• Management & Information Systems Review
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• v.14
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• pp.151-180
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• 2004

Owing to the digital revolution, Internet Commerce and Electronic commerce, revolutionize the way of doing business and making payment. The entrance of the Internet has a prominent for spread of Electronic Commerce and those phenomenons will result in paperless trading and cashless trade. By virtue of Internet, an increasing share of business transactions occurs online. Electronic payment is essential for the smooth progress of the electronic commerce as electronic payment plays the important role in the electronic commerce, that is, the value transfer restyling from the electronic commerce. Traditionally international settlement systems such as letters of credits, remittance and documentary collections operated as important and poplar method of payment, Now, information technology has made it possible to pay for the sale of goods and services over the internet. In international trade, there are service providers (bolero, TradeCard, BeXcom) to settle payment electronically through the Internet. The purpose of this study is to Conduct comparative analysis with approach manner functional respect systematic respect, role. It is shown which the Electronic payment system is better. In this study, the author attempts to find the problems is (bolero, TradeCard, BeXcom) and solutions in switching from the documentary payment system to the electronic one. This conclusion of this study can be summarized as followings. In resoect of the law, bolero should seek to prevert the users from being treated unfairly due to multilateral agreement on Rulebook. TradeCard, BeXcom do not have the proper law that users are governed. so far as the practice problems concerned, stability of computer's operation and security of message interchange should be warranted and improved continuously. Through the standardization of the electronic document and the development of software, the examination of the shipping occuments must be done automatically. Bolero should induce more banks to take part in Bolero, and make the carrier the cost and time in managing the traditional document which will be used for the time being. In respect of information technology and security, to deduce the risk in the electronic settlement system and positively uses the global authentication guideline(Identrus).

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.5
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• pp.113-130
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• 2011

Due to the development of information and communication technology, the global influence on politics, economics, society, and culture has grown. A major example of this impact on the economic sector is the growth of e-commerce, which increases both the speed and efficiency of businesses. In light of these new developments, businesses need to shift away from the misconception that information overwhelms to embrace the enhanced competitiveness that e-commerce provides. However, concern about fraudulent transactions through e-commerce is pertinent because of the loss in both critical revenue and consumer confidence in open markets. Current solutions for fraudulent transactions include real-time monitoring and processing, payment pending, and confirmation through SMS, E-mail, and other wired means. Our research focuses on the management of Fraud Detection Systems (FDS) to safeguard online electronic payment systems. With effective implementation of our research we hope to foster an honorable online trading culture and protect consumers. Future comparative research in domestic and abroad markets would provide further insight into preventing fraudulent transactions.

• Journal of Convergence Society for SMB
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• v.6 no.2
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• pp.19-24
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• 2016

A Study on the Information Security System of Fin-Tech Business In traditional electronic commerce, there have not been severe issues of trading information through documents in paper or the closed EDI. The scale of e-commerce has increased as internet develops, however, turning to the online e-commerce, which caused a number of issues such as authentication, information forgery, and non-repudiation between the parties. To prevent conflicts from such troubles and perform the post management, security technologies are applied throughout the process of e-commerce, certificates intervening. Lately, meanwhile, FinTech has been creating a sensation around the mobile payment service. Incidents of information leakage from card corporations and hackings imply the need of securing safety of the financial service. Development and evolution of FinTech industry must be accompanied by information protection. Therefore, this research aims to inquire into the information security system of leading FinTech company in a foreign country.

• Proceedings of the Korea Contents Association Conference
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• 2008.05a
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• pp.431-435
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• 2008

Thanks to the development of internet, on-line market expands exponentially and corresponding solid business models are drawn attentions. Most of on-line trading items are limited with selling game related items however, Linden Lab made its turning point by introducing cyber reality game to shape the cyber life with creating his own Avatar in 2003. After 2003, Second life has grown sharply that over 12 million users around the world. While former games are progressed within fixed scenario, the concept of avatars who live his or her own lives at the cyber space that successfully differentiate from former online game. Further, cyber money, Linden Dollar can be used to buy real estate, cloth, shoes just like at real economy system. Not only for using corporate marketing, various areas of activities; promotion of public sector, politics, education are also functioned at the cyber life. In Korea, Korean version of Second life was introduced at the end of 2007 that draws attentions from the users. In this study, I examine various business models of cyber through Second life and suggest feasible culture-contents applying models.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.3
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• pp.1439-1444
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• 2013

Recently, as the use of the applications like online banking and stock trading is increasing by the rapid development of the network, security of data content is becoming more and more important. Accordingly, public key or symmetric key encryption algorithm is widely used in open networks such as the internet for the protection of data. Generally, public key cryptographic systems is based on two famous number theoretic problems namely factoring or discrete logarithm problem. So, public key cryptographic systems is relatively slow compared to symmetric key cryptography systems. Among public key cryptographic systems, the advantage of ECC compared to RSA is that it offers equal security for a far smaller key. For this reason, ECC is faster than RSA. In this paper, we propose a efficient key generation method for elliptic curve cryptography system based on the real number field.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.6
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• pp.253-260
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• 2015

When we buy items in online stores, it is common to face recommended items that meet our interest. These recommendation system help users not only to find out related items, but also find new things that may interest users. Recommendation system has been widely studied and various models has been suggested such as, collaborative filtering and content-based filtering. Though collaborative filtering shows good performance for predicting users preference, there are some conditions where collaborative filtering cannot be applied. Sparsity in user data causes problems in comparing users. Systems which are newly starting or companies having small number of users are also hard to apply collaborative filtering. Content-based filtering should be used to support this conditions, but content-based filtering has some drawbacks and weakness which are tendency of recommending similar items, and keeping history of a user makes recommendation simple and not able to follow up users preference changes. To overcome this drawbacks and limitations, we suggest weighted window assisted user history based recommendation system, which captures user's purchase patterns and applies them to window weight adjustment. The system is capable of following current preference of a user, removing useless recommendation and suggesting items which cannot be simply found by users. To examine the performance under user and data sparsity environment, we applied data from start-up trading company. Through the experiments, we evaluate the operation of the proposed recommendation system.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.161-177
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• 2015

With the rapid evolution of technology, the size, number, and the type of databases has increased concomitantly, so data mining approaches face many challenging applications from databases. One such application is discovery of fraud patterns from agricultural product wholesale transaction instances. The agricultural product wholesale market in Korea is huge, and vast numbers of transactions have been made every day. The demand for agricultural products continues to grow, and the use of electronic auction systems raises the efficiency of operations of wholesale market. Certainly, the number of unusual transactions is also assumed to be increased in proportion to the trading amount, where an unusual transaction is often the first sign of fraud. However, it is very difficult to identify and detect these transactions and the corresponding fraud occurred in agricultural product wholesale market because the types of fraud are more intelligent than ever before. The fraud can be detected by verifying the overall transaction records manually, but it requires significant amount of human resources, and ultimately is not a practical approach. Frauds also can be revealed by victim's report or complaint. But there are usually no victims in the agricultural product wholesale frauds because they are committed by collusion of an auction company and an intermediary wholesaler. Nevertheless, it is required to monitor transaction records continuously and to make an effort to prevent any fraud, because the fraud not only disturbs the fair trade order of the market but also reduces the credibility of the market rapidly. Applying data mining to such an environment is very useful since it can discover unknown fraud patterns or features from a large volume of transaction data properly. The objective of this research is to empirically investigate the factors necessary to detect fraud transactions in an agricultural product wholesale market by developing a data mining based fraud detection model. One of major frauds is the phantom transaction, which is a colluding transaction by the seller(auction company or forwarder) and buyer(intermediary wholesaler) to commit the fraud transaction. They pretend to fulfill the transaction by recording false data in the online transaction processing system without actually selling products, and the seller receives money from the buyer. This leads to the overstatement of sales performance and illegal money transfers, which reduces the credibility of market. This paper reviews the environment of wholesale market such as types of transactions, roles of participants of the market, and various types and characteristics of frauds, and introduces the whole process of developing the phantom transaction detection model. The process consists of the following 4 modules: (1) Data cleaning and standardization (2) Statistical data analysis such as distribution and correlation analysis, (3) Construction of classification model using decision-tree induction approach, (4) Verification of the model in terms of hit ratio. We collected real data from 6 associations of agricultural producers in metropolitan markets. Final model with a decision-tree induction approach revealed that monthly average trading price of item offered by forwarders is a key variable in detecting the phantom transaction. The verification procedure also confirmed the suitability of the results. However, even though the performance of the results of this research is satisfactory, sensitive issues are still remained for improving classification accuracy and conciseness of rules. One such issue is the robustness of data mining model. Data mining is very much data-oriented, so data mining models tend to be very sensitive to changes of data or situations. Thus, it is evident that this non-robustness of data mining model requires continuous remodeling as data or situation changes. We hope that this paper suggest valuable guideline to organizations and companies that consider introducing or constructing a fraud detection model in the future.

• Journal of Service Research and Studies
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• v.6 no.3
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• pp.29-40
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• 2016

Since the late 20th century, there has been much effort to improve the market value of media contents which are commercialized in a digital format, by fusing digital data of video, audio, numerals, characters with IT technology together. Then by what criteria and methodologies could the market value for the drama "Sons of the Sun" or the animated film 'Frozen', often referred to in the meida, be estimated? In the circumstances there has been little or no research on the valuation framework of media contents and the status of their valuation system development to date, we propose a practical valuation models for various purposes such as contents trading, review of investment adequacy, etc., by formalizing and presenting a contents valuation framework for the four types of media of movies, online games, and broadcasting commercials, and animations. Therefore, we develope computational methods of cash flows which includes production cost by media content types, provide reference databases associated with key variables of valuation (economic life cycle, discount rates, contents contribution and royalty rates), and finally propose the valuation framework of media contents based on both income approach and relief-from-royalty method which has been applied to valuation of intangible assets so far.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.123-138
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• 2017

Since the stock market is driven by the expectation of traders, studies have been conducted to predict stock price movements through analysis of various sources of text data. In order to predict stock price movements, research has been conducted not only on the relationship between text data and fluctuations in stock prices, but also on the trading stocks based on news articles and social media responses. Studies that predict the movements of stock prices have also applied classification algorithms with constructing term-document matrix in the same way as other text mining approaches. Because the document contains a lot of words, it is better to select words that contribute more for building a term-document matrix. Based on the frequency of words, words that show too little frequency or importance are removed. It also selects words according to their contribution by measuring the degree to which a word contributes to correctly classifying a document. The basic idea of constructing a term-document matrix was to collect all the documents to be analyzed and to select and use the words that have an influence on the classification. In this study, we analyze the documents for each individual item and select the words that are irrelevant for all categories as neutral words. We extract the words around the selected neutral word and use it to generate the term-document matrix. The neutral word itself starts with the idea that the stock movement is less related to the existence of the neutral words, and that the surrounding words of the neutral word are more likely to affect the stock price movements. And apply it to the algorithm that classifies the stock price fluctuations with the generated term-document matrix. In this study, we firstly removed stop words and selected neutral words for each stock. And we used a method to exclude words that are included in news articles for other stocks among the selected words. Through the online news portal, we collected four months of news articles on the top 10 market cap stocks. We split the news articles into 3 month news data as training data and apply the remaining one month news articles to the model to predict the stock price movements of the next day. We used SVM, Boosting and Random Forest for building models and predicting the movements of stock prices. The stock market opened for four months (2016/02/01 ~ 2016/05/31) for a total of 80 days, using the initial 60 days as a training set and the remaining 20 days as a test set. The proposed word - based algorithm in this study showed better classification performance than the word selection method based on sparsity. This study predicted stock price volatility by collecting and analyzing news articles of the top 10 stocks in market cap. We used the term - document matrix based classification model to estimate the stock price fluctuations and compared the performance of the existing sparse - based word extraction method and the suggested method of removing words from the term - document matrix. The suggested method differs from the word extraction method in that it uses not only the news articles for the corresponding stock but also other news items to determine the words to extract. In other words, it removed not only the words that appeared in all the increase and decrease but also the words that appeared common in the news for other stocks. When the prediction accuracy was compared, the suggested method showed higher accuracy. The limitation of this study is that the stock price prediction was set up to classify the rise and fall, and the experiment was conducted only for the top ten stocks. The 10 stocks used in the experiment do not represent the entire stock market. In addition, it is difficult to show the investment performance because stock price fluctuation and profit rate may be different. Therefore, it is necessary to study the research using more stocks and the yield prediction through trading simulation.