• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.13
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• pp.537-561
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• 2000

In these days, there are various types of fraud in L/C transaction. But we has no article on fraud in the UCP. So the matter of fraud has been depended on the judgement of court of each country. But the judgements are different in each case. These cause the difficulties in practice. To solve this problem, it is desirable to insert the relative article in the UCP. I considered the article 5-109 of UCC for pre-study on this matter. But the article 5-109 of UCC has some problems. To arrange the relative article on fraud in the UCP, we have to consider more severely on article 5-109 of UCC. Especially, it should be studied on cases in practice. This is left for next study.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.6
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• pp.1475-1487
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• 2018

To verify remitter's identity when the remitter transfers money to a recipient using an electronic financial service provided by the financial institution, the remitter inputs the information; such as the withdrawal account number, the withdrawal amount, the password pre-registered with the financial company, or the information from authenticating medium that is previously distributed by the financial institution. However, the 1-Way transaction between the financial institution and the remitter is exposed to a great risk of accidents such as an anomaly remittance or a voice phishing fraud. Therefore, in this study, we propose a 2-WAY trade verification model for electronic financial transaction that can be mutually agreed by allowing the recipient to share the transaction information with the remitter and the financial company. We have improved the traditional electronic financial transaction's method by replacing it to 2-WAY trade method, and it is used for various purposes; such as preventing an error within the remittance or voice phishing fraud, enhancing loan transaction and contract transaction, etc. Through these variety of applications, we are expecting to reduce the inconveniences while improving the convenience of financial transaction and vitalizing the P2P transaction of financial institution.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.35-40
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• 2022

With the advent of the internet age and the outbreak of COVID-19, many companies have embraced online trade. However, due to the way the cyber economy works, the number of companies engaged in financial fraud by falsifying their transaction amounts and customer numbers has been gradually increasing. The purpose of this study is to analyze financial fraud of companies in the Internet era and to present solutions. Therefore, this study analyzed the financial fraud behavior of Luckin Coffee in China as an example and studied the causes and countermeasures of financial fraud. As a result, it was found that the cause of financial fraud lies in the opacity of cash flows from online transactions. The recommendations proposed by this study is to improve internal control systems in companies, develop risk management system, and establish comprehensive external supervision system

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.65-77
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• 2022

Due to the recent development in electronic financial services, transactions of electronic prepayment are rapidly growing, leading to growing fraud attempts. This paper proposes a methodology that can effectively detect fraud transactions in electronic prepayment by machine learning algorithms, including support vector machines, decision trees, and artificial neural networks. Actual transaction data of electronic prepayment services were collected and preprocessed to extract the most relevant variables from raw data. Two different approaches were explored in the paper. One is a transaction-based approach, and the other is a user ID-based approach. For the transaction-based approach, the first model is primarily based on raw data features, while the second model uses extra features in addition to the first model. The user ID-based approach also used feature engineering to extract and transform the most relevant features. Overall, the user ID-based approach showed a better performance than the transaction-based approach, where the artificial neural networks showed the best performance. The proposed method could be used to reduce the damage caused by financial accidents by detecting and blocking fraud attempts.

• Journal of the Korea Convergence Society
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• v.8 no.4
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• pp.19-24
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• 2017

The growing number of electronic financial transactions (e-banking) has entailed the rapid increase in security threats such as extortion and falsification of financial transaction data. Against such background, rigid security and countermeasures to hedge against such problems have risen as urgent tasks. Thus, this study aims to implement an improved case model by applying the Fraud Detection System (hereinafter, FDS) in a financial corporation 'A' using big data technique (e.g. the function to collect/store various types of typical/atypical financial transaction event data in real time regarding the external intrusion, outflow of internal data, and fraud financial transactions). As a result, There was reduction effect in terms of previous scenario detection target by minimizing false alarm via advanced scenario analysis. And further suggest the future direction of the enhanced FDS.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.5
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• pp.400-405
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• 2001

This paper addresses an effective approach of training neural networks classifiers for credit card fraud detection. The proposed approach uses evolutionary programming to trails the neural networks classifiers based on maximization of the detection rate of fraudulent usages on some ranges of the rejection rate, loot minimization of mean square error(MSE) that Is a common criterion for neural networks learning. This approach enables us to get classifier of satisfactory performance and to offer a directive method of handling various conditions and performance measures that are required for real fraud detection applications in the classifier training step. The experimental results on "real"credit card transaction data indicate that the proposed classifiers produces classifiers of high quality in terms of a relative profit as well as detection rate and efficiency.

• Asia pacific journal of information systems
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• v.28 no.4
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• pp.308-319
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• 2018

This paper aims at providing valuable insights on Financial Fraud Detection on a mobile money transactional activity. We have predicted and classified the transaction as normal or fraud with a small sample and massive data set using Azure and Spark ML, which are traditional systems and Big Data respectively. Experimenting with sample dataset in Azure, we found that the Decision Forest model is the most accurate to proceed in terms of the recall value. For the massive data set using Spark ML, it is found that the Random Forest classifier algorithm of the classification model proves to be the best algorithm. It is presented that the Spark cluster gets much faster to build and evaluate models as adding more servers to the cluster with the same accuracy, which proves that the large scale data set can be predictable using Big Data platform. Finally, we reached a recall score with 0.73, which implies a satisfying prediction quality in predicting fraudulent transactions.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.173-182
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• 2022

In this paper, we propose a machine learning model that can prevent fraudulent transactions in advance and interpret them using the XAI approach. For the experiment, we collected a real data set of 12,258 mobile phone sales posts from Joonggonara, a major domestic online C2C resale trading platform. Characteristics of the text corresponding to the post body were extracted using Doc2vec, dimensionality was reduced through PCA, and various derived variables were created based on previous research. To mitigate the data imbalance problem in the preprocessing stage, a complex sampling method that combines oversampling and undersampling was applied. Then, various machine learning models were built to detect fraudulent postings. As a result of the analysis, LightGBM showed the best performance compared to other machine learning models. And as a result of SHAP, if the price is unreasonably low compared to the market price and if there is no indication of the transaction area, there was a high probability that it was a fraudulent post. Also, high price, no safe transaction, the more the courier transaction, and the higher the ratio of 0 in the price also led to fraud.

• Journal of Korea Trade
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• v.24 no.1
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• pp.59-81
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• 2020

Purpose - This study analyzes the scope of due diligence and risks of banks and K-Sure in trade finance covered by EFF focusing on Moneual case, one of the latest and biggest trade finance fraud cases in Korea. Also, we suggest anti-fraud measures in trade finance on the part of banks and K-Sure in order to give them a desirable way of due diligence and reasonable risk management of export insurance. Design/methodology - Based on Moneual case of trade finance fraud, this study employs the methodology of an extended literature review and analysis of court decisions. Findings - Seoul High Court of Korea failed to decide whether K-Sure was wholly obliged to pay the insurance against the banks' EFF claims, but issued a compulsory mediation order, judging that both the banks and K-Sure were responsible by 50:50. The court may have judged that both the parties had lacked their due diligence in the trade finance. It is quite difficult for trade finance providers to manually investigate whether the transaction is suspected of trade finance fraud, so digitalization of trade finance which can facilitate the prevention and detection of trade fraud needs to be realized quickly. Since there has been no international rule available for open account trade finance up till now, clearly stipulated EFF terms on the exporter's genuine export obligation might have protected K-Sure from the disaster. Originality/value - This study investigates the due diligence of the banks and K-Sure in Moneual case which few researchers have considered, to the best of our knowledge. This study also suggests several practical methods (including block chain) to prevent complicating trade finance fraud amid increasing use of an open account, and further offers reasonable risk management of EFF employing international factoring rule which is also related to problematic open account trade finance.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.1
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• pp.115-128
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• 2017

The methods of electronic financial fraud continue to evolve. Various research and countermeasures have been proposed to counter this problem, but it is difficult to eradicate it. The purpose of this study is to analyze the risk of electronic financial fraud through MS Threat Risk Modeling and to propose the countermeasures against the electronic financial fraud. As a result of the analysis, it is confirmed that despite the difference of authentication methods, there is a high risk of pharming, and it is difficult to prevent attack by using only additional authentication means, device security or user authentication based security system. Therefore, this study suggests the introduction of preventive measures such as readjustment of transaction limit by security means, account authentication, and additional physical security measures. It also suggests the establishment and implementation of a comprehensive electronic financial fraud prevention policy through linkage of electronic fraud prevention system and improvement of public relations and user awareness.