• Journal of Electrical Engineering and Technology
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• v.1 no.1
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• pp.1-7
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• 2006

In an electricity market, the spot market is normally integrated with a forward or future market. The advantage of the forward market is to allow the market participants to deal in a part or the whole trading portfolio at a fix price in advance and to avoid risk associated to the uncertain price of the spot market. Japan has introduced a continuous auction base forward market from April 2005. This paper analyzes the Japanese forward market rules and operations, and introduces a new algorithm that may improve the efficiency of the market itself. The proposed algorithm enables us to give consideration to the specific characteristics of the power system and to integrate them in the auction mechanism. The benefits of the proposed algorithm are verified on an electronic simulation platform and the results described in this paper.

• Proceedings of the Safety Management and Science Conference
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• 2002.11a
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• pp.295-304
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• 2002

This case describes key development challenges and patterns as experienced by Internet Auction Company, Ltd. from the startup stage through IPO to the sale to eBay. Currently an eBay company, Internet Auction Co. continues to pursue new auction systems and substitute distribution systems for the future with a renewed entrepreneurial spirit. In 1997, Mr Hyuk Oh saw a business opportunity in the Internet allowing two-way communications. At that time, the success stories of eBay and Onsale in the United States were good stimuli to his startup of an auction site. He opened the Website in April 1, 1998 after four months of development efforts. Auction has merit more than other shopping malls. First, it brings about interesting and benefit. Second, it has interactive trade system. Third, customers will trade for reasonable price through direct own will of them. Fourth, Auction's model brings about multitude sale through aggressive sales. Even if Network Marketing has socially negative effect in case of inappropriate use, it is good mood for further business. In case E-Commerce use as useful purpose, it will have competitive priority revenue model.

• The Journal of Information Technology
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• v.6 no.4
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• pp.69-80
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• 2003

The electronic commerce continues to grow dramatically. Also, consumer complaints and damages related to e-commerce grow rapidly. The analysis of consumer complaints showed that consumer of e-commerce tended to buy the various product categories including cloth and home appliance. The damages of delivery problem rapidly rose by 1,185.3%. In categories of transaction including of 'internet shopping mall', 'internet contents' and 'internet auction', about 90% of consumer damages was related to 'internet shopping mall' but consumer damages of 'internet contents' was anticipated to be increased in the near future. The major goods and services of damages was 'digital camera', notebook PC', 'internet game service' etc. Therefore, it is required to establish consumer protection measures to be prevent consumer fraud such as internet shopping mall, Halfplaza.com, and major goods and services of damages. Also, it is need to establish system of spontaneous consumer protection improving consciousness of e-commerce companies.

• 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.