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신기술 발전에 따른 산업 지형의 변화 전망과 대응 전략
장병열(Jang Pyoung Yol),설라영 과학기술정책연구원 2015 정책연구 Vol.- No.-
Recently Fintech is the core innovation driver in the finance industry. Even though the Korean government announced several policies to support Fintech innovation, it is not sufficient to enhance the Fintech innovation in Korea. The purpose of this research to analyze and investigate the Fintech in terms of technological perspective, industrial perspective, and societal and framework conditions perspective. For this purpose, this research consists of 5 chapters. The Chapter 1, Introduction describes the research background and purpose, research methodology, and research structure. In Chapter 2, the concept and development prospect of Fintech are proposed. And the background and concept of Fintech, related works, technology characteristics and development prospect of Fintech, change of finance service and model due to Fintech, and the R&D Investment in Finance Industry are described in Chapter 2. In Chapter 3, the prospect of finance industry structure change due to Fintech are analyzed. In particular, change of finance service innovation paradigm, Fintech related domestic policies, internet bank and finance industry structure change, and finance industry restructuring and ecosystem change are investigated. The Chapter 4, prospect of society, market and framework conditions change due to Fintech are described. The societal impacts, prospect of market demands, prospect of framework conditions change are covered in this chapter. Based on the previous chapter, the Chapter 5 has concluding remarks and proposes the Fintech innovation and strategies for finance industry. This chapter proposed the direction and 19 policies tasks in terms of fintech based finance in dustry innovation policies, Fintech based finance industry promotion policies, and framework condition polices to support Fintech innovation.
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신기술 발전에 따른 산업 지형의 변화 전망과 대응 전략
이성호(Sung-Ho Lee),설라영(Ra-Yeong Seol),김은희(Eun-Hee Kim) 과학기술정책연구원 2015 정책연구 Vol.- No.-
Cognitive computing is self-learning systems that can obtain not only explicit knowledge but also tacit knowledge through recognizing patterns from big data, and leads to autonomous systems that can handle complex and ambiguous problems without human intervention. Although the past artificial intelligence attempts relied mainly on deductive inference, the current cognitive computing research mainly adopts inductive reasoning(including statistical methods and machine learning), taking advantage of big data collected from the Internet and plenty of various sensors. Cognitive computing is expected to replace nearly half of the current labors within a decade or two, and hence can innovate business models through offering non face-to-face transactions, personally customized services, and on-demand shared services. Global leaders such as Google, Microsoft, Facebook, and IBM expand their influences by transforming cognitive computing capabilities into new platform services intertwined with cloud computing and big data infrastructure. This will disrupt the boundaries between manufacturing and service industries, and those between labor-intensive and capital-intensive industries of the existing industrial structure. Autonomous driving technology is a good example that illustrates how cognitive computing can disrupt the existing industrial structure. If autonomous driving is offered as unmanned self-driving taxi services (on-demand shared services), instead of an optional function of one’s own car, it will replace a large number of new car purchase as well as manned taxi services. In the conclusion chapter, the implications from the foresight exercises are summarized, and a number proactive government strategies are suggested to effectively respond to opportunities and threats that cognitive computing may create in the near future.
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