• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.31
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• pp.27-56
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• 2006

Today's financial institutions usually take environmental issues seriously into consideration as they could not evade lender liability in an increasing number of cases. On the international scene, a brand-new concept of the "Equator Principles" in the New Millenium has driven more and more international banks to adopt these Principles in project financing. Sustainable development has been a key word in understanding new trends of the governments, financial institutions, corporations and civic groups in the 21st century. The Equator Principles are a set of voluntary environmental and social guidelines for sustainable finance. These Principles commit bank officers to avoid financial support to projects that fail to meet these guidelines. The Principles were conceived in 2002 on an initiative of the International Finance Corporation(IFC), and launched in June 2003. Since then, dozens of major banks, accounting for up to 80 percent of project loan market, have adopted the Principles. Accordingly, the Principles have become the de facto standard for all banks and investors on how to deal with potential social and environmental issues of projects to be financed. Compliance with the Equator Principles facilitates for endorsing banks to participate in the syndicated loan and help them to manage the risks associated with large-scale projects. The Equator Principles call for financial institutions to provide loans to projects under the following circumstances: - The risk of the project is categorized in accordance with internal guidelines based upon the environmental and social screening criteria of the IFC. - For Category A and B projects, borrowers or sponsors are required to conduct a Social and Environmental Assessment, the preparation of which must meet certain requirements and satisfactorily address key social and environmental issues. - The Social and Environmental Assessment report should address baseline social and environmental conditions, requirements under host country laws and regulations, sustainable development, and, as appropriate, IFC's Environmental, Health and Safety Guidelines, etc. - Based on the Social and Environmental Assessment, Equator banks then make agreements with borrowers on how they mitigate, monitor and manage the risks through a Social and Environmental Management System. Compliance with the plan is included in the covenant clause of loan agreements. If the borrower doesn't comply with the agreed terms, the bank will take corrective actions. The Equator Principles are not a mere declaration of cautious banks but a full commitment of lenders. A violation of the Principles in the process of project financing, which led to an unexpected damage to the affected community, would not give rise to any specific legal remedies other than ordinary lawsuits. So it is more effective for banks to ensure consistent implementation of the Principles and to have them take responsible measures to solve social and environmental issues. Public interests have recently mounted up with respect to environmental issues on the occasion of the Supreme Court's decision (2006Du330) on the fiercely debated reclamation project at Saemangeum. The majority Justices said that the expected environmental damages like probable pollution of water and soil were not believed so serious and that the Administration should continue to implement the project seeking ways to make it more environment friendly. In this case, though the Category A Saemangeum Project was carried out by a government agency, the Supreme Court behaved itself as a signal giver to approve or stop the environment-related project like an Equator bank in project financing. At present, there is no Equator bank in Korea in contrast to three big banks in Japan. Also Korean contractors, which are aggressively bidding for Category A-type projects in South East Asia and Mideast, might find themselves in a disadvantageous position because they are generally ignorant of the environmental assessment associated with project financing. In this regard, Korean banks and overseas project contractors should care for the revised Equator Principles and the latest developments in project financing more seriously. It's because its scope has expanded to the capital cost of US$10 million or more across all industry sectors regardless of developing countries or not. It should be noted that, for a Korean bank, being an Equator bank is more or less burdensome in a short-term period, but it must be conducive to minimizing risks and building up good reputation in the long run.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.4
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• pp.167-178
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• 2021

The purpose of this study is to analyze the direct investment decision factors in the Kaesong Industrial Complex and Vietnam, and to contribute to the creation of domestic jobs and the revitalization of the inter-Korean economy. According to the analysis, most of the Kaesong Industrial Complex and Vietnamese investment companies are entering the complex for the purpose of utilizing cheap labor, cheap factory locations, sales/development of local markets, and bypass export production bases in third countries. This can be divided into production-efficient investors using differences in production price such as labor costs and market-oriented investors to sell and expand the local market, which seems to be consistent with global direct investment patterns such as Nike, Apple, and Amazon. However, even if the North Korea-U.S. denuclearization talks ease or lift sanctions, Vietnamese investors' willingness to invest in the North Korea has been most burdened by the possibility of closing special economic zones due to political risks. Last but not least, it is important to note that those willing to invest in North Korea are mostly smaller enterprises in textiles, sewing, footwear and leather industries-those that benefit from low-cost labor. Since their size is small, they need policy support in financing, especially in the early stages of their business. Even after they grow past the early stages, those without collateral would still need state guarantee letters to get financing. Thus, it is worth considering to use the Inter-Korean Cooperation Fund to compensate commercial banks for bad loan loss or for low-interest loans for smaller SMEs. The interviews with SMEs found that red-tape is one of the biggest difficulties they face. Thus, it is recommended that a one-stop service agency should be established to cover all processes and issues related to inter-Korean economic cooperation to eliminate redundancy and expediate government support for SMEs.

• Journal of Ginseng Culture
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• v.5
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• pp.32-51
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• 2023

Because red ginseng was exported in large quantities to the Qing Dynasty in the 19th century, a large-scale ginseng cultivation complex was established in Kaesong. Sibyunje (時邊制), a privately led loan system unique to merchants in Kaesong, made it possible for them to raise the enormous capital required for ginseng cultivation. The imperial family of the Korean Empire promulgated the Posamgyuchik (包蔘規則) in 1895, and this signaled the start of the red ginseng monopoly system. In 1899, when the invasion of ginseng farms by the Japanese became severe, the imperial soldiers were sent to guard the ginseng farms to prevent the theft of ginseng by the Japanese. Furthermore, the stateled compensation mission, Baesanggeum Seongyojedo (賠償金 先交制度), provided 50%-90% of the payment for raw ginseng, which was paid in advance of harvest. In 1895, rising seed prices prompted some merchants to import and sell poor quality seeds from China and Japan. The red ginseng trade order was therefore promulgated in 1920 to prohibit the import of foreign seeds without the government's permission. In 1906-1910, namely, the early period of Japanese colonial rule, ginseng cultivation was halted, and the volume of fresh ginseng stocked as a raw material for red ginseng in 1910 was only 2,771 geun (斤). However, it increased significantly to 10,000 geun between 1915 and 1919 and to 150,000 geun between 1920 and 1934. These increases in the production of fresh ginseng as a raw material for red ginseng were the result of various policies implemented in 1908 with the aim of fostering the ginseng industry, such as prior disclosure of the compensation price for fresh ginseng, loans for cultivation expenditure in new areas, and the payment of incentives to excellent cultivators. Nevertheless, the ultimate goal of Japanese imperialism at the time was not to foster the growth of Korean ginseng farming, but to finance the maintenance of its colonial management using profits from the red ginseng business.

• Journal of Service Research and Studies
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• v.13 no.2
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• pp.130-145
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• 2023

The digital transformation of finance is accelerating, and digital banking has already become a major banking channel. Banks have traditionally placed importance on CRM(Customer Relationship Management) and have tried to retain their loyal customers, who contribute significantly to the bank, such as long-term transactions, holding accounts with a certain balance or more, and holding loans. In this situation, this study exploratorily analyzed the consumer behavior of digital banking deposits in a major bank of Korea(1,145 samples). Statistical analysis was performed using SPSS. The main findings of the study are summarized as follows. It was found that there were differences of consumer behavior in digital banking deposits by generation, and the MZ generation used digital banking more on holidays than other generations. As a result of analyzing the behavior of existing loyal customers and regular customers of digital banking deposit, there was a significant difference in both the amount and period of the deposit. It was confirmed that the existing loyal customers of the bank also engage in consumer behavior that contributes to the bank in digital banking. In addition, the interaction between the customer type and the date of sign up for the deposit period, which is the goal setting of financial consumers, it was found that there was a significant effect. This study empirically analyzed the consumer behavior of digital banking in a situation where decrease of bank branches and encounters with digital banking. The major concepts of the consumer behavior theory are Loyal Customer, Goal Pursuit, and Habit, which were confirmed in an example of digital banking. The results of this study can suggest practical implications for existing banks and Internet-only banks, including the importance of customer management in digital banking.

• The Korean Journal of Air & Space Law and Policy
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• v.32 no.1
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• pp.177-200
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• 2017

This writer have thus far reviewed the civil and criminal obligations of the directors of a parent company that sponsored financially troubled affiliates. What was discussed here applies to logistics companies in the same manner. Hanjin Shipping cannot expect its parent company, Korean Air to prop it up financially. If such financial aid is offered without any collateral, under Korean criminal law, the directors of the parent company bears the burden of civil and criminal responsibility. One way to get around this is to secure fairness in terms of the process and the content of aid. Fairness in terms of process refers to the board of directors making public all information and approving such aid. Fairness in terms of content refers to impartial transactions that block out any possibilities of the chairman of the corporate group acting in his private interest. In the case of Korean Air bailing out Hanjin, the meeting of board of directors were held five times and a thorough review was conducted on the risks involved in the loans being repaid or not. After the review, measures to guard against undesirable scenarios were established before finally deciding on bailing out Hanjin. As such, there are no issues. In terms of the fairness of content, too, there were practically no room for the majority shareholder or controlling shareholder to pocket profits at the expense of the company. This is because the continued aid offered to a financially troubled company (i.e. Hanjin Shipping) was a posing a burden to even the controlling shareholder. This writer argues that the concept of the interest of the entire corporate group needs to be recognized. That is, it must be recognized that the relationship of control and being controlled between parent company and affiliate company, or between affiliate companies serves a practical benefit to the ongoing concern and growth of the group and is therefore just. Moreover, the corporate group and its affiliates, as well as their directors and management must recognize that they have an obligation to prioritize the interests of the corporate group ahead of the interests of the company that they are directly associated with. As such, even if Korean Air offered a loan to Hanjin Shipping without collateral, the act cannot be treated as an offense to law, nor can the directors be accused of damages that they bear the responsibility of compensating under civil law.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.83-102
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• 2021

The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altman's Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accuracy around 50% of the predicted probability of the default. When it comes to the distribution of samples for each predicted probability of the default, both LightGBM and XGBoost models have a relatively large number of samples for both 0~10% and 90~100% of the predicted probability of the default. Although Random Forest model has an advantage with regard to the perspective of classification accuracy with small number of cases, LightGBM or XGBoost could become a more desirable model since they classify large number of cases into the two extreme intervals of the predicted probability of the default, even allowing for their relatively low classification accuracy. Considering the importance of type 2 error and total prediction accuracy, XGBoost and DNN show superior performance. Next, Random Forest and LightGBM show good results, but logistic regression shows the worst performance. However, each predictive model has a comparative advantage in terms of various evaluation standards. For instance, Random Forest model shows almost 100% accuracy for samples which are expected to have a high level of the probability of default. Collectively, we can construct more comprehensive ensemble models which contain multiple classification machine learning models and conduct majority voting for maximizing its overall performance.