• International Commerce and Information Review
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• v.11 no.1
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• pp.265-287
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• 2009

Most of the art gallery has been running their own websites. Not only in physical space but also in cyber space, they are achieving their goals through introducing exhibition and the artist and selling the artist's works. To this end, what are considered important items to the website contents of a large art gallery are assessed. These items include the goals and the mission of the art gallery, the targets of users, the scope of the information, the payment of information fees, relationships with other resources, reliability, accuracy and objectivity. And the analysis of these assessments are to promote the content of the gallery's website, and how to further promote customer satisfaction through the help is provided.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.06a
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• pp.243-252
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• 2001

Recently, as most companies recognize the importance of the customer relationship management, they strongly believe that they must know who their customers are. The job of a customer is very important information for us to understand the customer. However, since most customers are reluctant to reveal themselves, they do not let us know their jobs, and even provide false information about their jobs. The target domain of our research is mobile telecommunication. In this research, we developed a system that identifies the customer's job by utilizing the Call Detailed Record. From the Call Detailed Record, we extracted such variables as 'Average Monthly Payment'and 'Age of the Customer'and so forth. Moreover, we generated many summary variables and derived variables such as 'Number of Calls during Work Hours in Weekday', and 'Ratio of Calls from other Mobile Telephone'. Using artificial neural networks, we developed a two-step Job Identification System. In the first step, it identifies the four job classes then in the second step, it subdivides these four job classes into seven jobs. The accuracy of identifying the seven jobs was 69.1%.

• Electronics and Telecommunications Trends
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• v.34 no.2
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• pp.29-39
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• 2019

The interest in facial recognition technology has been growing with the advancement of AI technology. With a confirmed accuracy of over 99%, the areas of application of the technology have expanded, including smartphone unlocking, online payment authorization, building access management, and criminal apprehension. This indicates that the technology has effectively transitioned from laboratory to field applications. This study performs patent analysis to determine recent innovations and diffusion trends in facial recognition technology. Specifically, R&D activities involving facial recognition technology are investigated at both the country level and company level. Significant patents are also considered. This study contributes to R&D management teams by proposing useful plans and strategies.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.6
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• pp.1525-1540
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• 2015

Due to a rapid advancement in the electronic commerce technology, the payment method varies from cash to electronic settlement such as credit card, mobile payment and mobile application card. Therefore, financial fraud is increasing notably for a purpose of personal gain. In response, financial companies are building the FDS (Fraud Detection System) to protect consumers from fraudulent transactions. The one of the goals of FDS is identifying the fraudulent transaction with high accuracy by analyzing transaction data and personal information in real-time. Data mining techniques are providing great aid in financial accounting fraud detection, so it have been applied most extensively to provide primary solutions to the problems. In this paper, we try to provide an overview of the research on data mining based fraud detection. Also, we classify researches under few criteria such as data set, data mining algorithm and viewpoint of research.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.12
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• pp.671-676
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• 2016

Recently, according to the limelight of Fintech, simple payment using biometric at smartphone is widely used. In this paper, we propose a new contactless biometric method using thumb image without additional sensors unlike previous biometrics such as fingerprint, iris, and vein recognition. In our method, length, width, and skin texture information are used as features. For that, illumination normalization, skin region segmentation, size normalization and alignment procedures are sequentially performed from the captured thumb image. Then, correlation coefficient is calculated for similarity measurement. To analyze recognition accuracy, genuine and imposter matchings are performed. At result, we confirmed the FAR of 1.68% at the FRR of 1.55%. In here, because the distribution of imposter matching is almost normal distribution, our method has the advantage of low FAR. That is, because 0% FAR can be achieved at the FRR of 15%, the proposed method is enough to 1:1 matching for payment verification.

• Journal of Information Technology Services
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• v.11 no.4
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• pp.233-250
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• 2012

In this paper, credit card delinquency means the possibility of occurring bad debt within the certain near future from the normal accounts that have no debt and the problem is to predict, on the monthly basis, the occurrence of delinquency 3 months in advance. This prediction is typical binary classification problem but suffers from the issue of data imbalance that means the instances of target class is very few. For the effective prediction of bad debt occurrence, Support Vector Machine (SVM) with kernel trick is adopted using credit card usage and payment patterns as its inputs. SVM is widely accepted in the data mining society because of its prediction accuracy and no fear of overfitting. However, it is known that SVM has the limitation in its ability to processing the large-scale data. To resolve the difficulties in applying SVM to bad debt occurrence prediction, two stage clustering is suggested as an effective data reduction method and ensembles of SVM models are also adopted to mitigate the difficulty due to data imbalance intrinsic to the target problem of this paper. In the experiments with the real world data from one of the major domestic credit card companies, the suggested approach reveals the superior prediction accuracy to the traditional data mining approaches that use neural networks, decision trees or logistics regressions. SVM ensemble model learned from T2 training set shows the best prediction results among the alternatives considered and it is noteworthy that the performance of neural networks with T2 is better than that of SVM with T1. These results prove that the suggested approach is very effective for both SVM training and the classification problem of data imbalance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.827-833
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• 2020

In this paper, to reduce the unpaid rate of local governments, the internal data elements affecting the arrears in Water-INFOS are searched through interviews with meter readers in certain local governments. Candidate data affecting arrears from national statistical data were derived. The influence of the independent variable on the dependent variable was sampled by examining the disorder of the dependent variable in the data set called information gain. We also evaluated the higher prediction rates of decision tree and logistic regression using n-fold cross-validation. The results confirmed that the decision tree can find more accurate customer payment patterns than logistic regression. In the process of developing an analysis algorithm model using machine learning, the optimal values of two environmental variables, the minimum number of data and the maximum purity, which directly affect the complexity and accuracy of the decision tree, are derived to improve the accuracy of the algorithm.

• Health Policy and Management
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• v.14 no.1
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• pp.121-147
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• 2004

Since the pilot program for a DRG-based prospective payment system was introduced in 1997, the performance of KDRGs has been one of hotly debated issues. The objectives of this study are to refine the classification algorithm of the KDRGs and to assess the improvement achieved by the refinement. The U.S. Medicare DRGs version 17.0 and the Australian Refined DRGs version 4.1 were reviewed to identify areas of possible impro-vement. Refined changes in the classification and result of date analyses were submitted to a panel of 48 physicians for their reviews and suggestions. The refinement was evaluated by the variance reduction in resource utilization achieved by the KDRG The database of 2,182,168 claims submitted to the Health Insurance Review Agency during 2002 was used for evaluation. As the result of the refinement, three new MDCs were introduced and the number of ADEGs increased from 332 to 674. Various age splits and two to four levels of severity classification for secondary diagnoses were introduced as well. A total of 1,817 groups were defined in the refined KDRGs. The variance reduction for charges of all patients increased from 48.2％ to 53.6％ by the refinement, and from 65.6％ to 73.1％ for non-outlier patients. The r-square for length of stays of all patients was increased from 28.3％ to 32.6％, and from 40.4％ to 44.9％ for non-outlier patients. These results indicated a significant improvement in the classification accuracy of the KDRG system.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.1167-1174
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• 2020

This study aims to evaluate the effect of computerized tax services in improving tax performance by moderating governance principles in Jordanian tax income departments. The study is based on a questionnaire distributed to income tax auditors, who were chosen by the simple random sampling method, so that 170 questionnaires were subjected to statistical analysis. The study models were formulated in the form of simple and multiple regression equations to test the hypotheses of the study, in addition to relying on One Sample T-test to calculate the mean of questionnaire answers. The most prominent research results is that the application of tax governance principles through the provision of computerized tax services is reflected in the increase in confidence between taxpayers and income tax departments, the efficiency of tax performance, and tax proceeds. Also, the study pointed out the importance of the income tax departments to prepare strategic plans regarding the development and the follow-up of modern technologies related to computerized tax services. It especially regards linking and collecting tax from taxpayers such as via electronic tax payment and collection system, in order to ensure the speed of completion, accuracy of calculation, and raising the efficiency of tax performance.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.2
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• pp.35-54
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• 2009

The recent economic crisis not only reduces the profit of department stores but also incurs the significance losses caused by the increasing late-payment rate of credit cards. Under this pressure, the scope of credit prediction needs to be broadened from the simple prediction of whether this customer has a good credit or not to the accurate prediction of how much profit can be gained from this customer. This study classifies the delinquent customers of credit card in a Korean department store into homogeneous clusters. Using this information, this study analyzes the repayment patterns for each cluster and develops the credit prediction system to manage the delinquent customers. The model presented by this study uses Kohonen network, which is one of artificial neural networks of data mining technique, to cluster the credit delinquent customers into clusters. Cox proportional hazard model is also used, which is one of survival analysis used in medical statistics, to analyze the repayment patterns of the delinquent customers in each cluster. The presented model estimates the repayment period of delinquent customers for each cluster and introduces the influencing variables on the repayment pattern prediction. Although there are some differences among clusters, the variables about the purchasing frequency in a month and the average number of installment repayment are the most predictive variables for the repayment pattern. The accuracy of the presented system leaches 97.5%.