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Generative AI for Surveys on Payment Apps: AIs' View on Privacy and Technology

Author

Listed:
  • Koji Takahashi

    (Director, Institute for Monetary and Economic Studies, Bank of Japan (E-mail: kouji.takahashi-2@boj.or.jp))

  • Joon Suk Park

    (Manager, Office of Digital Currency of Bank of Korea (E-mail: parkjs@bok.or.kr))

Abstract

We use generative artificial intelligence (GenAI), specifically ChatGPT, to simulate surveys on payment tools, focusing on perceptions of privacy and benefits. To validate the responses generated by GenAI, we compare the results with an existing survey on the privacy of financial apps by Brits and Jonker (2023). By designing prompts for hypothetical respondents (hereafter generative agents) that mirror the distribution of characteristics observed in actual surveys, we find that their views on payment app benefits and privacy align with real survey results when respondents are grouped by their level of privacy concern. Privacy-concerned agents view financial apps less favorably and perceive more risks, even without indicating this tendency in the prompts. Additionally, ChatGPT reflects the stark difference between users and non- users observed in the actual survey, with users finding payment apps more beneficial and less risky than non-users, despite not specifying these features in the prompt. However, ChatGPT does not replicate the variation--measured by the standard deviation of responses--observed in the actual survey, even when we specify detailed demographic characteristics of the generative agents in the prompt to match the dispersion in the observed data. This result means that there is a possibility that minority opinions may not be reflected. Moreover, ChatGPT provides responses with a bias towards being more privacy concerned. These results suggest that GenAI has the potential to be used as a complementary tool for surveys on users' perceptions of the privacy and benefits of payment tools, rather than as a substitute for actual surveys responded by humans.

Suggested Citation

  • Koji Takahashi & Joon Suk Park, 2025. "Generative AI for Surveys on Payment Apps: AIs' View on Privacy and Technology," IMES Discussion Paper Series 25-E-13, Institute for Monetary and Economic Studies, Bank of Japan.
    • Handle: RePEc:ime:imedps:25-e-13
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    References listed on IDEAS

    as
    1. Nessrine Omrani & Nicolas Soulié, 2020. "Privacy Experience, Privacy Perception, Political Ideology and Online Privacy Concern: The Case of Data Collection in Europe," Revue d'économie industrielle, De Boeck Université, vol. 0(4), pages 217-255.
    2. John J. Horton, 2023. "Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?," NBER Working Papers 31122, National Bureau of Economic Research, Inc.
    3. Henner Gimpel & Dominikus Kleindienst & Daniela Waldmann, 2018. "The disclosure of private data: measuring the privacy paradox in digital services," Electronic Markets, Springer;IIM University of St. Gallen, vol. 28(4), pages 475-490, November.
    4. Li, Jiaqi, 2023. "Predicting the demand for central bank digital currency: A structural analysis with survey data," Journal of Monetary Economics, Elsevier, vol. 134(C), pages 73-85.
    5. Jens Ludwig & Sendhil Mullainathan & Ashesh Rambachan, 2024. "Large Language Models: An Applied Econometric Framework," Papers 2412.07031, arXiv.org, revised Dec 2025.
    6. Argyle, Lisa P. & Busby, Ethan C. & Fulda, Nancy & Gubler, Joshua R. & Rytting, Christopher & Wingate, David, 2023. "Out of One, Many: Using Language Models to Simulate Human Samples," Political Analysis, Cambridge University Press, vol. 31(3), pages 337-351, July.
    7. John J. Horton, 2023. "Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?," Papers 2301.07543, arXiv.org.
    8. Hans Brits & Nicole Jonker, 2023. "The Use of Financial Apps: Privacy Paradox or Privacy Calculus?," Working Papers 794, DNB.
    Full references (including those not matched with items on IDEAS)

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    Keywords

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    JEL classification:

    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software

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