• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.29 no.6
• /
• pp.1285-1303
• /
• 2019

In the current credit scoring system, the credit bureau gathers credit information from financial institutions and calculates a credit score based on it. However, because all sensitive credit information is stored in one central authority, there are possibilities of privacy violations and successful external attacks can breach large amounts of personal information. To handle this problem, we propose privacy-preserving credit scoring in which a user gathers credit information from financial institutions, calculates a credit score and proves that the score is calculated correctly using a zero-knowledge proof and a blockchain. In addition, we propose a zero-knowledge proof scheme that can efficiently prove committed inputs to check whether the inputs of a zero-knowledge proof are actually provided by financial institutions with a blockchain. This scheme provides perfect zero-knowledge unlike Agrawal et al.'s scheme, short CRSs and proofs, and fast proof and verification. We confirmed that the proposed credit scoring can be used in the real world by implementing it and experimenting with a credit score algorithm which is similar to that of the real world.

• Journal of Information Processing Systems
• /
• v.11 no.2
• /
• pp.173-183
• /
• 2015

At present, it is simple to the electronic commerce credit scoring model, as a brush credit phenomenon in E-commerce has emerged. This phenomenon affects the judgment of consumers and hinders the rapid development of E-commerce. In this paper, that E-commerce credit evaluation model that uses a Gaussian density function is put forward by density test and the analysis for the anomalies of E-commerce credit rating, it can be fond out the abnormal point in credit scoring, these points were calculated by nonlinear credit scoring algorithm, thus it can effectively improve the current E-commerce credit score, and enhance the accuracy of E-commerce credit score.

• The Journal of Asian Finance, Economics and Business
• /
• v.10 no.2
• /
• pp.257-265
• /
• 2023

This paper investigates the determinants of credit accessibility and the effect of credit on the income of farm households borrowing from Vietnam Bank for Agriculture and Rural Development, Giong Rieng District Branch, Kien Giang Province. Based on the primary data of 200 farming households who are the customer of the bank, the study applied the Probit regression model to examine determinant factors of credit accessibility of farm households and employed the Propensity score matching method to investigate the impact of credit on households' income. The findings of the Probit regression shown that three independent variables that significantly influence the access to credit of households are household size, income source, and farm size. Besides that, the Propensity score matching method results showed a difference of 23.799 million VND/year between the income of borrowing households and that of non-borrowing households at the significance level of 1%. The difference in the imcome from the interval and central matching methods are VND 24.700 million VND/year and VND 24.633 million VND/year, respectively. Given empirical findings suggetsted that several recommendations to increase the credit accessibility of farm households, thereby creating favorable conditions for improving their income.

• The Journal of Information Systems
• /
• v.29 no.1
• /
• pp.241-265
• /
• 2020

Purpose Various machine learning techniques are used to implement for predicting corporate credit. However, previous research doesn't utilize time series input features and has a limited prediction timing. Furthermore, in the case of corporate bond credit rating forecast, corporate sample is limited because only large companies are selected for corporate bond credit rating. To address limitations of prior research, this study attempts to implement a predictive model with more sample companies, which can adjust the forecasting point at the present time by using the credit score information and corporate information in time series. Design/methodology/approach To implement this forecasting model, this study uses the sample of 2,191 companies with KIS credit scores for 18 years from 2000 to 2017. For improving the performance of the predictive model, various financial and non-financial features are applied as input variables in a time series through a sliding window technique. In addition, this research also tests various machine learning techniques that were traditionally used to increase the validity of analysis results, and the deep learning technique that is being actively researched of late. Findings RNN-based stateful LSTM model shows good performance in credit rating prediction. By extending the forecasting time point, we find how the performance of the predictive model changes over time and evaluate the feature groups in the short and long terms. In comparison with other studies, the results of 5 classification prediction through label reclassification show good performance relatively. In addition, about 90% accuracy is found in the bad credit forecasts.

• KDI Journal of Economic Policy
• /
• v.38 no.3
• /
• pp.79-106
• /
• 2016

This paper investigates the effectiveness of public credit guarantee programs and interest-support programs for SMEs (small and medium enterprises). First, assuming that there is an imperfect information structure in the SME loan market, we analyze how SME support financial programs affect the corporate decisions made by SMEs with regard to default or loan sizes. In addition, this paper theoretically computes the optimal levels of credit guarantee amounts and the interest-support spread under equilibrium with imperfect information in a competitive loan market. Second, the paper empirically analyzes the continuous policy-treatment effect with the GPS (generalized propensity score) method. In particular, we consider the ratio of guaranteed debt to the total debt as a continuous policy treatment. The empirical results show that marginal effects of a credit guarantee on SMEs' productivity, profitability, and growth potential decrease with the ratio of guaranteed debt to the total debt. In addition, the average effect of a credit guarantee is maximized when this ratio is at 50% to 60%.

• Journal of the Korea Society of Computer and Information
• /
• v.21 no.4
• /
• pp.63-71
• /
• 2016

This paper is about applying efficient data mining method which improves the score calculation and proper building performance of credit ranking score system. The main idea of this data mining technique is accomplishing such objectives by applying Correlation based Feature Selection which could also be used to verify the properness of existing rank scores quickly. This study selected 2047 manufacturing companies on KOSPI market during the period of 2009 to 2013, which have their own credit rank scores given by NICE information service agency. Regarding the relevant financial variables, total 80 variables were collected from KIS-Value and DART (Data Analysis, Retrieval and Transfer System). If correlation based feature selection could select more important variables, then required information and cost would be reduced significantly. Through analysis, this study show that the proposed correlation based feature selection method improves selection and classification process of credit rank system so that the accuracy and credibility would be increased while the cost for building system would be decreased.

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

• Journal of the Korean Data and Information Science Society
• /
• v.21 no.6
• /
• pp.1225-1235
• /
• 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.

• The Korean Journal of Applied Statistics
• /
• v.21 no.2
• /
• pp.211-219
• /
• 2008

Customer Loyalty is very important for a company to be survived and to make profit for a long time. Especially, since the credit card company has to manage proper card members and merchants, the CRM(Customer Relationship Management) is much emphasized. A loyalty score is more essential to credit card companies which provide differential financial services based on card members and merchants than any other companies. In this paper, we discuss behavioral measures to define customer loyalty and suggest a method to make loyalty score with an example of a credit card company. The loyalty score developed is considered easy to understand and simple to apply in card industry. In the development of loyalty score, first, we define the loyal customers and non-loyal customers by measuring variables indicating loyalty. And we perform individual logistic regression by each exploratory measuring variable and obtain the weight of measure variables using Chi-square statistics which is used for model fitness. The loyalty score suggested shows very stable results in terms of PSI (Population Stability Index) as time goes.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.4
• /
• pp.171-180
• /
• 2021

The purpose of this paper is to estimate the impact of credit rationing on the amount of trade credit used by farmers in Vietnam. This study employs a survey data collected through direct interviews with heads of 1,065 rice households randomly selected out of provinces and city in the Mekong River Delta (MRD). In each province or city, the village with the largest area of land devoted to rice production from the district with the largest area of land devoted to rice production was picked up for survey. In each village, 200 rice farmers were randomly chosen for interview. Based on a probit model and a semi-parametric propensity score matching (PSM) estimator while controlling socio-demographic traits of rice farmers, the estimated results show that non-credit rationed farmers use less trade credit to finance production compared to their credit rationed counterparts. Moreover, the amount of trade credit used by farmers decreases as the degree of credit rationing drops. This paper provides evidence of the substitutive relationship between bank credit and trade credit. It also implicitly suggests that banks can drive trade creditors out of the market if they manage to solve the problem of information asymmetry and transaction cost.