercial paper evaluation is annually performed by the credit-evaluation agency. Credit evaluation is performed by the informal judgemental system, which has potential to induce serious inconsistencies in decision-making. We present an objective scoring model which does not suffer from the weakness of the subjective judgement system. The model used is illustrated by analyzing the commercial paper evaluation for the 3 motor-companies(H, K and S motors).