• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.361-381
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• 2023

Research on corporate bankruptcy prediction has been focused on financial information. Since the company's financial information is updated quarterly, there is a problem that timeliness is insufficient in predicting the possibility of a company's business closure in real time. Evaluated companies that want to improve this need a method of judging the soundness of a company that uses information other than financial information to judge the soundness of a target company. To this end, as information technology has made it easier to collect non-financial information about companies, research has been conducted to apply additional variables and various methodologies other than financial information to predict corporate bankruptcy. It has become an important research task to determine whether it has an effect. In this study, we examined the impact of electronic payment-related information, which constitutes non-financial information, when predicting the closure of business operators using electronic payment service and examined the difference in closure prediction accuracy according to the combination of financial and non-financial information. Specifically, three research models consisting of a financial information model, a non-financial information model, and a combined model were designed, and the closure prediction accuracy was confirmed with six algorithms including the Multi Layer Perceptron (MLP) algorithm. The model combining financial and non-financial information showed the highest prediction accuracy, followed by the non-financial information model and the financial information model in order. As for the prediction accuracy of business closure by algorithm, XGBoost showed the highest prediction accuracy among the six algorithms. As a result of examining the relative importance of a total of 87 variables used to predict business closure, it was confirmed that more than 70% of the top 20 variables that had a significant impact on the prediction of business closure were non-financial information. Through this, it was confirmed that electronic payment-related information of non-financial information is an important variable in predicting business closure, and the possibility of using non-financial information as an alternative to financial information was also examined. Based on this study, the importance of collecting and utilizing non-financial information as information that can predict business closure is recognized, and a plan to utilize it for corporate decision-making is also proposed.

• Journal of Intelligence and Information Systems
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• v.1 no.1
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• pp.123-134
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• 1995

인공신경망을 이용한 기업도예측에 관한 연구는 일반적으로 대기업을 대상으로 수행되고 있으며, 분석자료로는주로 재무제표에서 얻어지는 재무정보를 사용하고 있다. 이들 대기업의 재무정보들은 비교적양이 풍부하고 신뢰성이 높기 때문에 인공신경망을 이용한 도산예측의 적중률이 80%∼85%의 높은 수준을 보이고 있다. 하지만, 중소기업이 재무정보는 불충분할 뿐만 아니라 신뢰성이 낮을 가능성이 높기 때문에, 중소기업의 도산예측에 있어서 재무정보만을 사용하게 되면 그 정확도가 떨어지게 된다. 본 연구에서는 인공신경망을 이용한 중소기업의 도산예측에 있어서, 재무정보를 보완할 수 있는 비재무정보의 유용성을 검증하였다. 연구결과 본 연구에서 사용한 비재무정보가 획득가능한 비재무정보중 극히 일부에 지나지 않았음에도 불고하고, 재무정보만을 사용하였을 때보다 예측력이 10%정도나 향상되었다.

• Journal of Industrial Convergence
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• v.21 no.6
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• pp.1-11
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• 2023

The purpose of this study is to verify the factors affecting corporate bankruptcy by using non-financial information of companies invested by credit guarantee institutions. In this study, 594 companies (525 normal companies, 69 insolvent companies) invested in by the Korea Credit Guarantee Fund from March 2014 to the end of December 2022 were selected as samples. Non-financial information of companies was divided into founder characteristics information, company characteristics information, and corporate investment information, and cross-analysis and logistic regression analysis were conducted. As a result of the cross-analysis, personal credit rating, industry, and joint investment were selected as significant variables, and logistic regression analysis was conducted for those variables, and two variables, personal credit rating and joint investment, were selected as important factors for bankruptcy. In business management, the founder's personal credit and the importance of joint investment in investment support were found out. It will help to minimize bankruptcy if institutions that support investment in SMEs reflect these results in their screening and systematically build cooperative relationships with private investment institutions. It is hoped that this study will provide an opportunity to pay more attention to the factors that affect the bankruptcy of companies that receive direct investment from public institutions.

• The Journal of Korean Institute for Practical Engineering Education
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• v.3 no.1
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• pp.103-109
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• 2011

The ODA(Official Development Assistance) of Korea is growing considerably, and the ODA projects in the technical and vocational education have the good performance to the recipient countries, because they have the direct and quick benefits. But there are no economic model of performance measurement for the ODA in the technical and vocational education. The purpose of this study is to develop the economic model of measuring the performance of the ODA in the technical and vocational education. This study use the ROI model using financial information and the BSC model using non-financial information, and link each model closely. The ROI model using financial information is more relevant, because the benefits of the technical and vocational education are distinct and quick to the recipient groups.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.1
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• pp.139-149
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• 2019

The purpose of this study is to contribute to the minimization of the social cost due to the insolvency by improving the success rate of the startups by providing useful information to the founders and the start-up support institutions through analysis of non-financial information affecting the failure of the startups. This study is aimed at entrepreneurs. The entrepreneurs that are defined by the credit guarantee institutions generally refer to entrepreneurs within 5 years of establishment. The data used in the study are sampled from the companies that were supported by the start-up guarantee from January 2014 to December 2013 as the end of December 2017. The total number of sampled firms is 2,826, 2,267 companies (80.2%), and 559 non-performing companies (19.8%). The non-financial information of the entrepreneur was divided into the entrepreneur characteristics information, the entrepreneur characteristics information, the entrepreneur asset information and the entrepreneur 's credit information, and cross-tabulations and logistic regression analysis were conducted. As a result of cross-tabulations, univariate analysis showed that personal credit rating, presence in the industry, presence of residential housing, presence of employees, and presence of financial statements were selected as significant variables. As a result of the logistic regression analysis, three variables such as personal credit rating, occupation in the industry, and presence of residential house were found to be important factors affecting the failure of founding companies. This result shows the importance of entrepreneur 's personal credibility and experience and entrepreneur' s assets in business management. The start-up support institutions should reflect these results in the entrepreneur 's credit evaluation system, and the entrepreneurs need training on the importance of the personal credit and the management plan in the entrepreneurial education. The results of this analysis will contribute to the minimization of the incapacity of startups by providing useful non-financial information to founders and start-up support organizations.

• The Journal of the Korea Contents Association
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• v.14 no.10
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• pp.218-226
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• 2014

In order to improve manufacturing performance by flexibilities, type of strategies, types of information with manufacturing companies in Korea. This study is based on the situation theory. The variables of flexibility were applied with 'product flexibility' and 'mix flexibility'. 'low-cost strategy' and 'differentiation strategy' were applied at strategy types. Financial information and non-financial information, information attributes are applied at. At this study, product flexibility is significantly influenced the differentiation strategy. Mix flexibility is significantly influenced the low cost strategy. Only the low-cost strategy significantly affected on financial information and non-financial information. financial information and non-financial information were significantly influenced on Productive performance. To achieve the purpose of this study, Structural Equation Model (SEM) has been applied.

• Journal of Intelligence and Information Systems
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• v.4 no.1
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• pp.41-58
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• 1998

본 연구는 여러 가지 Data Mining 기법들로부터 도출된 지식과 AHP를 이용하여 도출된 전문가의 지식을 사용된 정보의 특성에 따라 조사하고, 이러한 각각의 지식들을 중심으로 부도예측 모형을 설계한 후, 각 모형의 특성 및 부도예측력에 대한 실증적 비교연구에 그 목적을 두고 있다. 사용된 Data Mining 기법들은 통계적 다중판별분석 모형, ID3 모형, 인공신경망 모형이며, 전문가 지식의 추출은 AHP를 사용하여 45명의 전문가로부터 부도와 관련하여 인터뷰 및 설문조사를 실시하였다. 특히 부도예측에 사용된 변수의 특성을 정량적 재무정보와 정성적 비재무정보로 나누어서 각 모형의 특성을 비교연구하였다. 연구결과 부도예측시 정성적정보의 중요성을 확인하였으며, 전문가의 지식을 기반으로한 AHP 모형이 위험예측모형으로 사용될 수 있음을 실증적으로 보여주었다.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.111-122
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• 2023

The purpose of this study is to verify the factors affecting survival time by estimating survival rate and survival time using non-financial information of social enterprises using credit guarantee in credit guarantee institutions, and provide information to stakeholders to improve survival rate and employ to contribute to maintaining and expanding the As a research method, survival analysis was performed using a non-parametric analysis method, Kaplan-Meier Analysis. As a sample, 621 companies (577 normal companies, 44 insolvent companies) established between 2009 and 2018 were selected as the target companies. As a result of examining the factors affecting survival time by classifying social enterprise representative information and corporate information, representative credit rating, representative home ownership, credit transaction period, and corporate credit rating were derived as significant variables affecting survival time. In the future, financial institutions will be able to induce corporate soundness by reflecting factors that affect survival when examining loans for social enterprises, contributing to job retention and reduction of social costs. Supporting organizations such as the government and private organizations will be able to use it in various ways, such as policy establishment and education and training for the growth and sustainability of social enterprises. With this study as an opportunity, I hope that research will continue with more interest in the factors influencing social enterprise performance as well as corporate insolvency.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.13 no.5
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• pp.63-72
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• 2018

The purpose of this study was to analyze the survival rate and survival characteristics of young start-up entrepreneurs supported with public financing, by using non-parametric statistic of Kaplanr-Meier Analysis on non-financial data. Average survival periods of different survival characteristics have been estimated by dividing the age groups into 20s and 30s. After then, the main variables affecting the survival period have been analyzed. 3,825 firms guaranteed by Credit Guarantee Institutions in Korea were used as database for the analysis. 3,242 firms have survived while 583 firms have gone insolvent. The study period was from January 1, 2011 to December 31, 2017. Age-based breakdown of the business founders show that 3 variables in the 20s and 5 variables in the 30s are derived as the significant variables, resulting in the significant differences of each age group. In other words, the start-up support agencies and financial institutions need to develop a credit evaluation system that distinguishes the criteria of age range and find information that reflect the characteristics of entrepreneurs in their 20s as well as developing tailor-made financial products. Also, step-by-step support measures are required for the start-ups of high survival times and make them grow into promising SMEs. Meanwhile, non-financial support plans shall be invigorated along with the financial ones to help the start-ups of low survival times. This study is meaningful in that the survival analysis has been conducted by using the non-financial data of young start-up entrepreneurs. It is expected that the results of this analysis contribute to the enhancement of survival rate of start-ups by providing start-up support agencies and start-up business owners with the unique information of the survival characteristics.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.12
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• pp.3768-3774
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• 2009

This paper explored how soft information and hard information were used when SBC(Small Business Corporation, Korea) reviewed government loan applications. The data set is made up of financial and non-financial data of small-business firms since 2004. A non-financial data set is considered as soft information. Relative importance of three kinds information such as credit information, soft information, financial information is compared with each other by using the logit model. As a result, credit information is most critical to the loan approval, and then soft information follows, lastly financial information has the smallest effect on the loan approval. This is because the credit information is made up of the non-linear combination of soft information and financial information. When the relative importance of soft information and financial information is considered, soft information is relatively more critical to the loan approval then financial information. This is because financial ratios provided by small-business firms are not reliable enough.