• Title, Summary, Keyword: Detect unusual trading

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Real-Time Fraud Detection using Data Quality Diagnosis Techniques for R&D Grant (데이터 품질진단 기법을 이용한 연구개발비 이상거래 실시간 탐지)

  • Jang, Ki-Man;kim, Chang-Su;Jung, Hoe-kyung
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.19 no.11
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    • pp.2609-2614
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    • 2015
  • National research and development projects institutions have implemented various measures in order to prevent R&D expenses abuse and negate enforcement. but it reveals a limit to prevent abuse of R&D expenses[1,2]. In this paper, to prevent abuses resulting from the R & D for the unusual trading post caught collecting information from the R & D phase implementation plan to detect unusual transactions. The results are subjective and research institutions, and specialized agencies to take advantage of shared, real-time cross-linkage between the credit card companies. Studies of data quality diagnostic techniques developed for this purpose related regulations and manuals, Q & A, FAQ, Outside-in business rules that derive from a variety of information, such as personnel interviews (Outside-In) was used for analysis.

Improving the Performance of Market Surveillance (증권시장에서의 효과적인 주가감시모형)

  • 안철환
    • Journal of the Korean Society for Quality Management
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    • v.28 no.1
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    • pp.1-12
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    • 2000
  • Since Black Monday there has been a rash of systems developments which aimed at automating and upgrading the surveillance mechanism of monitoring the many facets of security trading. A more sophisticated mathematical model for detecting abnormal trading activities was created by Davis and Ord of Penn State along with Nobel prize laureates Solow and Modigliani of MIT. They used CAPM(Capital Asset Pricing Model) to explain the movements of stock price and applied an idea of residuals to detect unusual movements. In this paper, their idea is discussed and a new method is proposed, which involves a confidence interval of future observation in linear regression. One of the examples of the stock watch system adopting this statistical method is also presented.

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Efficient Inverter Type Compressor System using the Distribution of the Air Flow Rate (공기 변화량 분포를 이용한 효율적인 인버터타입 압축기 시스템)

  • Shim, JaeRyong;Kim, Yong-Chul;Noh, Young-Bin;Jung, Hoe-kyung
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.19 no.10
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    • pp.2396-2402
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    • 2015
  • Air compressor, as an essential equipment used in the factory and plant operations, accounts for around 30% of the total electricity consumption in U.S.A, thereby being proposed advanced technologies to reduce electricity consumption. When the fluctuation of the compressed airflow rate is small, the system stability is increased followed by the reduction of the electricity consumption which results in the efficient design of the energy system. In the statistical analysis, the normal distribution, log normal distribution, gamma distribution or the like are generally used to identify system characteristics. However a single distribution may not fit well the data with long tail, representing sudden air flow rate especially in extremes. In this paper, authors decouple the compressed airflow rate into two parts to present a mixture of distribution function and suggest a method to reduce the electricity consumption. This reduction stems from the fact that a general pareto distribution estimates more accurate quantile value than a gaussian distribution when an airflow rate exceeds over a large number.

Detection of Phantom Transaction using Data Mining: The Case of Agricultural Product Wholesale Market (데이터마이닝을 이용한 허위거래 예측 모형: 농산물 도매시장 사례)

  • Lee, Seon Ah;Chang, Namsik
    • 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.