• Information Systems Review
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• v.9 no.1
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• pp.161-189
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• 2007

The purposes of this dissertation are to identify various factors affecting the outcomes of feasibility analysis and investment decision makings of new IT project plans and empirically analysis the relationships among them. 9 variables which have been drawn from prior studies and industry practices are the amount of the necessary resource such as development budget and time, the expect financial benefits, the degree of alignments between IT projects and the business strategy, the estimated risk, and the investment priority as the dependent variable. Data from 125 IT projects of K bank, the leading commercial bank in Korea, have been collected and Regression Analysis and ANOVA have been performed. As results, 5 out of 8 hypothesis have been accepted partially or totally.

• Journal of Digital Convergence
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• v.14 no.7
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• pp.11-20
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• 2016

This study examines the possibility of implementing the technology financing for export-import based small and medium sized enterprises. Our sample consists of 2,753 small and medium sized enterprises, receiving financial support from the Export-Import Bank of Korea for the period of 2011-2013. We find that only 400(200) firms reserve IPs(patents) annually. Given that IPs are likely to concentrate on manufacturer industries such as electronic components, computers, video, sound and communication equipment manufacturing(KSIC 26), other machinery and equipment manufacturing(KSIC 29), manufacture of motor vehicles and trailers(KSIC 31). We also find that the total assets, sales and R&D expenses of IP holding companies greatly exceeds those of companies without IPs. In addition, IP holding companies' liquidity seems slight edge and the leverage ratio is somewhat lower. However, profitability ratios of IP holding companies are rather than harsh or similar level. 20~30% of IP holding firms show very week credit scores, implying that banks' default risk is expected to be significant.

• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.37-41
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• 2021

In recent years, ESG investment is increasing worldwide, and awareness of ESG risks such as environment, society, and governance is increasing, and non-financial investments are considered when making investment decisions. With the recent Corvid 19 crisis, the focus is on the environment, and investments related to bio and health are gaining popularity, while new investments are completely suspended in coal-related businesses, and decisions are made in the direction of sequential termination or withdrawal of existing businesses This has resulted in an increasing number of managers setting climate change and sustainability as top priorities in their investment portfolios. As a result, it is necessary to present effective countermeasures to changes in the investment environment, and to make efforts to respond and prepare an investment system that can help build a risk management system. Therefore, I would like to briefly review the ESG investment trends and present implications considering shared growth and mutual benefit.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.4
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• pp.221-228
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• 2024

Predicting insurance claims is a key task for insurance companies to manage risks and maintain financial stability. Accurate insurance claim predictions enable insurers to set appropriate premiums, reduce unexpected losses, and improve the quality of customer service. This study aims to enhance the performance of insurance claim prediction models by applying ensemble learning techniques. The predictive performance of models such as Random Forest, Gradient Boosting Machine (GBM), XGBoost, Stacking, and the proposed Dynamic Weighted Ensemble (DWE) model were compared and analyzed. Model performance was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and the Coefficient of Determination (R²). Experimental results showed that the DWE model outperformed others in terms of evaluation metrics, achieving optimal predictive performance by combining the prediction results of Random Forest, XGBoost, LR, and LightGBM. This study demonstrates that ensemble learning techniques are effective in improving the accuracy of insurance claim predictions and suggests the potential utilization of AI-based predictive models in the insurance industry.

• Korean Journal of Construction Engineering and Management
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• v.13 no.4
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• pp.60-68
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• 2012

The past IMF foreign exchange crisis and subprime financial crisis had a big influence on variability of macroeconomics, even if the origin of its occurrence might be different. This not only had a significant infrequence on the overall industries, but also produced many insolvent companies by being closely linked with a management environment of an individual construction company leading the construction industry. Actually, the level of default risk of construction companies before and after fluctuation of macroeconomics gets to experience a rapid changing process, and a difference in reaction against shock exists according to each company. Accordingly, the purpose of this paper is to confirm the fluctuation process of the default risk of construction companies under the fluctuation of macroeconomics such as the IMF financial crisis and the subprime mortgage crisis. As an analysis result, it is judged that the subprime financial crisis gave bigger shock to construction companies than the foreign exchange crisis, and it is expected that this would have a relation with the construction market before shock of macroeconomics. In addition, it was analyzed that when comparing insolvent companies with normal companies, the recovery speed of normal companies is faster. It is judged that this was affected by a difference of internal business capacity between insolvent companies and normal companies.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.104-104
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• 2011

2010년 기준 세계 물산업은 약 4,800억불 규모이며, 2025년에는 약 1조 달러 규모로 성장할 것으로 전망된다. 또한 기후변화 등의 요인으로 물산업의 범위는 물순환체계 전과정을 포괄하는 "유역종합개발+상하수도+대체수자원"으로 확장될 것으로 예측된다. 그러나 지금까지의 국내 물산업 육성은 주로 상하수도 분야 중심으로 국한되어, 기후변화에 대응한 유역종합개발 분야에 대한 시장 진출기회를 상실하고 있다. 상하수도 중심의 물산업은 이미 선진 메이저 기업들이 선점하여 치열한 경쟁이 벌어지고 있는 '레드오션'이라 할 수 있으므로, 새로운 물산업의 강국으로 부상하기 위해서는 우리가 가진 장점을 바탕으로 물산업의 새로운 영역을 개척하는 방안에 대한 연구가 필요한 시점이다. 본 연구는 먼저 국내외 물순환체계 전과정(유역종합개발+상하수도+대체수자원)에 대한 시장 조사을 통해 세계 물산업 시장을 프로젝트 유형별, 지역별로 분석하고, 이를 토대로 국내 물산업 육성과 해외진출을 위한 당면 과제를 다음과 같이 제시하였다. 첫째, 민관협력을 위한 제도적 틀을 형성할 필요가 있다. 정부의 역할이 매우 중요한데, 정부 또는 기금이 자금의 단순한 대부자에서 적극적인 투자자로 전환함으로서 국내 민간기업들의 해외시장 진입장벽을 낮추어 줄 필요가 있다. 정부 주도의 민관협력이 활성화되면 참여 기업의 재무적 리스크를 현저히 줄일 수 있다. 또한, 상하수도 운영 경험을 축적한 공기업이 해외진출 지원기능을 수행하도록 하여야 한다. 즉, 공기업이 민간 기업의 경쟁자가 아니라 지원자가 될 수 있도록 프레임을 바꿔주어야 한다. 둘째, 물산업 클러스터의 형성이다. 물산업 제조업은 대부분 중소 벤처기업으로 독자적인 해외진출이 곤란하므로, 물전문 공기업이 중소 벤처기업 육성 및 해외진출의 앵커 역할을 담당하는 것이 필요하다. 이스라엘이나 싱가포르의 물산업 클러스터처럼 Anchor 역할을 행하는 공기업과 민간기업이 장기적 협력관계를 구축할 수 있는 기반을 마련해야한다. 셋째, 신시장 역량의 창출이다. 기후변화로 크게 성장할 전망인 통합물관리 시장에 대한 전략적 접근이 요구된다. 우선 ODA 등 대외 원조자금을 활용하여 투자비가 적게 들고 정보를 선점할 수 있는 조사 설계부터 시작하여, 댐 및 수력개발, 상하수도 건설 운영 등에 단계적으로 접근할 수 있을 것이다. 또한, 향후 도입될 예정인 물인프라의 Smart 기술, 첨단 수처리 기술 등을 활용하여 새로운 시장을 개척해야 한다. 4대강살리기 사업, 해수담수화 등 조기에 경쟁우위를 갖출 수 있는 사업과 기술을 Flagship Project로 브랜드화하여 우리나라를 "물강국"으로 포지셔닝할 경우 세계 물시장 공략에 보다 효과적일 것으로 판단된다.

• Korean Journal of Construction Engineering and Management
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• v.19 no.4
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• pp.21-33
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• 2018

In 2018, the real estate markets have hardly been transacted according to the government's tight regulations of real estates, and have the high possibility to reach a low hit due to the hike of loan interest rates following the U. S rise of base money rate. The key profits for the large construction companies mainly come from the overseas plant projects and the domestic non-governmental construction projects. They suffered a lot such as the lowering of their credit ratings due to the large losses caused by the frquent design changes and work delay. Even in the domestic non-governmental construction projects, the general business risks are on the rise due to the property marketing moving over to the decreasing phase. The small and medium sized security companies has realized a lot of operaring profits as they participated in the PF market to make up for the losses in the securities trading business. But, now as the housing market is not so good around the nation except Seoul and the financial states of large construction companies are not good enough, they can face the liquidity crisis if there happens the problems in the PF backed securities which they have handled. As Korean economy experienced the crisis in the savings banks before, it is recommended that Financial Supervisory Service proposes the preemptive control method and supervision direction to overcome the crisis.

• The Journal of the Convergence on Culture Technology
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• v.8 no.2
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• pp.47-52
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• 2022

The purpose of this study is to identify the risk level of Chinese commercial banks' loan assets and to analyze what factors affect the stability of Chinese commercial banks. In addition, Chinese commercial banks are classified based on the asset size of 200 billion yuan, and the difference in stability according to size is investigated. The analysis results are as follows. First, it was estimated that as the proportion of household and corporate loans of commercial banks in China increased, the stability of banks decreased. Although the Chinese financial authorities are currently restricting the conservative management of loan assets, it will be necessary to preemptively manage risk on loan assets by setting an appropriate standard for loan-to-deposit ratio in the future. Second, as a result of analyzing the stability of large banks based on 200 billion yuan of bank assets, it was estimated that the stability of large banks was lower. As large banks are likely to conduct aggressive loan asset management, continuous management of non-performing assets is required in the future. This study will serve as a measure for improving the stability of commercial banks in China by estimating the effect of loan asset management of Chinese commercial banks on financial stability. In particular, by examining the stability of large banks, a strategy for sustainable development of the financial industry is required by diagnosing the weaknesses of large banks.

• Information Systems Review
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• v.24 no.1
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• pp.105-123
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• 2022

Thanks to the remarkable success of Artificial Intelligence (A.I.) techniques, a new possibility for its application on the real-world problem has begun. One of the prominent applications is the bankruptcy prediction model as it is often used as a basic knowledge base for credit scoring models in the financial industry. As a result, there has been extensive research on how to improve the prediction accuracy of the model. However, despite its impressive performance, it is difficult to implement machine learning (ML)-based models due to its intrinsic trait of obscurity, especially when the field requires or values an explanation about the result obtained by the model. The financial domain is one of the areas where explanation matters to stakeholders such as domain experts and customers. In this paper, we propose a novel approach to incorporate financial domain knowledge into local rule generation to provide explanations for the bankruptcy prediction model at instance level. The result shows the proposed method successfully selects and classifies the extracted rules based on the feasibility and information they convey to the users.

• Information Systems Review
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• v.25 no.1
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• pp.111-128
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• 2023

With the development of artificial intelligence technology, interest in data-based product preference estimation and personalized recommender systems is increasing. However, if the recommendation is not suitable, there is a risk that it may reduce the purchase intention of the customer and even extend to a huge financial loss due to the characteristics of the financial product. Therefore, developing a recommender system that comprehensively reflects customer characteristics and product preferences is very important for business performance creation and response to compliance issues. In the case of financial products, product preference is clearly divided according to individual investment propensity and risk aversion, so it is necessary to provide customized recommendation service by utilizing accumulated customer data. In addition to using these customer behavioral characteristics and transaction history data, we intend to solve the cold-start problem of the recommender system, including customer demographic information, asset information, and stock holding information. Therefore, this study found that the model proposed deep learning-based collaborative filtering by deriving customer latent preferences through characteristic information such as customer investment propensity, transaction history, and financial product information based on customer transaction log records was the best. Based on the customer's financial investment mechanism, this study is meaningful in developing a service that recommends a high-priority group by establishing a recommendation model that derives expected preferences for untraded financial products through financial product transaction data.