• The Journal of Society for e-Business Studies
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• v.11 no.4
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• pp.87-106
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• 2006

Today as the scale of e-commerce constantly expands, the number and the amount of the consumer frauds are also increasing very rapidly, without sufficient levels of systematic support to prevent them. Internet Escrow service is one of the promising payment mechanisms, which guarantees secure electronic trades and payments. Especially, if the real-time product delivery information is available via RFID-based track-and-trace environment, the security and efficiency of the Internet Escrow services would be improved a lot. In this research, proposed a novel approach to integrate EPCglobal Network, which is a de-facto standard for RFID-based information network model, with Internet Escrow services. The proposed service model was implemented in the form of "Integrated Financial Platform", which supports the contracts among trading partners and the payment via Escrow services by being fully integrated with bank systems. Using the implemented EPCglobal Network-based Escrow service system, we would be able not only to shorten the money-flow cycle and to develop new kinds of loan services, but also to overcome the problems of existing Escrow services including the lack of product-related information and the delay of purchasing decisions.

• Korean Journal of Construction Engineering and Management
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• v.19 no.4
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• pp.21-33
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• 2018

In 2018, the real estate markets have hardly been transacted according to the government's tight regulations of real estates, and have the high possibility to reach a low hit due to the hike of loan interest rates following the U. S rise of base money rate. The key profits for the large construction companies mainly come from the overseas plant projects and the domestic non-governmental construction projects. They suffered a lot such as the lowering of their credit ratings due to the large losses caused by the frquent design changes and work delay. Even in the domestic non-governmental construction projects, the general business risks are on the rise due to the property marketing moving over to the decreasing phase. The small and medium sized security companies has realized a lot of operaring profits as they participated in the PF market to make up for the losses in the securities trading business. But, now as the housing market is not so good around the nation except Seoul and the financial states of large construction companies are not good enough, they can face the liquidity crisis if there happens the problems in the PF backed securities which they have handled. As Korean economy experienced the crisis in the savings banks before, it is recommended that Financial Supervisory Service proposes the preemptive control method and supervision direction to overcome the crisis.

• 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.