• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.10a
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• pp.68-98
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• 2004

Although the internet is useful for transferring information, Internet auction environments make fraud more attractive to offenders because the chance of detection and punishment are decreased. One of fraud is phantom transaction which is a colluding transaction by the buyer and seller to commit illegal discounting of credit card. They pretend to fulfill the transaction paid by credit card, without actual selling products, and the seller receives cash from credit card corporations. Then seller lends it out buyer with quite high interest rate whose credit score is so bad that he cannot borrow money from anywhere. The purpose of this study is to empirically investigate the factors to detect of the phantom transaction in online auction. Based up on the studies that explored behaviors of buyers and sellers in online auction, bidding numbers, bid increments, sellers' credit, auction length, and starting bids were suggested as independent variables. We developed an Internet-based data collection software agent and collect data on transactions of notebook computers each of which winning bid was over 1,000,000 won. Data analysis with logistic regression model revealed that starting bids, sellers' credit, and auction length were significant in detecting the phantom transaction.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.6
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• pp.255-264
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• 2016

Due to the development of information and communication technology, the great change on economics has grown and the biggest change is the e-commerce. With the methods of electronic financial frauds becoming advanced, reported phishing incidents have greatly increased. The Fraud Detection System(hereafter FDS) has taken effect to prevent electronic financial frauds, but economic losses still occurring. This Paper aims to analyze the financial environment, financial information technology environment, financial information technology security environment and some features of the institutional changes. In order to supplement the defect of FDS, it gives some recommendations for the improvement of the effective FDS Management System and information sharing on frauds with some public institution and a major consideration for collection or utilization of personal information.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.73-77
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• 2023

As the DeFi market continues to expand, fraudulent activities using smart contracts have also increased. HoneyPot and Ponzi schemes are well-known frauds that exploit smart contracts. While several studies have demonstrated the potential to detect smart contracts implementing these scams, there has been a lack of research focusing on simultaneously detecting both types of fraud. This paper addresses this gap by harnessing artificial intelligence to conduct experiments for the detection of both HoneyPot and Ponzi schemes. The study employs the CNN (Convolutional Neural Network) model, commonly used for malware detection. To effectively utilize CNN, the bytecode of smart contracts is transformed into visual representations. The experimental results showcase a recall rate of 0.89 and an F1 score of 0.85, indicating promising detection capabilities.

• 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 Preventive Medicine and Public Health
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• v.36 no.2
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• pp.147-152
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• 2003

Objectives : To develop a Diagnosis-Related Group (DRG) fraud candidate detection method, using data mining techniques, and to examine the efficiency of the developed method. Methods ; The Study included 79,790 DRGs and their related claims of 8 disease groups (Lens procedures, with or without, vitrectomy, tonsillectomy and/or adenoidectomy only, appendectomy, Cesarean section, vaginal delivery, anal and/or perianal procedures, inguinal and/or femoral hernia procedures, uterine and/or adnexa procedures for nonmalignancy), which were examined manually during a 32 months period. To construct an optimal prediction model, 38 variables were applied, and the correction rate and lift value of 3 models (decision tree, logistic regression, neural network) compared. The analyses were peformed separately by disease group. Results : The correction rates of the developed method, using data mining techniques, were 15.4 to 81.9%, according to disease groups, with an overall correction rate of 60.7%. The lift values were 1.9 to 7.3 according to disease groups, with an overall lift value of 4.1. Conclusions : The above findings suggested that the applying of data mining techniques is necessary to improve the efficiency of DRG fraud candidate detection.

• Journal of Korea Trade
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• v.24 no.1
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• pp.59-81
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• 2020

Purpose - This study analyzes the scope of due diligence and risks of banks and K-Sure in trade finance covered by EFF focusing on Moneual case, one of the latest and biggest trade finance fraud cases in Korea. Also, we suggest anti-fraud measures in trade finance on the part of banks and K-Sure in order to give them a desirable way of due diligence and reasonable risk management of export insurance. Design/methodology - Based on Moneual case of trade finance fraud, this study employs the methodology of an extended literature review and analysis of court decisions. Findings - Seoul High Court of Korea failed to decide whether K-Sure was wholly obliged to pay the insurance against the banks' EFF claims, but issued a compulsory mediation order, judging that both the banks and K-Sure were responsible by 50:50. The court may have judged that both the parties had lacked their due diligence in the trade finance. It is quite difficult for trade finance providers to manually investigate whether the transaction is suspected of trade finance fraud, so digitalization of trade finance which can facilitate the prevention and detection of trade fraud needs to be realized quickly. Since there has been no international rule available for open account trade finance up till now, clearly stipulated EFF terms on the exporter's genuine export obligation might have protected K-Sure from the disaster. Originality/value - This study investigates the due diligence of the banks and K-Sure in Moneual case which few researchers have considered, to the best of our knowledge. This study also suggests several practical methods (including block chain) to prevent complicating trade finance fraud amid increasing use of an open account, and further offers reasonable risk management of EFF employing international factoring rule which is also related to problematic open account trade finance.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.1
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• pp.133-146
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• 2015

Ever sophisticated e-finance fraud techniques have led to an increasing number of reported phishing incidents. Financial authorities, in response, have recommended that we enhance existing Fraud Detection Systems (FDS) of banks and other financial institutions. FDSs are systems designed to prevent e-finance accidents through real-time access and validity checks on client transactions. The effectiveness of an FDS depends largely on how fast it can analyze and detect abnormalities in large amounts of customer transaction data. In this study we detect fraudulent transaction patterns and establish detection rules through e-finance accident data analyses. Abnormalities are flagged by comparing individual client transaction patterns with client profiles, using the ruleset. We propose an effective flagging method that uses decision trees to normalize detection rules. In demonstration, we extracted customer usage patterns, customer profile informations and detection rules from the e-finance accident data of an actual domestic(Korean) bank. We then compared the results of our decision tree-normalized detection rules with the results of a sequential detection and confirmed the efficiency of our methods.

• The Korean Society of Law and Medicine
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• v.13 no.1
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• pp.41-69
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• 2012

First introduced in 1977, Korean health care system reached to national coverage in short period of time never seen before in any other countries, and rated as successful system protecting the health of the public at relatively low price. However, despite those positive evaluations, some of fraudulent medical organizations or pharmacies are hindering the sound development of the national health care system with meticulous false billing exaggerating the number of patients or the days of their treatment. To prevent aforementioned nursing home fraud and false billing, the misconduct should be punished as subject to the criminal law and severally punished for fines and payments which far exceed the expected amount of illicit gains as it is basically violation of criminal fraud, other than the forced return of illicit gains based on civil laws. Furthermore, the Health Insurance Review and Assessment Service should strengthen and complement the fraud investigators, the review process, and the professional training to raise the detection rates. It might also want to review ways to implement whistleblower rewarding system and rewards for evidences of healthcare fraud to overcome the limits of external review.

• Journal of Intelligence and Information Systems
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• v.16 no.1
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• pp.93-116
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• 2010

Ensemble approach is applied to the detection modeling of illegal cash accommodation (ICA) that is the well-known type of fraudulent usages of credit cards in far east nations and has not been addressed in the academic literatures. The performance of fraud detection model (FDM) suffers from the imbalanced data problem, which can be remedied to some extent using an ensemble of many classifiers. It is generally accepted that ensembles of classifiers produce better accuracy than a single classifier provided there is diversity in the ensemble. Furthermore, recent researches reveal that it may be better to ensemble some selected classifiers instead of all of the classifiers at hand. For the effective detection of ICA, we adopt ensemble size reduction technique that prunes the ensemble of all classifiers using accuracy and diversity measures. The diversity in ensemble manifests itself as disagreement or ambiguity among members. Data imbalance intrinsic to FDM affects our approach for ICA detection in two ways. First, we suggest the training procedure with over-sampling methods to obtain diverse training data sets. Second, we use some variants of accuracy and diversity measures that focus on fraud class. We also dynamically calculate the diversity measure-Forward Addition and Backward Elimination. In our experiments, Neural Networks, Decision Trees and Logit Regressions are the base models as the ensemble members and the performance of homogeneous ensembles are compared with that of heterogeneous ensembles. The experimental results show that the reduced size ensemble is as accurate on average over the data-sets tested as the non-pruned version, which provides benefits in terms of its application efficiency and reduced complexity of the ensemble.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.5
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• pp.1179-1195
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• 2018

Recently, a variety of attacks on web services have been occurring through a multiple access environment such as PC, tablet, and smartphone. These attacks are causing various subsequent damages such as online fraud transactions, takeovers and theft of accounts, fraudulent logins, and information leakage through web service vulnerabilities. Creating a new fake account for Fraud attacks, hijacking accounts, and bypassing IP while using other usernames or email addresses is a relatively easy attack method, but it is not easy to detect and block these attacks. In this paper, we have studied a method to detect online fraud transaction and obsession by identifying and managing devices accessing web service using web-based device fingerprinting. In particular, it has been proposed to identify devices and to manage them by scoring process. In order to secure the validity of the proposed scheme, we analyzed the application cases and proved that they can effectively defend against various attacks because they actively cope with online fraud and obtain visibility of user accounts.