• International Journal of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.243-248
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• 2022

Considering the current situation where cash transactions account for 51.5% of private consumption expenditure, it is very important to secure a tax base by exposing business operators' cash transactions. In the September 2011 national audit, it was pointed out that although a significant part of the investment amount of businesses (VAN operators, etc.) related to the issuance of cash receipts has been recovered, they are still supported through the state tax. At this point in time when a significant amount of the initial investment has been recovered, it is necessary to study a new way to support business operators through methods other than the tax credit method. This study proposes various methods to improve the current cash receipt system and describes the advantages and disadvantages of each method. The most important thing for the improvement of the cash receipt system is that the issuance of cash receipts should be beneficial to business operators. As a result of this study, the most desirable improvement method is to provide differential compensation for the discriminatory cost because the cost is different for each cash receipt operator. For this purpose, we analyze the best way to improve the cash receipt system is a tax credit method and a tax credit for maintenance costs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.30-45
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• 2024

This study introduces an effective method for predicting individual local tax delinquencies using prevalent machine learning and deep learning algorithms. The evaluation of credit risk holds great significance in the financial realm, impacting both companies and individuals. While credit risk prediction has been explored using statistical and machine learning techniques, their application to tax arrears prediction remains underexplored. We forecast individual local tax defaults in Republic of Korea using machine and deep learning algorithms, including convolutional neural networks (CNN), long short-term memory (LSTM), and sequence-to-sequence (seq2seq). Our model incorporates diverse credit and public information like loan history, delinquency records, credit card usage, and public taxation data, offering richer insights than prior studies. The results highlight the superior predictive accuracy of the CNN model. Anticipating local tax arrears more effectively could lead to efficient allocation of administrative resources. By leveraging advanced machine learning, this research offers a promising avenue for refining tax collection strategies and resource management.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.9
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• pp.3223-3231
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• 2010

For a long time, most of advanced countries have supported the innovative firms with various support methods such as tax credit, subsidy, human resource education, and so on. Tax credit for innovation is the most popular industrial policy in these countries including Korea. However, in Korea, the effect of tax credit policy has been rarely analyzed. On the other hand, a considerable number of studies discover that tax credit policy in other countries influences positively on invest of R&D expenditure. This paper shows that tax credit policy positively influences on firm's innovation performances in Korea. The evaluated innovative effect of tax credit policy in this paper is more persuasive because it introduces various innovation performance variables including patent application with Propensity score matching method(PSM).

• The Journal of the Korea Contents Association
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• v.17 no.8
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• pp.382-395
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• 2017

This study tries to analyze whether the Earned Income Tax Credit (EITC), which was modified in 2011, has the effect of work incentive. In this sense, by establishing the 8th Wave of Korea Welfare Panel Study (2013) and the 9th Wave (2014), Furthermore, in order to overcome the methodological limit, the results of two-party analysis method will be compared by firstly carrying out multiple regression analysis and then performing propensity score matching analysis. The 535 households out of 6,025 were selected. The following are the results of multiple digression analysis and propensity score matching analysis. First, there was no statistically meaningful relationship with regard to the perception of the EITC. Second, there was a statistically meaningful result in the reduction of working hours with regard to whether a household received labor incentive or not. The study found that the revised EITC is not providing incentives which stimulates the will to work.

• Korean Journal of Construction Engineering and Management
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• v.23 no.4
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• pp.36-46
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• 2022

It takes a relatively long time for construction companies that lack the ability to finance to adapt to construction policy in the construction industry. However, financial institutions rarely provide financial products to construction companies, particularly small and medium-sized construction companies, because their security capacity and credit rating are low. This study investigates the financial products needed for small and medium construction companies to adapt to policy changes. The demand of small and medium construction companies for financial products is analyzed by experts' advise and survey. And, when the investigated financial products for the construction industry are introduced, the legal systems in need of revision are analyzed. Based on the analyzed demand and the number of legal systems needing revision, the priority for the introduction of financial products to the construction industry is analyzed. Among the financial products investigated, the priority of "Expert consultation, such as accountant, tax accountant, lawyer, etc." is the highest. In future studies, the criteria and method of financial product development for high-priority financial products could be researched.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.67-84
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• 2010

In terms of business, forecasting is a work of what is expected to happen in the future to make managerial decisions and plans. Therefore, the accurate forecasting is very important for major managerial decision making and is the basis for making various strategies of business. But it is very difficult to make an unbiased and consistent estimate because of uncertainty and complexity in the future business environment. That is why we should use scientific forecasting model to support business decision making, and make an effort to minimize the model's forecasting error which is difference between observation and estimator. Nevertheless, minimizing the error is not an easy task. Case-based reasoning is a problem solving method that utilizes the past similar case to solve the current problem. To build the successful case-based reasoning models, retrieving the case not only the most similar case but also the most relevant case is very important. To retrieve the similar and relevant case from past cases, the measurement of similarities between cases is an important key factor. Especially, if the cases contain symbolic data, it is more difficult to measure the distances. The purpose of this study is to improve the forecasting accuracy of case-based reasoning approach using fuzzy relation and composition. Especially, two methods are adopted to measure the similarity between cases containing symbolic data. One is to deduct the similarity matrix following binary logic(the judgment of sameness between two symbolic data), the other is to deduct the similarity matrix following fuzzy relation and composition. This study is conducted in the following order; data gathering and preprocessing, model building and analysis, validation analysis, conclusion. First, in the progress of data gathering and preprocessing we collect data set including categorical dependent variables. Also, the data set gathered is cross-section data and independent variables of the data set include several qualitative variables expressed symbolic data. The research data consists of many financial ratios and the corresponding bond ratings of Korean companies. The ratings we employ in this study cover all bonds rated by one of the bond rating agencies in Korea. Our total sample includes 1,816 companies whose commercial papers have been rated in the period 1997~2000. Credit grades are defined as outputs and classified into 5 rating categories(A1, A2, A3, B, C) according to credit levels. Second, in the progress of model building and analysis we deduct the similarity matrix following binary logic and fuzzy composition to measure the similarity between cases containing symbolic data. In this process, the used types of fuzzy composition are max-min, max-product, max-average. And then, the analysis is carried out by case-based reasoning approach with the deducted similarity matrix. Third, in the progress of validation analysis we verify the validation of model through McNemar test based on hit ratio. Finally, we draw a conclusion from the study. As a result, the similarity measuring method using fuzzy relation and composition shows good forecasting performance compared to the similarity measuring method using binary logic for similarity measurement between two symbolic data. But the results of the analysis are not statistically significant in forecasting performance among the types of fuzzy composition. The contributions of this study are as follows. We propose another methodology that fuzzy relation and fuzzy composition could be applied for the similarity measurement between two symbolic data. That is the most important factor to build case-based reasoning model.