Impact of Chatbot Service on Bank Performance Based on a Case Study of IBM Corporation

Haoqi Wu

doi:10.54097/0y765327

Authors

Haoqi Wu

DOI:

https://doi.org/10.54097/0y765327

Keywords:

Chatbot; Bank performance; Risk management; Mechanism of action.

Abstract

Exploring the impact of chatbot on bank performance helps to identify the potential of this technology to revolutionize the bank's business model and enhance customer experience, while it can improve employee efficiency and provide a scientific basis and guidance for bankers to effectively respond to the increasing competitive market and achieve bank performance growth and sustainable development. This paper delineates the definitions of chatbot and business performance, provides an overview of current research, and examines the influence of chatbots on bank performance. Furthermore, it utilizes IBM's chatbot as a case study to investigate its impact on banking performance, focusing primarily on cost reduction, enhanced customer satisfaction, and improved employee efficiency. At the same time, the banking industry also faces the negative risk impact of chatbot, which mainly includes perceived risk, security threat and moral risk. The paper also concludes with recommendations on the negative risk impacts of smart customer service. This study links chatbot with bank performance to fill the research gap. It also provides an outlook and suggestions for the future development prospects of chatbot in the banking industry.

Downloads

Download data is not yet available.

References

Nithuna S, Laseena C A. Review on implementation techniques of chatbot[C]//2020 International Conference on Communication and Signal Processing (ICCSP). IEEE, 2020: 0157-0161. Pérez-Soler S, Juarez-Puerta S, Guerra E, et al. choosing a chatbot development tool [J]. IEEE Software, 2021, 38(4): 94-103.

Pérez-Soler S, Juarez-Puerta S, Guerra E, et al. choosing a chatbot development tool [J]. IEEE Software, 2021, 38(4): 94-103.

Ahmad N A, Che M H, Zainal A, et al. Review of chatbots design techniques [J]. International Journal of Computer Applications, 2018, 181(8): 7-10.

Ansari M, Shaikh S, Parbulkar M S, et al. Intelligent Chatbot[J]. INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) NREST–2021, 2021, 9(04).

Raffoni A, Visani F, Bartolini M, et al. Business performance analytics: exploring the potential for performance management systems [J]. Production Planning & Control, 2018, 29(1): 51-67.

Eccles R G. The performance measurement manifesto [J]. Harvard business review, 1991, 69(1): 131-137.

Kaplan R S, Norton D P. Balanced Scorecard Success: The Kaplan-Norton Collection (4 Books) [M]. Harvard Business Review Press, 2015.

Turing A M. Computing machinery and intelligence [M]. Springer Netherlands, 2009.

Weizenbaum J. ELIZA—a computer program for the study of natural language communication between man and machine [J]. Communications of the ACM, 1966, 9(1): 36-45.

Colby K M, Weber S, Hilf F D. Artificial paranoia [J]. Artificial intelligence, 1971, 2(1): 1-25.

Wallace R S. The anatomy of ALICE [M]. Springer Netherlands, 2009.

Mikic F A, Burguillo J C, Llamas M, et al. Charlie: An aiml-based chatterbot which works as an interface among ines and humans[C]//2009 EAEEIE annual conference. IEEE, 2009: 1-6.

Soofastaei A. Introductory Chapter: Virtual Assistants [J]. Virtual Assistant, 2021, 1.

Io H N, Lee C B. Understanding the Adoption of Chatbot: A Case Study of Siri[C]//Advances in Information and Communication Networks: Proceedings of the 2018 Future of Information and Communication Conference (FICC), Vol. 1. Springer International Publishing, 2019: 632-643.

Kepuska V, Bohouta G. Next-generation of virtual personal assistants (microsoft cortana, apple siri, amazon alexa and google home) [C]//2018 IEEE 8th annual computing and communication workshop and conference (CCWC). IEEE, 2018: 99-103.

Srivastava S, Prabhakar T V. Desirable features of a Chatbot-building platform[C]//2020 IEEE International Conference on Humanized Computing and Communication with Artificial Intelligence (HCCAI). IEEE, 2020: 61-64.

Kaplan R S, Cooper R. Cost & effect: using integrated cost systems to drive profitability and performance [M]. Harvard Business Press, 1998.

Thatcher M E, Oliver J R. The impact of technology investments on a firm’s production efficiency, product quality, and productivity [J]. Journal of management information systems, 2001, 18(2): 17-45.

Van Der Wiele T, Boselie P, Hesselink M. Empirical evidence for the relationship between customer satisfaction and business performance [J]. Managing Service Quality: An International Journal, 2002, 12(3): 184-193.

Jacobson R. Unobservable effects and business performance [J]. Marketing Science, 1990, 9(1): 74-85.

Wang X, Lin X, Shao B. How does artificial intelligence create business agility? Evidence from chatbots [J]. International journal of information management, 2022, 66: 102535.

Fotheringham D, Wiles M A. The effect of implementing chatbot customer service on stock returns: An event study analysis [J]. Journal of the Academy of Marketing Science, 2023, 51(4): 802-822.

Friedman N, Geiger D, Goldszmidt M. Bayesian network classifiers [J]. Machine learning, 1997, 29: 131-163.

Schuster M, Paliwal K K. Bidirectional recurrent neural networks [J]. IEEE transactions on Signal Processing, 1997, 45(11): 2673-2681.

Chen C H, Lee W P, Huang J Y. Tracking and recognizing emotions in short text messages from online chatting services [J]. Information Processing & Management, 2018, 54(6): 1325-1344.

Hwang S, Kim J. Toward a chatbot for financial sustainability [J]. Sustainability, 2021, 13(6): 3173.

Khan S, Rabbani M R. Chatbot as Islamic finance expert (CaIFE) when finance meets artificial intelligence[C]//proceedings of the 2020 4th international symposium on computer science and intelligent control. 2020: 1-5.

Quah J T S, Chua Y W. Chatbot assisted marketing in financial service industry[C]//Services Computing–SCC 2019: 16th International Conference, Held as Part of the Services Conference Federation, SCF 2019, San Diego, CA, USA, June 25–30, 2019, Proceedings 16. Springer International Publishing, 2019: 107-114.

Oruganti S C. Virtual bank assistance: An AI based voice bot for better banking [J]. International Journal of Research, 2020, 9(1): 177-183.

Devi C P. Banking Chatbot (B-Bot [J]. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021, 12(10): 5795-5804.

Brandon T, Maria M. How Chatbots Can Provide a Better Customer Experience [EB]. https://www.ibm.com/blog/how-chatbots-can-provide-a-better-customer-experience/

IBM. Customer response in seconds, not minutes [EB]. https://www.ibm.com/case-studies/bradesco.

Forrest. The Total Economic Impact of IBM Watson Assistant. [Z]. https://www.ibm.com/downloads/cas/ZGL0ZNBE

Holton G A. Defining risk [J]. Financial analyst’s journal, 2004, 60(6): 19-25.

Trivedi J. Examining the customer experience of using banking chatbots and its impact on brand love: The moderating role of perceived risk [J]. Journal of internet Commerce, 2019, 18(1): 91-111.

Mehrolia S, Alagarsamy S, Moorthy V, et al. Will users continue using banking chatbots? The moderating role of perceived risk [J]. FIIB Business Review, 2023: 23197145231169900.

Bozic J, Wotawa F. Security testing for chatbots[C]//IFIP International Conference on Testing Software and Systems. Cham: Springer International Publishing, 2018: 33-38.

Waheed N, Ikram M, Hashmi S S, et al. An empirical assessment of security and privacy risks of web-based Chatbots[C]//International Conference on Web Information Systems Engineering. Cham: Springer International Publishing, 2022: 325-339.

Derner E, Batistič K. Beyond the safeguards: Exploring the security risks of chatgpt[J]. arXiv preprint arXiv:2305.08005, 2023.

Yang J, Chen Y L, Por L Y, et al. A systematic literature review of information security in chatbots[J]. Applied Sciences, 2023, 13(11): 6355.