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Shing: A Conversational Agent to Alert Customers of Suspected Online-payment Fraud with Empathetical Communication Skills

Published:07 May 2021Publication History

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.

References

  1. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  2. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  3. 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 ScholarGoogle ScholarCross RefCross Ref
  4. 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 ScholarGoogle ScholarCross RefCross Ref
  5. 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 ScholarGoogle ScholarCross RefCross Ref
  6. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  7. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  8. 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 ScholarGoogle ScholarCross RefCross Ref
  9. 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 ScholarGoogle ScholarCross RefCross Ref
  10. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  11. 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 ScholarGoogle Scholar
  12. 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 ScholarGoogle Scholar
  13. 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 ScholarGoogle ScholarCross RefCross Ref
  14. 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 ScholarGoogle Scholar
  15. 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 ScholarGoogle ScholarCross RefCross Ref
  16. Google Design. Overview - Conversational components - Conversation design. Retrieved September 10, 2020 from https://designguidelines.withgoogle.com/conversation/conversational-components/overview.htmlGoogle ScholarGoogle Scholar
  17. William Ickes. 1993. Empathic Accuracy. Journal of Personality (1993).Google ScholarGoogle Scholar
  18. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  19. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  20. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  21. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  22. 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 ScholarGoogle ScholarCross RefCross Ref
  23. 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 ScholarGoogle ScholarCross RefCross Ref
  24. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  25. Lai S, Xu L, Liu K, Recurrent convolutional neural networks for text classification[C]//Twenty-ninth AAAI conference on artificial intelligence. 2015.Google ScholarGoogle Scholar
  26. 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 ScholarGoogle ScholarCross RefCross Ref
  27. 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 ScholarGoogle Scholar
  28. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  29. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  30. 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 ScholarGoogle ScholarCross RefCross Ref
  31. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  32. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  33. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  34. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  35. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  36. Oxford. Backchannel. Oxford Learner's Dictionaries. Retrieved August 14, 2020 from https://www.oxfordlearnersdictionaries.com/us/definition/american_english/backchannelGoogle ScholarGoogle Scholar
  37. 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 ScholarGoogle ScholarCross RefCross Ref
  38. 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 ScholarGoogle ScholarCross RefCross Ref
  39. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  40. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  41. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  42. 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 ScholarGoogle ScholarCross RefCross Ref
  43. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  44. Statista. Digital Payments - worldwide | Statista Market Forecast. Retrieved August 12, 2020 from https://www.statista.com/outlook/296/100/digital-payments/worldwide.Google ScholarGoogle Scholar
  45. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  46. 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 ScholarGoogle ScholarCross RefCross Ref
  47. 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 ScholarGoogle ScholarCross RefCross Ref
  48. 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 ScholarGoogle Scholar
  49. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  50. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  51. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  52. V. H. Yngve. 1970. On getting a word in edgewise. Chicago Linguistics Society, 6th Meeting (1970).Google ScholarGoogle Scholar

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          cover image ACM Conferences
          CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
          May 2021
          10862 pages
          ISBN:9781450380966
          DOI:10.1145/3411764

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