• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.63
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• pp.245-260
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• 2014

This study is aimed to summarize a tense situation of Risk management for Kaesong Industrial Business Enterprise in 2013 and to investigate trade insurance of K-sure. Now we have to find a new way to protect Kaesong Industrial Business Enterprises from uncertain environment and also need to prevent a recurrence of parallel cases in the domain of South-North economic cooperation in Korean peninsula. There are two method to protect Kaesong Industrial Business Enterprises. First they rely on the Korea government for protection. Second they need to effect trade insurance of K-sure. such as Export Credit Guaranty or Short-term Export Insurance. They shall create a wise predictable environment to protect Kaesong Industrial Business Enterprises themselves without resort to Korea government. Of course there are many things left behind to consider I hope it will be helpful to those who prepare South-North economic cooperation especially in Kaesong Industrial Complex.

• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.42
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• pp.133-160
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• 2009

In International trade the buyer and seller are normally separated from on another not only by distance but also by differences in language and culture. It is rarely possible for the performance of obligations to be simultaneous and the performance of contracts therefore calls for trust in a situation in which the parties are unlikely to feel able to trust each other unless they have a longstanding and successful relationship. Thus the seller under an international contract of sale will not wish to surrender documents of title to goods to the buyer until he has at least an assurance of payment, and no buyer will wish to pay for goods until he has received them. A gap of distrust thus exists which is often bridged by the undertaking of an intermediary known and trusted by both parties who will undertake on his own liability to pay the seller the contract price in return for the documents of title and then pass the documents to the buyer in return for the reimbursement. This is a common explanation of the theory behind the documentary letter of credit in which the undertaking of a bank of international repute serves as a "guarantee" to each party that the other will perform his obligations. The independence principle, also referred to as the "autonomy principle", is at the core of letter of credit or bank guarantee law. This principle provides that the letter of credit or bank guarantee is independent of the underlying contractual commitment - that is, the transaction that the credit is intented to secure - between the applicant and the beneficiary ; the credit is also independent of the relationship between the bank and its customer, the applicant. The most important exception to the independence principle is the doctrine of fraud in the transaction. A strict interpretation of the rule that the guarantee is independent of the underlying transaction would lead to the conclusion that neither fraud nor manifest abuse of rights by the beneficiary would constitute an objection to payment. There is one major problem related to "Independent guarantees", namely abusive or unfair callings. The beneficiary may make an unfair calling under the guarantee. The countermeasure of beneficiary's unfair calling divided three cases. First, advance countermeasure namely by contract. In other words, when the formation of the contract, the parties must insert the Force Majeure Clause, Arbitration Clause to Contract, and clear statement to the condition for demand calling. Second, post countermeasure namely by court. Many countries, including the United States, authorize the courts to grant an order enjoining the issuer from paying or enjoining the beneficiary from receiving payment under the guaranty letter. Third, Export Insurance. For example, the Export Credit Guarantees Department is prepared, subject to certain conditions, to cover the risk of unfair calling. Of course, KEIC in Korea is cover the risk of the all things for guarantees. On international projects, contractor performance is usually guaranteed by either a standby letters of credit or Independent guarantee. These instruments will be care the parties.

• 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.