• The Journal of Information Systems
• /
• v.14 no.1
• /
• pp.41-65
• /
• 2005

The credit ration is a significant area of financial management which is of major interest to practitioners, financial and credit analysts. The components of credit rating are identified decision models are developed to assess credit rating an the corresponding creditworthiness of firms an accurately ad possble. Although many early studies demonstrate a priori which of these techniques will be most effective to solve a specific classification problem. Recently, a number of studies have demonstrate that a hybrid model integration artificial intelligence approaches with other feature selection algorthms can be alternative methodologies for business classification problems. In this article, we propose a hybrid approach using rough set theory as an alternative methodology to select appropriate attributes for case-based reasoning. This model uses rough specific interest lies in lthe stable combining of both rough set theory to extract knowledge that can guide dffective retrevals of useful cases. Our specific interest lies in the stable combining of both rough set theory and case-based reasoning in the problem of corporate credit rating. In addition, we summarize backgrounds of applying integrated model in the field of corporate credit rating with a brief description of various credit rating methodologies.

• The Journal of Economics, Marketing and Management
• /
• v.10 no.3
• /
• pp.21-36
• /
• 2022

Purpose: The purpose of this study is to compare and analyze the enterprise's score index calculated from atypical data and corrected data. Research design, data, and methodology: In this study, news articles which are non-financial information but qualitative data were collected from 2,432 SMEs that has been extracted "square proportional stratification" out of 18,910 enterprises with fixed data and compared/analyzed each enterprise's score index through text mining analysis methodology. Result: The analysis showed that qualitative data can be quantitatively evaluated by region, industry and period by collecting news from SMEs, and that there are concerns that it could be an element of alternative credit evaluation. Conclusion: News data cannot be collected even if one of the small businesses is self-employed or small businesses has little or no news coverage. Data normalization or standardization should be considered to overcome the difference in scores due to the amount of reference. Furthermore, since keyword sentiment analysis may have different results depending on the researcher's point of view, it is also necessary to consider deep learning sentiment analysis, which is conducted by sentence.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.18 no.5
• /
• pp.77-90
• /
• 2023

This study aims to investigate the potential of alternative credit assessment through Social Networking Sites (SNS) as a complementary tool to conventional loan review processes. It seeks to discern the impact of SNS usage characteristics and loan product attributes on credit loan repayment. To achieve this objective, we conducted a binomial logistic regression analysis examining the influence of SNS usage patterns, loan characteristics, and personal attributes on credit loan conditions, utilizing data from Company A's credit loan program, which integrates SNS data into its actual loan review processes. Our findings reveal several noteworthy insights. Firstly, with respect to profile photos that reflect users' personalities and individual characteristics, individuals who choose to upload photos directly connected to their personal lives, such as images of themselves, their private circles (e.g., family and friends), and photos depicting social activities like hobbies, which tend to be favored by individuals with extroverted tendencies, as well as character and humor-themed photos, which are typically favored by individuals with conscientious traits, demonstrate a higher propensity for diligently repaying credit loans. Conversely, the utilization of photos like landscapes or images concealing one's identity did not exhibit a statistically significant causal relationship with loan repayment. Furthermore, a positive correlation was observed between the extent of SNS usage and the likelihood of loan repayment. However, the level of SNS interaction did not exert a significant effect on the probability of loan repayment. This observation may be attributed to the passive nature of the interaction variable, which primarily involves expressing sympathy for other users' comments rather than generating original content. The study also unveiled the statistical significance of loan duration and the number of loans, representing key characteristics of loan portfolios, in influencing credit loan repayment. This underscores the importance of considering loan duration and the quantity of loans as crucial determinants in the design of microcredit products. Among the personal characteristic variables examined, only gender emerged as a significant factor. This implies that the loan program scrutinized in this analysis does not exhibit substantial discrimination based on age and credit scores, as its customer base predominantly consists of individuals in their twenties and thirties with low credit scores, who encounter challenges in securing loans from traditional financial institutions. This research stands out from prior studies by empirically exploring the relationship between SNS usage and credit loan repayment while incorporating variables not typically addressed in existing credit rating research, such as profile pictures. It underscores the significance of harnessing subjective, unstructured information from SNS for loan screening, offering the potential to mitigate the financial disadvantages faced by borrowers with low credit scores or those ensnared in short-term liquidity constraints due to limited credit history a group often referred to as "thin filers." By utilizing such information, these individuals can potentially reduce their credit costs, whereas they are supposed to accrue a more substantial financial history through credit transactions under conventional credit assessment system.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.16 no.3
• /
• pp.13-23
• /
• 2020

Currently, online means of replacing resident registration numbers(RRN) include I-Pin, mobile phone, credit card, MyPin, and general-purpose certificate. In order to issue alternative means based on the RRN, it must be designated through the designation review by the Korea Communications Commission(KCC) through a designation review by personal proofing agency and be subject to annual management. However, the criteria for designation and follow-up of the designation of the personal proofing agency carried out by KCC have been used in 2010 without revision, and there are problems that do not conform to the evaluation standards of various alternative means. Therefore, in this paper, we propose a method for improving the designation criteria and management system of the personal proofing service agency. The proposed method analyzes the characteristics of the alternative identification-based personal proofing service and proposes a follow-up management standard that can appropriately evaluate the analyzed characteristics and improves the designation criteria according to the emergence of new alternatives. Through the proposed method, it can be seen that it is possible to strengthen the safety of the personal proofing service based on the alternative means of RRN provided online and face-to-face and to protect the user's personal information.

• Management & Information Systems Review
• /
• v.35 no.3
• /
• pp.173-193
• /
• 2016

The aim of this paper is to extend the TOPSIS(Technique for Order Preference by Similarity to Ideal Solution) to the fuzzy environment for solving the disaster recovery priority decision problem in credit bureau business information system. In this paper, the rating of each information systems and the weight of each criterion are described by linguistic terms which can be expressed in trapezoidal fuzzy numbers. Then, a vertex method is proposed to calculate the distance between two trapezoidal fuzzy numbers. According to the concept of the TOPSIS, a closeness coefficient is defined to determine the ranking order of all information systems. The combination between the fuzzy set and TOPSIS brings several benefits when compared with other approaches, such that the fuzzy TOPSIS require few fuzzy judgements to parameterization, which contributes to the agility of the decision process, it does not limit the number of alternatives simultaneously evaluated, and it does not cause the ranking reversal problem when a new alternative is included in the evaluation process. This paper is demonstrated with a real case study of a credit rating agency involving 9 evaluation criteria and 9 credit bureau business information systems assessed by 6 evaluators, and provide the systematic disaster recovery framework for BCP(Business Continuity Planning) to practitioner. Finally, this paper show that the procedure of the proposed fuzzy TOPSIS method is well suited as a decision-making tool for the disaster recovery priority decision problem in credit bureau business information system.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.13 no.3
• /
• pp.125-140
• /
• 2018

According to statistics, it is shown that domestic SMEs rely on bank loans for the majority of fund procurement. From financial information shortage (Thin file) that does not provide information necessary for credit evaluation from banks such as financial statements. In order to overcome these problems, recently, in alternative finance such as P2P, using differentiated information such as demographics, trading information and the like utilizing Fintech instead of existing financial information, small funds A new credit evaluation method has been expanding to provide SMEs with small amounts of money. In this paradigm of environmental change, in this research, credit evaluation which can expand fund supply to SMEs by utilizing big data based on trade area information such as sales fluctuation, location conditions etc. In this research, we try to find such a solution. By analyzing empirically the big data generated in the trade area, we verify the effectiveness as a credit evaluation factor and try to derive the main parameters necessary for the business performance evaluation of the founder of SMEs. In this research, for 17,116 material businesses in Seoul City that operate the service industry from 2009 to February 2018, we collect trade area information generated for each business location from Big Data specialized company NICE Zini Data Co., Ltd.. We collected and analyzed the data on the locations and commercial areas of the facilities that were difficult to obtain from SMEs and analyzed the data that affected the Corporate financial Distress. It is possible to refer to the variable of the existing unused big data and to confirm the possibility of utilizing it for efficient financial support for SMEs, This is to ensure that commercial lenders, even in general commercial banks, are made to be more prominent in one sector of the financing of SMEs. In this research, it is not the traditional financial information about raising fund of SMEs who have basically the problem of information asymmetry, but a trade area analysis variable is derived, and this variable is evaluated by credit evaluation There is differentiation of research in that it verified through analysis of big data from Trading-area whether or not there is an effect on.

• The Korean Journal of Applied Statistics
• /
• v.25 no.6
• /
• pp.1027-1036
• /
• 2012

Among various biometrics recognition systems, statistical fingerprint recognition matching methods are considered using minutiae on fingerprints. We define similarity distance measures based on the coordinate and angle of the minutiae, and suggest a fingerprint recognition model following statistical distributions. We could obtain confidence intervals of similarity distance for the same and different persons, and optimal thresholds to minimize two kinds of error rates for distance distributions. It is found that the two confidence intervals of the same and different persons are not overlapped and that the optimal threshold locates between two confidence intervals. Hence an alternative statistical matching method can be suggested by using nonoverlapped confidence intervals and optimal thresholds obtained from the distributions of similarity distances.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.15 no.6
• /
• pp.135-154
• /
• 2020

This paper analyzes the 5,521 samples of the small and medium-sized businesses(SMBs) obtained from the Korea Credit Guarantee Fund. From January 2014 to September 2019, 85% of the SMBs have 5 or fewer full-time employees. The proportion of SMBs is overwhelmed by the elderly men, and most founders are the CEO. Also, about 87% of the workplace types are rented, while 64% of the CEO's residence types are owner-occupation. 47% of the financial grade score is less than 10 points out of 100 and 80% of SMBs have less than 200 million won of the loan guarantee. In particular, the total guarantee loan amount or the days of net guarantee have significantly positive relations with the working period of the CEO in the same industry, the number of employees, the operation period of SMBs, and the corporate business type. In the case of the financial grading score which has the highest weight in overall credit rating gets higher with the higher number of employees, the longer the operation period, and the corporate business type. However, the quantified non-financial grading score has no significant relationship with other explanatory variables, except for the corporate business type. This implies that a non-financial grade score is measured by other determinants that are not observed by the Korea credit guarantee fund. The pure non-financial grade score has positive relations with the working period of the CEO. Overall, this paper would help Korean SMBs upgrade their credit ratings and expand the money supply when there is no standardized credit rating model or no publicly available evaluation criteria for SMBs. We expect this paper provides important insights for further research and policy-makers for SMBs. In particular, to address the financial needs of thin-filers such as SMBs, technology-based financial services (TechFin) would use alternative data to evaluate the financial capabilities of thin-filers and to develop new financial services.

• The Journal of Economics, Marketing and Management
• /
• v.9 no.1
• /
• pp.25-40
• /
• 2021

Purpose: The forecasting is the likelihood scientifically proved judgment about the prospects, the possible conditions of this or that phenomenon in the future and (or) about the alternative ways and the means of their realization. To adapt the instruments of budgeting for the analysis cash flow of company. Research design, data and methodology: The creates the budget of cash flow were carried out on the basis of data of the report for the 2017 of corporations POSCO and in the first half of the 2018 Daewoo Shipbuilding & Marine Engineering of South Korea. Results: The simultaneous use of budgeting techniques and the simple financial analysis allows to systematize the transactions, to identify the main problem areas in the movement cash flows. Therefore, working capital analysis is to determine the limits of their fluctuations in view of the changes in the business processes. Conclusions: In the pedagogical context solved the features of budgeting in the part evaluation current assets, its financing, its elements: the cash, the debtor. In the process of budgeting of cash flow, in credit budget, in financial budget we can see the main indicators: the current assets, the functioning capital, the optimum number of debtors, the optimum amount of cash and another.

• The Korean Journal of Applied Statistics
• /
• v.35 no.1
• /
• pp.77-91
• /
• 2022

The optimal threshold estimation is considered in order to discriminate the mixture distribution in the fields of Biostatistics and credit evaluation. There exists well-known various accuracy measures that examine the discriminant power. Recently, Matthews correlation coefficient and the F1 statistic were studied to estimate optimal thresholds. In this study, we explore whether these accuracy measures are appropriate for the optimal threshold to discriminate the mixture distribution. It is found that some accuracy measures that depend on the sample size are not appropriate when two sample sizes are much different. Moreover, an alternative method for finding the optimal threshold is proposed using the correlation coefficient that defines the ratio of the confusion matrix, and the usefulness and utility of this method are also discusses.