数据加载中.....
|
请使用永久网址来引用或连结此文件:
https://nccur.lib.nccu.edu.tw/handle/140.119/131038
|
| 题名: | 提升意外保險部門之營運效率研究
IMPROVING OPERATIONAL EFFICIENCY OF A CASUALTY INSURANCE DEPARTMENT |
| 作者: | 卓尚緯
Cho, Stewart |
| 贡献者: | 蔡政憲
Tsai, Jason
卓尚緯
Cho, Stewart |
| 关键词: | 運作效率
意外險
人工智慧
核心系統
員工訓練
Operational efficiency
Casualty insurance
Artificial Intelligence
Core system
Employee training |
| 日期: | 2020 |
| 上传时间: | 2020-08-03 17:45:49 (UTC+8) |
| 摘要: | While improving the operational efficiency of a casualty insurance department in the property insurance industry has been extensively investigated, increasing the performance and efficiency of an underwriting department in a life insurance company is relatively unexplored. The paper studies the improvement of efficiency of the casualty insurance department. The research focuses on how a non-life insurance company should change in order to improve the efficiency of its casualty insurance department from five different factors, which are artificial intelligence (AI) and robots, core systems, internal business processes and delegation of authority policy, employee training, and distribution channels and marketing strategies. Interview information from the participants is categorized in order to ascertain the respondent’s opinions are presented correctly. The findings suggest that property insurance companies could consider using AI or robots, introducing a new core system, giving a higher limit of authorities or delegation of authority for its branches to underwrite more insurance products, and providing training that employees needed to handle daily business routines. These findings have implications for non-life insurance companies to have plans or intend to evaluate what things they should do to increase performance and competitiveness from different perspectives. |
| 參考文獻: | Accenture. (2005). Driving high performance in government: Maximizing the value of public-sector shared services. Retrieved from https://www.njleg.state.nj.us/PropertyTaxSession/OPI/accenture.pdf
Aon Public Limited Company. (2019). Global risk management survey. Survey Report. Retrieved from https://www.aon.com/getmedia/8d5ad510-1ae5-4d2b-a3d0-e241181da882/2019-Aon-Global-Risk-Management-Survey-Report.aspx
Financial Stability Board. (2017, 11 1). Artificial intelligence and machine learning in financial services - Market developments and financial stability implications. Retrieved from https://www.fsb.org/wp-content/uploads/P011117.pdf
High-Level Expert Group on Artificial Intelligence. (2019, 4 8). Ethics guidelines for trustworthy AI. Brussels: European Commission. Retrieved from https://ec.europa.eu/newsroom/dae/document.cfm?doc_id=60419
Institute for Robotic Process Automation and Artificial Intelligence. (2019). Definition and benefits. Retrieved from Institute for Robotic Process Automation and Artificial Intelligence: https://irpaai.com/definition-and-benefits/
Krish, K., Lansing, J., Münstermann, B., Olesen, P. B., & Vogelgesang, U. (2019, 11 4). IT modernization in insurance: Three paths to transformation. Retrieved from McKinsey & Company: https://www.mckinsey.com/industries/financial-services/our-insights/it-modernization-in-insurance-three-paths-to-transformation#
Lacity, M., & Willcocks, L. (2016). Robotic process automation: The next transformation lever for shared services. The Outsourcing Unit (OU). Retrieved from http://www.umsl.edu/~lacitym/OUWP1601.pdf
On-the-job training. (2020, 05 14). Retrieved from In Wikipedia: https://en.wikipedia.org/wiki/On-the-job_training
Organisation for Economic Co-operation and Development. (2017). Technology and innovation in the insurance sector. Organisation for Economic Co-operation and Development. Retrieved from https://www.oecd.org/pensions/Technology-and-innovation-in-the-insurance-sector.pdf
Organisation for Economic Co-operation and Development. (2020). The impact of big data and artificial intelligence (AI) in the insurance sector. Organisation for Economic Co-operation and Development. Retrieved from www.oecd.org/finance/Impact-Big-Data-AI-in-the-Insurance-Sector.htm
Osinubi, F. (2018). Looking into the future: Leveraging the power of AI and robotics. PricewaterhouseCoopers (PwC). Retrieved from https://www.pwc.com/ng/en/assets/pdf/leveraging-power-ai-and-robotics.pdf
李靜宜（2020年01月03日）。IFRS 17即將上路，臺灣本土廠商推微服務架構的保險資訊系統轉換解決方案。iThome。取自https://www.ithome.com.tw/news/135167
金融監督管理委員會（2019年03月28日）。上市(櫃)公司近期應遵循之新規範及相關措施。金融監督管理委員會。取自 https://www.fsc.gov.tw/ch/home.jsp?id=96&parentpath=0,2&mcustomize=news_view.jsp&dataserno=201903280001&toolsflag=Y&dtable=News
金融監督管理委員會保險局（2020年03月30日）。109年1月保險市場重要指標。取自https://www.ib.gov.tw/uploaddowndoc?file=statistics/202003301316550.pdf&filedisplay=202001%E6%8C%87%E6%A8%990305_%E5%90%AB%E5%85%AC%E5%BC%8F%E5%8A%A0%E9%A0%81_%E6%AD%A3%E5%BC%8F_final%E5%80%BC.pdf&flag=doc
洪榮昭（1991）。人力資源發展：企業培養人才之道。臺北市：師大書苑。
財團法人保險事業發展中心（2020年05月17日）。保險財務業務統計。取自 https://www.tii.org.tw/tii/information/information1/000001.html
財團法人資訊工業策進會（2020）。108年度「人工智慧(AI)技術導入對金融服務業之勞動法制影響調查」委託研究。勞動部委託研究（編號：108-MOL-010303）。勞動部。
楊芳苓、曾耀民（2020年3月18日）。農業保險法草案。立法院（編號：B01498），未出版。取自https://www.ly.gov.tw/Pages/Detail.aspx?nodeid=5249&pid=192711
楊純瑜（2012）。激勵因素、學習型組織、教育訓練、個人生涯規劃與工作績效關聯性之研究-以A保險公司員工為例。國立高雄應用科技大學企業管理系碩士在職專班碩士論文，(頁 49)。高雄市。取自https://hdl.handle.net/11296/nb68cc
楊絡懸（2020年05月08日）。「壽險大魔王」IFRS 17延至2023年！台灣預計2026年1月上路 保險業還有6年時間準備。ETtoday新聞雲。取自https://www.ettoday.net/news/20200318/1670927.htm
資誠聯合會計師事務所（2020）。兆豐產物保險股份有限公司財務報告暨會計師查核報告民國108 年度及107 年度。臺北市。取自 https://ins-info.ib.gov.tw/customer/Info2-8.aspx?UID=03090217
廖述源（2007）。財產保險經營。臺北市：財團法人保險事業發展中心。 |
| 描述: | 碩士
國立政治大學
國際經營管理英語碩士學位學程(IMBA)
106933028 |
| 資料來源: | http://thesis.lib.nccu.edu.tw/record/#G0106933028 |
| 数据类型: | thesis |
| DOI: | 10.6814/NCCU202000750 |
| 显示于类别: | [國際經營管理英語碩士學程IMBA] 學位論文
|
文件中的档案:
| 档案 |
描述 |
大小 | 格式 | 浏览次数 |
|---|
| 302801.pdf | | 1133Kb | Adobe PDF2 | 85 | 检视/开启 |
|
在政大典藏中所有的数据项都受到原著作权保护.
|
