ABSTRACT
Alerting customers on suspected online-payment fraud and persuade them to terminate transactions is increasingly requested with the rapid growth of digital finance worldwide. We explored the feasibility of using a conversational agent (CA) to fulfill this request. Shing, a voice-based CA, proactively initializes and repairs the conversation with empathetical communication skills in order to alert customers when a suspected online-payment fraud is detected, collects important information for fraud scrutiny and persuades customers to terminate the transaction once the fraud is confirmed. We evaluated our system by comparing it with a rule-based CA with regards to customer response and perceptions in a real-world context where our systems took 144,795 phone calls in total in which 83,019 (57.3%) natural breakdowns happened. Results showed that more customers stopped risky transactions after conversing with Shing. They seemed more willing to converse with Shing for more dialogue turns and provide transaction details. Our work presents practical implications for the design of proactive CA.
- Cigdem Akkaya and Helmut Krcmar. 2019. Potential use of digital assistants by governments for citizen services: The case of Germany. In ACM International Conference Proceeding Series. Association for Computing Machinery, New York, New York, USA, 81–90. https://doi.org/10.1145/3325112.3325241Google ScholarDigital Library
- Zahra Ashktorab, Mohit Jain, Q. Vera Liao, and Justin D. Weisz. 2019. Resilient chatbots: Repair strategy preferences for conversational breakdowns. In Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, New York, USA, 1–12. https://doi.org/10.1145/3290605.3300484Google ScholarDigital Library
- Paul S. Bellet and Michael J. Maloney. 1991. The Importance of Empathy as an Interviewing Skill in Medicine. JAMA: The Journal of the American Medical Association 266, 13 (1991), 1831–1832. https://doi.org/10.1001/jama.1991.03470130111039Google ScholarCross Ref
- Izak Benbasat and Weiquan Wang. 2005. Trust In and Adoption of Online Recommendation Agents. Journal of the Association for Information Systems 6, 3 (mar 2005), 72–101. https://doi.org/10.17705/1jais.00065Google ScholarCross Ref
- Timothy Bickmore, Daniel Mauer, Francisco Crespo, and Thomas Brown. 2007. Persuasion, task interruption and health regimen adherence. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 4744 LNCS. Springer Verlag, 1–11. https://doi.org/10.1007/978-3-540-77006-0_1Google ScholarCross Ref
- Scott Brave, Clifford Nass, and Kevin Hutchinson. 2005. Computers that care: Investigating the effects of orientation of emotion exhibited by an embodied computer agent. International Journal of Human Computer Studies (2005). https://doi.org/10.1016/j.ijhcs.2004.11.002Google ScholarDigital Library
- Eugene Cho. 2019. Hey Google, can I ask you something in private? The effects of modality and device in sensitive health information acquisition from voice assistants. In Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3290605.3300488Google ScholarDigital Library
- Colin Mackinnon Clark, Ulrike Marianne Murfett, Priscilla S. Rogers, and Soon Ang. 2013. Is Empathy Effective for Customer Service? Evidence From Call Center Interactions. Journal of Business and Technical Communication 27, 2 (2013), 123–153.Google ScholarCross Ref
- Herbert H. Clark and Susan E. Brennan. 2004. Grounding in communication. In Perspectives on socially shared cognition. American Psychological Association, 127–149. https://doi.org/10.1037/10096-006Google ScholarCross Ref
- Leigh Clark, Nadia Pantidi, Orla Cooney, Philip Doyle, Diego Garaialde, Justin Edwards, Brendan Spillane, Emer Gilmartin, Christine Murad, Cosmin Munteanu, Vincent Wade, and Benjamin R. Cowan. 2019. What makes a good conversation? Challenges in designing truly conversational agents. In Conference on Human Factors in Computing Systems. 1–12. https://doi.org/10.1145/3290605.3300705Google ScholarDigital Library
- David DeVault, Ron Artstein, Grace Benn, Teresa Dey, Ed Fast, Alesia Gainer, Kallirroi Georgila, Jon Gratch, Arno Hartholt, Margaux Lhommet, Gale Lucas, Stacy Marsella, Fabrizio Morbini, Angela Nazarian, Stefan Scherer, Giota Stratou, Apar Suri, David Traum, Rachel Wood, Yuyu Xu, Albert Rizzo, and Louis Philippe Morency. 2014. SimSensei kiosk: A virtual human interviewer for healthcare decision support. In 13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014.Google Scholar
- Jacob Devlin, Ming Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. arXiv:1810.04805Google Scholar
- Dinev and Hart. 2006. Privacy Concerns and Levels of Information Exchange: An Empirical Investigation of Intended e-Services Use. e-Service Journal 4, 3 (2006), 25. https://doi.org/10.2979/esj.2006.4.3.25Google ScholarCross Ref
- Sara Engelhardt, Emmeli Hansson, and Iolanda Leite. 2017. Better faulty than sorry: Investigating social recovery strategies to minimize the impact of failure in human-robot interaction. In CEUR Workshop Proceedings.Google Scholar
- Asbjørn Følstad, Cecilie Bertinussen Nordheim, and Cato Alexander Bjørkli. 2018. What makes users trust a chatbot for customer service? An exploratory interview study. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11193 LNCS. Springer Verlag, 194–208. https://doi.org/10.1007/978- 3- 030- 01437- 7_16Google ScholarCross Ref
- Google Design. Overview - Conversational components - Conversation design. Retrieved September 10, 2020 from https://designguidelines.withgoogle.com/conversation/conversational-components/overview.htmlGoogle Scholar
- William Ickes. 1993. Empathic Accuracy. Journal of Personality (1993).Google Scholar
- Mohit Jain, Pratyush Kumar, Ishita Bhansali, Q Vera Liao, Khai Truong, and Shwetak Patel. 2018. FarmChat: A Conversational Agent to Answer Farmer Queries. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2, 4 (dec 2018). https://doi.org/10.1145/3287048Google ScholarDigital Library
- Mohit Jain, Pratyush Kumar, Ramachandra Kota, and Shwetak N. Patel. 2018. Evaluating and informing the design of chatbots. In Proceedings of the 2018 Designing Interactive Systems Conference. Association for Computing Machinery, Inc, New York, New York, USA, 895–906. https://doi.org/10.1145/3196709.3196735Google ScholarDigital Library
- Jieun Kim, Woochan Kim, Jungwoo Nam, and Hayeon Song. 2020. "I Can Feel Your Empathic Voice": Effects of Nonverbal Vocal Cues in Voice User Interface. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems. ACM, New York, NY, USA, 1–8. https://doi.org/10.1145/3334480.3383075Google ScholarDigital Library
- Soomin Kim, Joonhwan Lee, and Gahgene Gweon. 2019. Comparing data from chatbot and web surveys effects of platform and conversational style on survey response quality. In Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, New York, USA, 1–12. https://doi.org/10.1145/3290605.3300316Google ScholarDigital Library
- Yoon Kim. 2014. Convolutional neural networks for sentence classification. In EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing. https://doi.org/10.3115/v1/d14-1181. arXiv:1408.5882Google ScholarCross Ref
- Yanghee Kim and Amy L. Baylor. 2006. Pedagogical agents as learning companions: The role of agent competency and type of interaction. Educational Technology Research and Development 54, 3 (jun 2006), 223–243. https://doi.org/10.1007/s11423-006-8805-zGoogle ScholarCross Ref
- Rafal Kocielnik, Daniel Avrahami, Jennifer Marlow, Di Lu, and Gary Hsieh. 2018. Designing for workplace reflection: A chat and voice-based conversational agent. In Proceedings of the 2018 Designing Interactive Systems Conference. Association for Computing Machinery, Inc, New York, New York, USA, 881–894. https://doi.org/10.1145/3196709.3196784Google ScholarDigital Library
- Lai S, Xu L, Liu K, Recurrent convolutional neural networks for text classification[C]//Twenty-ninth AAAI conference on artificial intelligence. 2015.Google Scholar
- Min Kyung Lee, Sara Kiesler, Jodi Forlizzi, Siddhartha Srinivasa, and Paul Rybski. 2010. Gracefully mitigating breakdowns in robotic services. In 5th ACM/IEEE International Conference on Human-Robot Interaction, HRI 2010. https://doi.org/10.1145/1734454.1734544Google ScholarCross Ref
- Yeoreum Lee, Jae-Eul Bae, Sona S Kwak, and Myung-Suk Kim. 2011. The effect of politeness strategy on human - robot collaborative interaction on malfunction of robot vacuum cleaner.Google Scholar
- Yi Chieh Lee, Naomi Yamashita, and Yun Huang. 2020. Designing a Chatbot as a Mediator for Promoting Deep Self-Disclosure to a Real Mental Health Professional. In Proceedings of the ACM on Human-Computer Interaction 4, CSCW1 (may 2020), 1–27. https://doi.org/10.1145/3392836Google ScholarDigital Library
- Yi-Chieh Lee, Naomi Yamashita, Yun Huang, and Wai Fu. 2020. "I Hear You, I Feel You": Encouraging Deep Self- Disclosure through a Chatbot. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI’20). Association for Computing Machinery, New York, NY, USA, 1–12. https://doi.org/10.1145/3313831.3376175Google ScholarDigital Library
- Robert W. Levenson and Anna M. Ruef. 1992. Empathy: A Physiological Substrate. Journal of Personality and Social Psychology 63, 2 (1992), 234–246. https://doi.org/10.1037/0022-3514.63.2.234Google ScholarCross Ref
- Chi-Hsun Li, Su-Fang Yeh, Tang-Jie Chang, Meng-Hsuan Tsai, Ken Chen, and Yung-Ju Chang. 2020. A Conversation Analysis of Non-Progress and Coping Strategies with a Banking Task-Oriented Chatbot. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI’20). Association for Computing Machinery, New York, NY, USA,. https://doi.org/10.1145/3313831.3376209Google ScholarDigital Library
- Q. Vera Liao, Matthew Davis, Werner Geyer, Michael Muller, and N. Sadat Shami. 2016. What can you do? Studying social-agent orientation and agent proactive interactions with an agent for employees. In Proceedings of the 2016 ACM Conference on Designing Interactive Systems. Association for Computing Machinery, Inc, New York, New York, USA, 264–275. https://doi.org/10.1145/2901790.2901842Google ScholarDigital Library
- Ewa Luger and Abigail Sellen. 2016. "Like Having a Really Bad PA": The Gulf between User Expectation and Experience of Conversational Agents. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI ’16). Association for Computing Machinery, New York, NY, USA, 5286–5297. https://doi.org/10.1145/2858036.2858288Google ScholarDigital Library
- Chelsea Myers, Anushay Furqan, Jessica Nebolsky, Karina Caro, and Jichen Zhu. 2018. Patterns for how users overcome obstacles in Voice User Interfaces. In Conference on Human Factors in Computing Systems, Vol. 2018-April. Association for Computing Machinery, New York, New York, USA, 1–7. https://doi.org/10.1145/3173574.3173580Google ScholarDigital Library
- Clifford Nass, Janathan Steuer, and Ellen R. Tauber. 1994. Computer are social actors. In Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, New York, USA. https://doi.org/10.1145/259963.260288Google ScholarDigital Library
- Oxford. Backchannel. Oxford Learner's Dictionaries. Retrieved August 14, 2020 from https://www.oxfordlearnersdictionaries.com/us/definition/american_english/backchannelGoogle Scholar
- Sohyun Park, Jeewon Choi, Sungwoo Lee, Changhoon Oh, Changdai Kim, Soohyun La, Joonhwan Lee, and Bongwon Suh. 2019. Designing a chatbot for a brief motivational interview on stress management: Qualitative case study. Journal of Medical Internet Research 21, 4 (apr 2019), e12231. https://doi.org/10.2196/12231Google ScholarCross Ref
- Rosalind W Picard and Jonathan Klein. 2002. Computers that recognise and respond to user emotion: theoretical and practical implications. Interacting with Computers 14, 2 (feb 2002), 141–169. https://doi.org/10.1016/S0953-5438(01)00055-8Google ScholarCross Ref
- Martin Porcheron, Joel E. Fischer, Stuart Reeves, and Sarah Sharples. 2018. Voice interfaces in everyday life. In Conference on Human Factors in Computing Systems, Vol. 2018-April. Association for Computing Machinery, New York, New York, USA, 1–12. https://doi.org/10.1145/3173574.3174214Google ScholarDigital Library
- Maha Salem, Gabriella Lakatos, Farshid Amirabdollahian, and Kerstin Dautenhahn. 2015. Would You Trust a (Faulty) Robot?: Effects of Error, Task Type and Personality on Human-Robot Cooperation and Trust. In ACM/IEEE International Conference on Human-Robot Interaction, Vol. 2015-March. IEEE Computer Society, New York, New York, USA, 141–148. https://doi.org/10.1145/2696454.2696497Google ScholarDigital Library
- Shruti Sannon, Brett Stoll, Dominic DiFranzo, Malte Jung, and Natalya N. Bazarova. 2018. How personification and interactivity influence stress-related disclosures to conversational agents. In Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW. Association for Computing Machinery, New York, NY, USA, 285–288. https://doi.org/10.1145/3272973.3274076Google ScholarDigital Library
- Emanuel A. Schegloff, Gail Jefferson, and Harvey Sacks. 1977. The Preference for Self-Correction in the Organization of Repair in Conversation. Language (1977). https://doi.org/10.2307/413107Google ScholarCross Ref
- Vasant Srinivasan and Leila Takayama. 2016. Help me please: Robot politeness strategies for soliciting help from people. In Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, 4945–4955. https://doi.org/10.1145/2858036.2858217Google ScholarDigital Library
- Statista. Digital Payments - worldwide | Statista Market Forecast. Retrieved August 12, 2020 from https://www.statista.com/outlook/296/100/digital-payments/worldwide.Google Scholar
- Ella Tallyn, Hector Fried, Rory Gianni, Amy Isard, and Chris Speed. 2018. The ethnobot: Gathering ethnographies in the age of IoT. In Conference on Human Factors in Computing Systems, Vol. 2018-April. Association for Computing Machinery, New York, New York, USA, 1–13. https://doi.org/10.1145/3173574.3174178Google ScholarDigital Library
- Raymond C. Tait. 2008. Empathy: Necessary for effective pain management? Current Pain and Headache Reports 12, 2 (2008), 108–112. https://doi.org/10.1007/s11916-008-0021-6Google ScholarCross Ref
- Chongyang Tao, Wei Wu, Can Xu, Wenpeng Hu, Dongyan Zhao and Rui Yan. One Time of Interaction May Not Be Enough: Go Deep with an Interaction-over-Interaction Network for Response Selection in Dialogues. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1–11 Florence, Italy, July 28 - August 2, 2019, Association for Computational Linguistics.Google ScholarCross Ref
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems.Google Scholar
- Yang Wang, Huichuan Xia, and Yun Huang. 2016. Examining American and Chinese internet users’ contextual privacy preferences of behavioral advertising. In Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW, Vol. 27. Association for Computing Machinery, New York, New York, USA, 539–552. https://doi.org/10.1145/2818048.2819941Google ScholarDigital Library
- Ziang Xiao, Michelle X. Zhou, Wenxi Chen, Huahai Yang, and Changyan Chi. 2020. If I Hear You Correctly: Building and Evaluating Interview Chatbots with Active Listening Skills. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery (ACM), New York, NY, USA, 1–14. https://doi.org/10.1145/3313831.3376131Google ScholarDigital Library
- Ziang Xiao, Michelle X. Zhou, Q. Vera Liao, Gloria Mark, Changyan Chi, Wenxi Chen, and Huahai Yang. 2019. Tell Me About Yourself: Using an AI-Powered Chatbot to Conduct Conversational Surveys with Open-ended Questions. ACM Transactions on Computer-Human Interaction 27,3(may2019),1–37. https://doi.org/10.1145/3381804Google ScholarDigital Library
- V. H. Yngve. 1970. On getting a word in edgewise. Chicago Linguistics Society, 6th Meeting (1970).Google Scholar
Index Terms
- Shing: A Conversational Agent to Alert Customers of Suspected Online-payment Fraud with Empathetical Communication Skills
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