금융산업의 빅데이터 경영 사례에 관한 연구: 은행의 빅데이터 활용 조직 및 프로세스를 중심으로
A Study on Big Data-Driven Business in the Financial Industry: Focus on the Organization and Process of Using Big Data in Banking Industry
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Purpose - The purpose of this study was to analyze cases of big data-driven business in the financial industry, focusing on organizational structure and business processes using big data in banking industry. Design/methodology/approach - This study used a case study approach. To this end, cases of two banks implementing big data-driven business were collected and analyzed. Findings - There are two things in common between the two cases. One is that the central tasks for big data-driven business are performed by a centralized organization. The other is that the role distribution and work collaboration between the headquarters and business departments are well established. On the other hand, there are two differences between the two banks. One marketing campaign is led by the headquarters and the other marketing campaign is led by the business departments. The two banks differ in how they carry out marketing campaigns and how they carry out big data-related tasks. Research implications or Originality - When banks plan and implement big data-driven business, the common aspects of the two banks analyzed through this case study can be fully referenced when creating an organization and process. In addition, it will be necessary to create an organizational structure and work process that best fit the special situation considering the company's environment or capabilities
Abstract
Purpose - The purpose of this study was to analyze cases of big data-driven business in the financial industry, focusing on organizational structure and business processes using big data in banking industry. Design/methodology/approach - This study used a case study approach. To this end, cases of two banks implementing big data-driven business were collected and analyzed. Findings - There are two things in common between the two cases. One is that the central tasks for big data-driven business are performed by a centralized organization. The other is that the role distribution and work collaboration between the headquarters and business departments are well established. On the other hand, there are two differences between the two banks. One marketing campaign is led by the headquarters and the other marketing campaign is led by the business departments. The two banks differ in how they carry out marketing campaigns and how they carry out big data-related tasks. Research implications or Originality - When banks plan and implement big data-driven business, the common aspects of the two banks analyzed through this case study can be fully referenced when creating an organization and process. In addition, it will be necessary to create an organizational structure and work process that best fit the special situation considering the company's environment or capabilities
참고문헌 (25)
- "초개인화 시대의 새로운 투자 패러다임, 다이렉트 인덱싱" (2023)[URL]
- "금융권 대세된 초개인화뱅킹...“기술력 제고 통해 서비스 차별화해야”" (2022)[URL]
- "초개인화 서비스" (2018)[URL]
- 서창적, "빅데이터 경영과 고객세분화를 통한 맞춤형 서비스 개발 : 신한카드 Sally 의 성공요인", 서비스경영학회지, 16(4), pp.249~271 (2015)
- 신수행, "기술, 조직, 환경 관점에서 기업의 경영품질 향상을 위한 빅데이터 활용의 핵심요인에 관한 연구", 한국 IT 서비스학회지, 18(1), pp.91~112 (2019)
- "글로벌 은행들의 초개인화 뱅킹 사례와 시사점" (2022)[URL]
- "AI 기반 초개인화 서비스의 현재와 미래" (2022)[URL]
- "“고객님, 석달간 안 쓴 넷플 해지할게요” 누군가 실시간 추천해준다면" (2022)[URL]
- "2024년, 은행이 놓치지 말아야 할 3가지" (2023)[URL]
- Akyüz, A., "Financial Strategies in Competitive Markets: Multidimensional Approaches to Financial Policies for Local Companies", pp.327~340 (2021)
- Davenport, T. H., "How 'big data' is different", MIT Sloan Management Review, 54(1), pp.21~24 (2012)
- Fares, O. H., "Utilization of artificial intelligence in the banking sector : A systematic literature review", Journal of Financial Services Marketing, 28(4), pp.835~852 (2023)
- Gupta, M., "Analysis of Financial Performance Pre and Post Use of Artificial Intelligence Applications Via CAMELS Lens : With Special Reference to HDFC Bank", International Journal of Intelligent Systems and Applications in Engineering, 12(5), pp.324~337 (2024)
- Imhoff, C., "Building the customer-centric enterprise: data warehousing techniques for supporting customer relationship management" (2001), Wiley
- Jagtiani, J., "The roles of alternative data and machine learning in fntech lending : Evidence from the Lending Club consumer platform", Financial Management, 48(4), pp.1009~1029 (2019)
- Mall, S., "An empirical study on credit risk management : The case of nonbanking financial companies", Journal of Credit Risk, 14(3), pp.49~66 (2018)
- Marinakos, G., "Imbalanced customer classification for bank direct marketing", Journal of Marketing Analytics, 5(1), pp.14~30 (2017)
- McAfee, A., "Big Data : The management revolution", Harvard Business Review, 90(10), pp.61~67 (2012)
- Mendia, V. J. M., "Toward customer hyper-personalization experience-A data-driven approach", Cogent Business and Management, 9(1), pp.1~16 (2022)
- Olson, D. L., "Comparative analysis of data mining methods for bankruptcy prediction", Decision Support Systems, 52(2), pp.464~473 (2012)
- Patnaik, P., "Management strategies for sustainability, new knowledge innovation, and personalized products and services", pp.35~67 (2022), IGI Global
- Pukas, A., "Hyper-Personalization as a Customer Relationship Management Tool in a SMART Organization", Problemy Zarzadzania, 20(3), pp.95~108 (2022)
- "Hyper-personalization for enhancing customer loyalty and satisfaction in Customer Relationship Management (CRM) systems" (2023)[URL]
- Schwartz, E. M., "Customer acquisition via display advertising using multi-armed bandit experiments", Marketing Science, 36(4), pp.500~522 (2017)
- Trivedi, J., "Examining the customer experience of using banking Chatbots and its impact on brand love : The moderating role of perceived risk", Journal of Internet Commerce, 18(1), pp.91~111 (2019)