• The Journal of Fisheries Business Administration
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• v.36 no.2 s.68
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• pp.1-24
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• 2005

The purpose of the study are summarized as follows : First, it has researched the possibility of the Distribution Information application in Sea Food Wholesale Market. Second, the effects which the Wholesale Market Company and the Intermediary Wholesaler Corporations can obtain in Sea Food Wholesale Market by building up a Distribution Information are classified into the effects of task, cost, and competition. It has analyzed the influence of these effects in direct and indirect Trust between the Wholesale Market Company and the Intermediary Wholesaler. Third, it has recognized the upcoming problems in Sea Food Wholesale Market by building up a Distribution Information, and it has suggested a plan to make the Distribution Information application successful in Sea Food Wholesale Market. This study has used a questionnaire to verify 5 hypotheses. Research model, factor analysis, correlation relationship analysis. The result of this study are summarized as follows : Building up the Distribution Information influences positively on the effectiveness of task, cost and competitiveness regardless of it being the Wholesale Market Company or the Intermediary Wholesaler corporation. However, the results of this analysis are to verify differences according to the degree of construction of the Distribution Information and the degree of the perception of the problems between the Wholesale Market Company and the Intermediary Wholesaler corporations have shown that there were distinct differences in the degree of computerization and of efforts to build a Distribution Information. Also there were distinct differences according to the degree of perceiving problems relating to building up the Distribution Information and the stages of the Distribution Information between the Wholesale Market Company and the Intermediary Wholesaler corporations. This study suggest three important steps that will help to establish a successful Distribution Information. First, the Wholesale Market Company and the Intermediary Wholesaler corporations should make efforts to increase mutual profits in partnership, and make direct Trust by sharing mutual information. Second, the lack of understanding of the Distribution Information between departments within the company requires educating employees about the Distribution Information. It is necessary to expand the communication networks of the Distribution Information between the Wholesale Market Company and the Intermediary Wholesaler. Third, mutual exchange of Information should be possible to offer systematic exchange of Information between the Wholesale Market Company and the Intermediary Wholesaler corporations.

• 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.