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浅析银行业智能客服系统的应用和发展 被引量:6

Analysis on the Application and Development of Intelligent Customer Service System in Banking Industry
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摘要 当前随着大数据、人工智能等互联网技术的飞速发展和运用,金融行业尤其是银行业也在逐渐发生巨大的技术化变革。此外,我国人口老龄化程度的进一步加深,人力成本也在不断增加。为了更好地降低人力成本,提升运营效率,出现了智能客服系统,通过使用智能客服来分流部分的人工话务量,可以有效提升效率和用户体验,从而更好地提升银行业的智能和高效程度。主要从服务型客服、投顾型客服、外呼型客服三个角度分析智能客服系统的发展背景和应用现状,对其在银行业实际中的应用进行相关探究,然后详细分析银行业智能客服存在的多种风险,并对未来发展提出相关的展望。 With the rapid development and application of Internet technologies such as big data and artificial intelligence,the financial industry,especially the banking industry,is gradually undergoing huge technological changes.In addition,with the further improvement of China’s aging population,labor costs are also increasing.On this basis,the demand for how to better reduce labor costs and improve operational efficiency is also gradually increasing.In this context,intelligent customer service system has emerged.By using intelligent customer service to divert part of the manual traffic,it can effectively improve efficiency and user experience,thereby better enhancing the intelligence and efficiency of the banking industry.This article analyzes the development background and application status of the intelligent customer service systemfromthree perspectives:service-oriented customer service,investment advisory customer service,and outbound customer service.Andmaker elevant resear chonits application in the banking industry.Then analyzed in detail the various risks of intelligent customer service in the banking industry,and put forward prospects for future development.
作者 王萌 许学军 WANG Meng;XU Xue-jun(College of Management,University of Shanghai for Science and Technology,Shanghai 200093,China)
出处 《经济研究导刊》 2021年第1期41-43,共3页 Economic Research Guide
关键词 智能客服 人工智能 银行 intelligent customer service artificial intelligence bank
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  • 1ICTCLAS简介[EB/OL].[2008-12-01].http://ictclas.org/sub_1_1.html.
  • 2孟凡博,刘进江.基于客服系统知识库的设计与实现[J].微计算机信息,2007,23(02X):235-237. 被引量:9
  • 3陈建美.中文情感词汇本体的构建及其应用[D].大连:大连理工大学,2009.
  • 4DAVE K, LAWRENCE S, PENNOCK D M. Mining the peanut gallery: opinion extraction and semantic classification of product reviews [ C ] J// Proceedings of the 12th International World Wide Web Conference, 2003 : 519-528.
  • 5LAPATA M, NG H T. Proceedings of the conference on empirical methods in natural language processing [ J ]. Postgraduate Medical Journal, 2006, 42 (484) : 130-131.
  • 6KANAYAMA H, NASUKAWA T. Fully automatic lex- icon expansion for domain oriented sentiment analysis [ C ]// Empirical Methods in Natural Language Pro- cessing, 2006: 355-363.
  • 7MORINAGA S, YAMANISHI K, TATEISHI K, et al. Mining product reputations on the Web [ J]. Kdd, 2002 : 341-349.
  • 8YEN S J, WU Y C, YANG J C, et al. A support vec- tor machine-based context-ranking model for question answering [ J ]. Information Sciences, 2013, 224 (2) : 77-87.
  • 9YU H, HATZIVASSILOGLOU V. Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences [ C ] // Proceedings of EMNLP-03, 2003 : 129-136.
  • 10NASUKAWA T, YI J. Sentiment analysis: capturing favorability using natural language processing [ C ]// Proceedings of The 2nd International Conference on Knowledge Capture. ACM, 2003 : 70-77.

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