• Journal of Information Technology Applications and Management
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• v.17 no.1
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• pp.11-33
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• 2010

The majority of graphs and visuals made publicly available by Thai listed companies tend to be disjointed and minimal. Only a little over fifty percent of the total 478 companies included graphic representations of their business operations and performance in the form of two or three dimensional spreadsheet based graphs in their annual reports, investor relations documents, websites and so on. For novice users, these visual representations are unlikely to give the big picture of what is the company's financial position and performance. Neither will they tell where the company stands in its own operating environment. The existing graphics and visuals, in very rare cases, can provide a sense of the company's future outlook. For boundary users such as audit committees whose duty is to promote good governance through transparency and disclosure, preliminary interview results show that there is some doubt as to whether the inclusion of big-picture visuals can really be of use to minority shareholders. These boundary users expect to see more insightful visuals beyond those produced by traditional spreadsheets which will enable them to learn to cope with the on-going turbulence in today's business environment more quickly. However, the debate is still going on as to where to draw the line between internal or external reporting visuals.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.23 no.3
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• pp.237-251
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• 2019

We extend the previous work [J. Korean Soc. Ind. Appl. Math. 21(3) 181] to two-and three-asset equity-linked securities (ELS). In the real finance market, two-or three-asset ELS is more popular than one-asset ELS. Therefore, we need to develop mobile platform for pricing the two-and three-asset ELS. The mobile implementation of the ELS pricing will be very useful in practice.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.99-122
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• 2019

According to the recent IDC (International Data Corporation) report, as from 2025, the total volume of data is estimated to reach ten times higher than that of 2016, corresponding to 163 zettabytes. then the main body of generating information is moving more toward corporations than consumers. So-called "the wave of Big-data" is arriving, and the following aftermath affects entire industries and firms, respectively and collectively. Therefore, effective management of vast amounts of data is more important than ever in terms of the firm. However, there have been no previous studies that measure the effects of big data investment, even though there are number of previous studies that quantitatively the effects of IT investment. Therefore, we quantitatively analyze the Big-data investment effects, which assists firm's investment decision making. This study applied the Event Study Methodology, which is based on the efficient market hypothesis as the theoretical basis, to measure the effect of the big data investment of firms on the response of market investors. In addition, five sub-variables were set to analyze this effect in more depth: the contents are firm size classification, industry classification (finance and ICT), investment completion classification, and vendor existence classification. To measure the impact of Big data investment announcements, Data from 91 announcements from 2010 to 2017 were used as data, and the effect of investment was more empirically observed by observing changes in corporate value immediately after the disclosure. This study collected data on Big Data Investment related to Naver 's' News' category, the largest portal site in Korea. In addition, when selecting the target companies, we extracted the disclosures of listed companies in the KOSPI and KOSDAQ market. During the collection process, the search keywords were searched through the keywords 'Big data construction', 'Big data introduction', 'Big data investment', 'Big data order', and 'Big data development'. The results of the empirically proved analysis are as follows. First, we found that the market value of 91 publicly listed firms, who announced Big-data investment, increased by 0.92%. In particular, we can see that the market value of finance firms, non-ICT firms, small-cap firms are significantly increased. This result can be interpreted as the market investors perceive positively the big data investment of the enterprise, allowing market investors to better understand the company's big data investment. Second, statistical demonstration that the market value of financial firms and non - ICT firms increases after Big data investment announcement is proved statistically. Third, this study measured the effect of big data investment by dividing by company size and classified it into the top 30% and the bottom 30% of company size standard (market capitalization) without measuring the median value. To maximize the difference. The analysis showed that the investment effect of small sample companies was greater, and the difference between the two groups was also clear. Fourth, one of the most significant features of this study is that the Big Data Investment announcements are classified and structured according to vendor status. We have shown that the investment effect of a group with vendor involvement (with or without a vendor) is very large, indicating that market investors are very positive about the involvement of big data specialist vendors. Lastly but not least, it is also interesting that market investors are evaluating investment more positively at the time of the Big data Investment announcement, which is scheduled to be built rather than completed. Applying this to the industry, it would be effective for a company to make a disclosure when it decided to invest in big data in terms of increasing the market value. Our study has an academic implication, as prior research looked for the impact of Big-data investment has been nonexistent. This study also has a practical implication in that it can be a practical reference material for business decision makers considering big data investment.

• Industry Promotion Research
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• v.8 no.2
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• pp.21-27
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• 2023

This study studies the policies and use cases of the government and the financial sector for artificial intelligence, and the future policy tasks of the financial sector. want to derive According to Gartner, noteworthy technologies leading the financial industry in 2022 include 'generative AI', 'autonomous system', 'Privacy Enhanced Computation (PEC) was selected. The financial sector is developing new technologies such as artificial intelligence, big data, and blockchain. Developments are spurring innovation in the financial sector. Data loss due to the spread of telecommuting after the corona pandemic As interests in sharing and personal information protection increase, companies are expected to change in new digital technologies. Global financial companies also utilize new digital technology to develop products or manage and operate existing businesses. I n order to promote process innovation, I T expenses are being expanded. The financial sector utilizes new digital technology to prevent money laundering, improve work efficiency, and strengthen personal information protection. are applying In the era of Big Blur, where the boundaries between industries are disappearing, the competitive edge in the challenge of new entrants In order to preoccupy the market, financial institutions must actively utilize new technologies in their work.

• Knowledge Management Research
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• v.19 no.1
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• pp.97-118
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• 2018

The finance-investment industry is currently focusing on research related to artificial intelligence and big data, moving beyond conventional theories of financial engineering. However, the case of equity optimization portfolio by using an artificial intelligence, big data, and its performance is rarely realized in practice. Thus, the purpose of this study is to propose process improvements in equity selection, information analysis, and portfolio composition, and lastly an improvement in portfolio returns, with the case of an equity optimization model based on quantitative research by an artificial intelligence. This paper is an empirical study of the portfolio based on an artificial intelligence technology of "D" asset management, which is the largest domestic active-quant-fiduciary management in accordance with the purpose of this paper. This study will apply artificial intelligence to finance, analyzing financial and demand-supply information and automating factor-selection and weight of equity through machine learning based on the artificial neural network. Also, the learning the process for the composition of portfolio optimization and its performance by applying genetic algorithms to models will be documented. This study posits a model that the asset management industry can achieve, with continuous and stable excess performance, low costs and high efficiency in the process of investment.

• Journal of Digital Convergence
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• v.15 no.6
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• pp.133-143
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• 2017

Data is the most important asset in the financial sector. On average, 71 percent of financial institutions generate competitive advantage over data analysis. In particular, in the card industry, the card transaction data is widely used in the development of merchant information, economic fluctuations, and information services by analyzing patterns of consumer behavior and preference trends of all customers. However, creation of new value through fusion of data is insufficient. This study introduces the analysis and forecasting of consumption trends of credit card companies which convergently analyzed the social data and the sales data of the company's own. BC Card developed an algorithm for linking card and social data with trend profiling, and developed a visualization system for analysis contents. In order to verify the performance, BC card analyzed the trends related to 'Six Pocket' and conducted th pilot marketing campaign. As a result, they increased marketing multiplier by 40~100%. This study has implications for creating a methodology and case for analyzing the convergence of structured and unstructured data analysis that have been done separately in the past. This will provide useful implications for future trends not only in card industry but also in other industries.

• Journal of Practical Engineering Education
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• v.12 no.1
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• pp.31-39
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• 2020

Big data analysis is analyzed and used in a variety of management and industrial sites, and plays an important role in management decision making. The job competency of big data analysis personnel engaged in management analysis work does not necessarily require the acquisition of microscopic IT skills, but requires a variety of experiences and humanities knowledge and analytical skills as a Data Scientist. However, big data education by state-run and state-run educational institutions and job education institutions based on the National Competency Standards (NCS) is proceeding in terms of software engineering, and this teaching methodology can have difficult and inefficient consequences for non-technical majors. Therefore, we analyzed the current Big Data platform and its related technologies and defined which of them are the requisite job competency requirements for field personnel. Based on this, the education courses for big data analysis and visualization techniques were organized for non-technical-based majors. This specialized curriculum was conducted by working-level officials of financial institutions engaged in management analysis at the management site and was able to achieve better educational effects The education methods presented in this study will effectively carry out big data tasks across industries and encourage visualization of big data analysis for non-technical professionals.

• Journal of the Korea Convergence Society
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• v.6 no.1
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• pp.85-91
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• 2015

Recently government has distributed precautionary measure and response procedures for smishing(SMS phishing), pharming, phishing, memory hacking and intensified Electronic Financial Transaction Act because of the sharp increase of electronic bank frauds. However, the methods of electronic bank frauds also developed and changed accordingly so much it becomes hard to cope with them. In contrast to earlier voice phishing targeted randomizing object, these new methods find out the personal information of targets and analyze them in detail making a big data base. And they are progressed into new kind of electronic bank frauds using those analyzed informations for voice phishing. This study analyze the attack method of voice phishing blended with the Big Data of personal informations and suggests response procedures for electronic bank frauds increasingly developed. Using the method to save meaningless data in a memory, attackers cannot deduct accurate information and try voice phishing properly even though they obtain personal information based on the Big Data. This study analyze newly developed social technologic attacks and suggests response procedures for them.

• Journal of Information Technology Applications and Management
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• v.23 no.3
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• pp.1-12
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• 2016

Visual representations for concepts in business management are quite challenging, especially those abstract concepts in Accountancy discipline. For example, there might not be a consensus on what to use to represent such abstraction as an asset, liability, or owner equity. This is because asset can be property, estate, resources, equipment, or any tangible or non-tangible valuables. Cognitive science concepts and behavior engagement have been used to develop visual representations for financial data. The concepts include spatial processing, big picture thinking, and metaphor. Review of past studies together with a brief research plan to test the usability for learning of four new augmented reality 0visuals are provided in the present paper.

• The Journal of Bigdata
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• v.7 no.2
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• pp.173-193
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• 2022

With the revision of the Data 3 Act, the financial MyData industry was created newly. MyData services collect the financial customers' data scattered in various financial companies and provide personalized services such as personal financial product recommendation, personal expenditure advice, etc. Although MyData service started in 2022, but the use of the service has not been significantly activated. This study attempted to analyze the factors affecting the use of MyData services from the perspective of financial consumers through VAM, UTAUT2 model. The factors related to the perceived value and intention to use MyData services of financial consumers were verified using benefit and sacrifice variables. Personal Innovativeness was used as a moderating variable. As a result of this study, it was found that personal product recommendation service has an important influence on the use of MyData services, and personal innovativeness has an effect as a modulating variable. It can be said that it is meaningful as a preceding study in terms of timing because it studied the perceived value of consumers less than a year after the MyData service began. From the practical perspectives, it was possible to show the change direction and marketing points of the MyData service. In practice, it was possible to confirm the direction of the service and the marketing point.