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題名 應用人工智慧及數位化工具於銀行授信作業之研究-以C銀行為例
作者 張佩瑜
Chang, Pei-Yu
貢獻者 蔡瑞煌<br>林靖庭
張佩瑜
Chang, Pei-Yu
關鍵詞 銀行授信
人工智慧
Credit
Artificial intelligence
日期 2024
上傳時間 5-Aug-2024 14:18:46 (UTC+8)
摘要 台灣金融研訓院(2022)報告指出,近90%的臺灣銀行業者已導入人工智慧(AI)、大數據及機器人流程自動化(RPA)技術,這些技術在未來將對銀行業務產生深遠影響,並且可能會改變其經營模式。授信是銀行的核心業務,其作業效率會直接影響銀行的營運績效,本研究以C銀行企業授信作業流程作為研究對象,探討將人工智慧及數位化工具導入C銀行企業授信作業中,並提出具體的應用方案,再與專家進行深度訪談評估應用方案在作業流程中運用的可行性和實用性。 本研究發現,人工智慧及數位化工具在銀行授信作業中的應用極具潛力,可提升作業效率和服務品質,但與客戶互動關係仍是銀行不可替代的核心競爭力。銀行在導入相關應用時,可以優先導入業務自動化的相關應用,能顯著提升作業效率和減少人力成本;讓工具作為輔助,保持人機協作平衡,同時提高效率及維持服務品質;管理階層應務實地評估實際需求,以避免不必要的投資及資源浪費;提供充分的教育訓練,可以讓應用方案順利推行及提升應用效果。
According to a report by the Taiwan Academy of Banking and Finance(2022), nearly 90% of Taiwan's banking industry has adopted artificial intelligence, big data, and robotic process automation technologies. These technologies are expected to have a profound impact on banking operations in the future and may change their business models. Loans and credit businesses are core operations of banks. This thesis focuses on the corporate credit operation process of Bank C, researching the application of AI and digital tools in the bank's corporate credit operations, proposing specific plans and interviewing experts to evaluate the feasibility and practicality of their use in the process. According to the results of this thesis, the application of AI and digital tools in bank credit operations has great potential to improve operational efficiency and service quality. However, customer interaction management remains an irreplaceable core competitive advantage for banks. When introducing related applications, banks can prioritize the implementation of business process automation tools, which can significantly enhance efficiency and reduce costs. Human-machine collaboration enhances efficiency while maintaining service quality. Conducting needs assessment can help avoid unnecessary investments and waste. Providing adequate training can ensure the smooth implementation of application plans and enhance their effectiveness.
參考文獻 中華民國銀行公會(2023)。中華民國銀行公會會員授信準則。 中華民國銀行公會(2023)。中華民國銀行公會會員徵信準則。 中華民國銀行公會(2024)。金融機構運用人工智慧技術作業規範。 中國信託銀行(2020年7月21日)。以客為本數位轉型新思維—無所不在、無時不在,中國信託有溫度的數位服務。https://www.ctbcbank.com/twrbo/zh_tw/index/ctbc_article/digital_article/blog_digital_ondemand/NB2020072859.html 中國信託銀行(2024年4月18日)。金檢聯防再進化打造詐騙預警偵測機制。https://www.ctbcbank.com/twrbo/zh_tw/index/ctbc_article/digital_article/blog_digital_ondemand/NB2024041823.html 玉山銀行。金融影響力。https://www.esunfhc.com/zh-tw/esg/finance/fintech 汪海清(2004)。企業徵信調查實務。財團法人台灣金融研訓院。 李昀璇(2023年11月25日)。臺灣多家銀行公開展示生成式AI應用研究,包含虛擬行員和內部輔助工具。https://www.ithome.com.tw/news/160008 林左裕(2023年6月13日)。金融放款、AI與外部估價可降低詐貸及泡沫風險。https://rer.nccu.edu.tw/article/detail/2306131459851 林金定, 嚴嘉楓, & 陳美花(2005)。質性研究方法:訪談模式與實施步驟分析。身心障礙研究季刊,3(2),122-136。http://dx.doi.org/10.30072/JDR.200506.0005 金融監督管理委員會(2015年1月13日)。打造數位化金融環境3.0全面啟動。https://www.fsc.gov.tw/ch/home.jsp?id=96&parentpath=0,2&mcustomize=news_view.jsp&dataserno=201501130003&toolsflag=Y&dtable=News 金融監督管理委員會銀行局(2024)。金融業務統計輯要第555期。https://www.fsc.gov.tw/webdowndoc?file=/stat/abs/11212.pdf 財團法人台灣金融研訓院(2023)。銀行授信實務(2023年版)。 財團法人台灣金融研訓院(2022)。我國銀行業金融科技創新與數位轉型大調查。https://www.tabf.org.tw/Article.aspx?id=4053&cid=1 倍力資訊。年度最夯話題:機器人流程自動化RPA到底是什麼?https://cpm.mpinfo.com.tw/article_d.php?lang=tw&tb=1&cid=20&id=261 陳昇瑋、溫怡玲(2019)。人工智慧在台灣。天下雜誌。 第一金控(2023)。2022年永續報告書。 國泰金控(2019年5月31日)。國泰智能客服「阿發」海內外獲雙獎肯定。https://www.cathayholdings.com/holdings/lastest_news/news_archive/newsarticle?newsID=9Q3PAgPOxUWpwNhmNk3mrg 黃哲斌(2023年7月3日)。We Are Frienemy!人工智慧如何改變新聞業?獨立評論@天下。https://opinion.cw.com.tw/blog/profile/51/article/13795 黃健雄(2020)。介接公務機關資料介紹及作業管理規範說明。金融聯合徵信第三十六期。 彰化銀行(2024)。2023年永續報告書。 彰化銀行(2024)。2023年年報。 熊治民(2023年7月19日)。生成式AI在製造領域應用展望。https://www.moea.gov.tw/MNS/doit/industrytech/IndustryTech.aspx?menu_id=13545&it_id=490 AWS. 什麼是生成式 AI? https://aws.amazon.com/tw/what-is/generative-ai/ CB Insights.(2023). Generative AI Bible: The ultimate guide to genAI disruption. https://www.cbinsights.com/research/report/generative-ai-bible/ Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard business review, 96(1), 108-116. Financial Stability Board.(2017). Artificial intelligence and machine learning in financial services Market developments and financial stability implications. https://www.fsb.org/wp-content/uploads/P011117.pdf GlobeNewswire.(January 12, 2022). Video conferencing market value worldwide in 2022 and 2027 (in billion U.S. dollars) [Graph]. In Statista. Retrieved January 15, 2024, from https://www.statista.com/statistics/1293045/video-conferencing-market-value-worldwide/ Goodell, J. W., Kumar, S., Lim, W. M., & Pattnaik, D.(2021). Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis. Journal of Behavioral and Experimental Finance, 32, 100577. Kontrimas, V., & Verikas, A. (2011). The mass appraisal of the real estate by computational intelligence. Applied Soft Computing, 11(1), 443-448. Kok, N., Koponen, E. L., & Martínez-Barbosa, C. A.(2017). Big data in real estate? From manual appraisal to automated valuation. The Journal of Portfolio Management, 43(6), 202-211. Königstorfer, F., & Thalmann, S.(2020). Applications of Artificial Intelligence in commercial banks–A research agenda for behavioral finance. Journal of behavioral and experimental finance, 27, 100352. McKinsey & Company.(2018). An executive’s guide to AI. https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Analytics/Our%20Insights/An%20executives%20guide%20to%20AI/Executives-guide-to-AI McKinsey & Company.(2021). Operationalizing machine learning in processes. https://www.mckinsey.com/capabilities/operations/our-insights/operationalizing-machine-learning-in-processes McKinsey & Company.(2023a). The economic potential of generative AI: The next productivity frontier. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier#introduction McKinsey & Company.(2023b). How can generative AI add value in banking and financial services? https://www.mckinsey.com/featured-insights/lifting-europes-ambition/videos-and-podcasts/how-can-generative-ai-add-value-in-banking-and-financial-services#/ McKinsey & Company.(2024). What is generative AI? https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-generative-ai NVIDIA.(2023). State of AI in Financial Services: 2023 Trends. https://resources.nvidia.com/en-us-state-ai-report OpenAI. Introducing Whisper. https://openai.com/research/whisper Prabhdeep Singh(August 3, 2020). What Are AI and RPA: The Differences, Hype, and When to Use Them Together. https://www.uipath.com/blog/automation/ai-rpa-differences-when-to-use-them-together Syed, R., Suriadi, S., Adams, M., Bandara, W., Leemans, S. J., Ouyang, C., ... & Reijers, H. A.(2020). Robotic process automation: contemporary themes and challenges. Computers in Industry, 115, 103162. Shidaganti, G., Salil, S., Anand, P., & Jadhav, V.(2021, August). Robotic process automation with AI and OCR to improve business process. In 2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC) (pp. 1612-1618). IEEE. UiPath. Robotic Process Automation(RPA).https://www.uipath.com/rpa/robotic-process-automation
描述 碩士
國立政治大學
國際金融碩士學位學程
111ZB1068
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0111ZB1068
資料類型 thesis
dc.contributor.advisor 蔡瑞煌<br>林靖庭zh_TW
dc.contributor.author (Authors) 張佩瑜zh_TW
dc.contributor.author (Authors) Chang, Pei-Yuen_US
dc.creator (作者) 張佩瑜zh_TW
dc.creator (作者) Chang, Pei-Yuen_US
dc.date (日期) 2024en_US
dc.date.accessioned 5-Aug-2024 14:18:46 (UTC+8)-
dc.date.available 5-Aug-2024 14:18:46 (UTC+8)-
dc.date.issued (上傳時間) 5-Aug-2024 14:18:46 (UTC+8)-
dc.identifier (Other Identifiers) G0111ZB1068en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/152840-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 國際金融碩士學位學程zh_TW
dc.description (描述) 111ZB1068zh_TW
dc.description.abstract (摘要) 台灣金融研訓院(2022)報告指出,近90%的臺灣銀行業者已導入人工智慧(AI)、大數據及機器人流程自動化(RPA)技術,這些技術在未來將對銀行業務產生深遠影響,並且可能會改變其經營模式。授信是銀行的核心業務,其作業效率會直接影響銀行的營運績效,本研究以C銀行企業授信作業流程作為研究對象,探討將人工智慧及數位化工具導入C銀行企業授信作業中,並提出具體的應用方案,再與專家進行深度訪談評估應用方案在作業流程中運用的可行性和實用性。 本研究發現,人工智慧及數位化工具在銀行授信作業中的應用極具潛力,可提升作業效率和服務品質,但與客戶互動關係仍是銀行不可替代的核心競爭力。銀行在導入相關應用時,可以優先導入業務自動化的相關應用,能顯著提升作業效率和減少人力成本;讓工具作為輔助,保持人機協作平衡,同時提高效率及維持服務品質;管理階層應務實地評估實際需求,以避免不必要的投資及資源浪費;提供充分的教育訓練,可以讓應用方案順利推行及提升應用效果。zh_TW
dc.description.abstract (摘要) According to a report by the Taiwan Academy of Banking and Finance(2022), nearly 90% of Taiwan's banking industry has adopted artificial intelligence, big data, and robotic process automation technologies. These technologies are expected to have a profound impact on banking operations in the future and may change their business models. Loans and credit businesses are core operations of banks. This thesis focuses on the corporate credit operation process of Bank C, researching the application of AI and digital tools in the bank's corporate credit operations, proposing specific plans and interviewing experts to evaluate the feasibility and practicality of their use in the process. According to the results of this thesis, the application of AI and digital tools in bank credit operations has great potential to improve operational efficiency and service quality. However, customer interaction management remains an irreplaceable core competitive advantage for banks. When introducing related applications, banks can prioritize the implementation of business process automation tools, which can significantly enhance efficiency and reduce costs. Human-machine collaboration enhances efficiency while maintaining service quality. Conducting needs assessment can help avoid unnecessary investments and waste. Providing adequate training can ensure the smooth implementation of application plans and enhance their effectiveness.en_US
dc.description.tableofcontents 第一章 緒論 1 第一節 研究背景與動機 1 第二節 研究目的 2 第三節 研究流程 2 第二章 文獻探討 4 第一節 銀行授信業務 4 第二節 人工智慧(ARTIFICIAL INTELLIGENCE,AI) 5 第三節 人工智慧在國內銀行業的應用 7 第三章 個案分析—以C銀行為例 10 第一節 C銀行簡介 10 第二節 C銀行企業授信作業流程 11 第三節 應用人工智慧及數位化工具 17 第四章 深度訪談 23 第一節 訪談對象 23 第二節 訪談題目設計 23 第三節 訪談分析 25 第五章 結論與建議 33 第一節 研究發現 33 第二節 授信作業導入人工智慧及數位化工具之建議 34 第三節 研究限制 36 參考文獻 38zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0111ZB1068en_US
dc.subject (關鍵詞) 銀行授信zh_TW
dc.subject (關鍵詞) 人工智慧zh_TW
dc.subject (關鍵詞) Crediten_US
dc.subject (關鍵詞) Artificial intelligenceen_US
dc.title (題名) 應用人工智慧及數位化工具於銀行授信作業之研究-以C銀行為例zh_TW
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 中華民國銀行公會(2023)。中華民國銀行公會會員授信準則。 中華民國銀行公會(2023)。中華民國銀行公會會員徵信準則。 中華民國銀行公會(2024)。金融機構運用人工智慧技術作業規範。 中國信託銀行(2020年7月21日)。以客為本數位轉型新思維—無所不在、無時不在,中國信託有溫度的數位服務。https://www.ctbcbank.com/twrbo/zh_tw/index/ctbc_article/digital_article/blog_digital_ondemand/NB2020072859.html 中國信託銀行(2024年4月18日)。金檢聯防再進化打造詐騙預警偵測機制。https://www.ctbcbank.com/twrbo/zh_tw/index/ctbc_article/digital_article/blog_digital_ondemand/NB2024041823.html 玉山銀行。金融影響力。https://www.esunfhc.com/zh-tw/esg/finance/fintech 汪海清(2004)。企業徵信調查實務。財團法人台灣金融研訓院。 李昀璇(2023年11月25日)。臺灣多家銀行公開展示生成式AI應用研究,包含虛擬行員和內部輔助工具。https://www.ithome.com.tw/news/160008 林左裕(2023年6月13日)。金融放款、AI與外部估價可降低詐貸及泡沫風險。https://rer.nccu.edu.tw/article/detail/2306131459851 林金定, 嚴嘉楓, & 陳美花(2005)。質性研究方法:訪談模式與實施步驟分析。身心障礙研究季刊,3(2),122-136。http://dx.doi.org/10.30072/JDR.200506.0005 金融監督管理委員會(2015年1月13日)。打造數位化金融環境3.0全面啟動。https://www.fsc.gov.tw/ch/home.jsp?id=96&parentpath=0,2&mcustomize=news_view.jsp&dataserno=201501130003&toolsflag=Y&dtable=News 金融監督管理委員會銀行局(2024)。金融業務統計輯要第555期。https://www.fsc.gov.tw/webdowndoc?file=/stat/abs/11212.pdf 財團法人台灣金融研訓院(2023)。銀行授信實務(2023年版)。 財團法人台灣金融研訓院(2022)。我國銀行業金融科技創新與數位轉型大調查。https://www.tabf.org.tw/Article.aspx?id=4053&cid=1 倍力資訊。年度最夯話題:機器人流程自動化RPA到底是什麼?https://cpm.mpinfo.com.tw/article_d.php?lang=tw&tb=1&cid=20&id=261 陳昇瑋、溫怡玲(2019)。人工智慧在台灣。天下雜誌。 第一金控(2023)。2022年永續報告書。 國泰金控(2019年5月31日)。國泰智能客服「阿發」海內外獲雙獎肯定。https://www.cathayholdings.com/holdings/lastest_news/news_archive/newsarticle?newsID=9Q3PAgPOxUWpwNhmNk3mrg 黃哲斌(2023年7月3日)。We Are Frienemy!人工智慧如何改變新聞業?獨立評論@天下。https://opinion.cw.com.tw/blog/profile/51/article/13795 黃健雄(2020)。介接公務機關資料介紹及作業管理規範說明。金融聯合徵信第三十六期。 彰化銀行(2024)。2023年永續報告書。 彰化銀行(2024)。2023年年報。 熊治民(2023年7月19日)。生成式AI在製造領域應用展望。https://www.moea.gov.tw/MNS/doit/industrytech/IndustryTech.aspx?menu_id=13545&it_id=490 AWS. 什麼是生成式 AI? https://aws.amazon.com/tw/what-is/generative-ai/ CB Insights.(2023). Generative AI Bible: The ultimate guide to genAI disruption. https://www.cbinsights.com/research/report/generative-ai-bible/ Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard business review, 96(1), 108-116. Financial Stability Board.(2017). Artificial intelligence and machine learning in financial services Market developments and financial stability implications. https://www.fsb.org/wp-content/uploads/P011117.pdf GlobeNewswire.(January 12, 2022). Video conferencing market value worldwide in 2022 and 2027 (in billion U.S. dollars) [Graph]. In Statista. Retrieved January 15, 2024, from https://www.statista.com/statistics/1293045/video-conferencing-market-value-worldwide/ Goodell, J. W., Kumar, S., Lim, W. M., & Pattnaik, D.(2021). Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis. Journal of Behavioral and Experimental Finance, 32, 100577. Kontrimas, V., & Verikas, A. (2011). The mass appraisal of the real estate by computational intelligence. Applied Soft Computing, 11(1), 443-448. Kok, N., Koponen, E. L., & Martínez-Barbosa, C. A.(2017). Big data in real estate? From manual appraisal to automated valuation. The Journal of Portfolio Management, 43(6), 202-211. Königstorfer, F., & Thalmann, S.(2020). Applications of Artificial Intelligence in commercial banks–A research agenda for behavioral finance. Journal of behavioral and experimental finance, 27, 100352. McKinsey & Company.(2018). An executive’s guide to AI. https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Analytics/Our%20Insights/An%20executives%20guide%20to%20AI/Executives-guide-to-AI McKinsey & Company.(2021). Operationalizing machine learning in processes. https://www.mckinsey.com/capabilities/operations/our-insights/operationalizing-machine-learning-in-processes McKinsey & Company.(2023a). The economic potential of generative AI: The next productivity frontier. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier#introduction McKinsey & Company.(2023b). How can generative AI add value in banking and financial services? https://www.mckinsey.com/featured-insights/lifting-europes-ambition/videos-and-podcasts/how-can-generative-ai-add-value-in-banking-and-financial-services#/ McKinsey & Company.(2024). What is generative AI? https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-generative-ai NVIDIA.(2023). State of AI in Financial Services: 2023 Trends. https://resources.nvidia.com/en-us-state-ai-report OpenAI. Introducing Whisper. https://openai.com/research/whisper Prabhdeep Singh(August 3, 2020). What Are AI and RPA: The Differences, Hype, and When to Use Them Together. https://www.uipath.com/blog/automation/ai-rpa-differences-when-to-use-them-together Syed, R., Suriadi, S., Adams, M., Bandara, W., Leemans, S. J., Ouyang, C., ... & Reijers, H. A.(2020). Robotic process automation: contemporary themes and challenges. Computers in Industry, 115, 103162. Shidaganti, G., Salil, S., Anand, P., & Jadhav, V.(2021, August). Robotic process automation with AI and OCR to improve business process. In 2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC) (pp. 1612-1618). IEEE. UiPath. Robotic Process Automation(RPA).https://www.uipath.com/rpa/robotic-process-automationzh_TW