• Journal of Legislation Research
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• no.53
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• pp.553-591
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• 2017

This article discussed whether so-called 'foreign judgments obtained by fraud' is in breach of public policy provided in Article 217(1)(3) of Civil Procedure Act and, if so, what the specific requirements could be. The summary of the conclusion is as follows. The 'foreign judgments obtained by fraud' is against the municipal procedural public policy and then shall not be recognized. In this regard one more question comes up whether reviewing if 'foreign judgments obtained by fraud' is in breach of the municipal procedural public policy is allowed in consideration of the principle of prohibition of $r{\acute{e}}vision$ au fond. Since the principle is applied entirely in the course of the above reviewing, it is allowed only when it does not breach the principle. The two instances that the reviewing is allowed are where the defendant was not able to produce evidences of fraud during foreign procedures and where the defendant's claim of fraud without evidences was rejected by the foreign court and then evidences of fraud were found after the foreign procedure was completed. On the other hand, the specific requirements for 'foreign judgments obtained by fraud' to be against public policy are following four requirements based on principle of strict interpretation of public policy. (1) plaintiff's intention to fraud, (2) preventing the defendant from being involved in the procedure by fraud or cheating the foreign court using manipulated evidences, (3) the defendant could not present himself in the foreign court procedure due to the plaintiff's extraneous fraud or the foreign court decided wrongly due to intrinsic fraud, and (4) defendant's fundamental procedural rights were breached to the extent that recognizing the effect of foreign judgments was against justice defendant's fundamental procedural rights. These results differ from the Supreme Court 2004. 10. 28. ruling 2002da74213 in many aspects. Most of all, in my opinion there is no need to distinguish between intrinsic fraud and extraneous fraud and reviewing 'foreign judgments obtained by fraud' is not in conflict with the principle of prohibition of $r{\acute{e}}vision$ au fond but the both may coexist. In this regard I expect the variation of the Supreme Court's position and hope to contribute to academia and practitioners.

• Journal of Intelligence and Information Systems
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• v.16 no.1
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• pp.93-116
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• 2010

Ensemble approach is applied to the detection modeling of illegal cash accommodation (ICA) that is the well-known type of fraudulent usages of credit cards in far east nations and has not been addressed in the academic literatures. The performance of fraud detection model (FDM) suffers from the imbalanced data problem, which can be remedied to some extent using an ensemble of many classifiers. It is generally accepted that ensembles of classifiers produce better accuracy than a single classifier provided there is diversity in the ensemble. Furthermore, recent researches reveal that it may be better to ensemble some selected classifiers instead of all of the classifiers at hand. For the effective detection of ICA, we adopt ensemble size reduction technique that prunes the ensemble of all classifiers using accuracy and diversity measures. The diversity in ensemble manifests itself as disagreement or ambiguity among members. Data imbalance intrinsic to FDM affects our approach for ICA detection in two ways. First, we suggest the training procedure with over-sampling methods to obtain diverse training data sets. Second, we use some variants of accuracy and diversity measures that focus on fraud class. We also dynamically calculate the diversity measure-Forward Addition and Backward Elimination. In our experiments, Neural Networks, Decision Trees and Logit Regressions are the base models as the ensemble members and the performance of homogeneous ensembles are compared with that of heterogeneous ensembles. The experimental results show that the reduced size ensemble is as accurate on average over the data-sets tested as the non-pruned version, which provides benefits in terms of its application efficiency and reduced complexity of the ensemble.

• International Journal of Advanced Culture Technology
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• v.12 no.1
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• pp.293-298
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• 2024

The purpose of an internal control system is to prevent the occurrence of errors and fraud in the process of producing accounting information, thereby providing investors with reliable information. For the effective operation of an internal control system, it is necessary to secure a sufficient number of personnel and experienced staff. This study focuses on the personnel directly involved in producing accounting information, examining whether companies that invest in their internal control staff experience a mitigation in the phenomenon of valuation errors. The analysis revealed that the size and experience months of the personnel responsible for internal control have a significant negative relationship with valuation errors. This result implies that by securing sufficient personnel for the smooth operation of the internal control system and placing experienced staff within the system, investors can effectively make judgments about the intrinsic value based on quality accounting information, thereby reducing valuation errors.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.877-884
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• 2011

The decision tree is a representative algorithm of data mining and used in many domains such as retail target marketing, fraud detection, data reduction, variable screening, category merging, etc. This method is most useful in classification problems, and to make predictions for a target group after dividing it into several small groups. When we create a model of decision tree with a large number of input variables, we suffer difficulties in exploration and analysis of the model because of complex trees. And we can often find some association exist between input variables by external variables despite of no intrinsic association. In this paper, we study on the removal method of unnecessary input variables using multiple external association rules. And then we apply the removal method to actual data for its efficiencies.