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Suppetia ex machina: How can AI technologies aid financial decision-making of people with low socioeconomic status?

Author

Listed:
  • Christian T. Elbaek

    (Department of Management, Aarhus University, Denmark)

  • Ifeatu Uzodinma

    (UCL School of Slavonic and East European Studies, University College London, United Kingdom)

  • Zilia Ismagilova

    (Department of Economics and Management, University of Trento, Italy)

  • Panagiotis Mitkidis

    (Department of Management, Aarhus University, Denmark)

Abstract
In this conceptual paper, we outline how individuals with low socioeconomic status are more vulnerable to making choices that undermine their welfare in economic decision environments that require an acceptable comprehension of risk. We propose that novel technologies, specifically Artificial Intelligence, can aid in improving financial decision-making for individuals with low risk awareness, and we suggest avenues where policy can leverage emerging Artificial Intelligence technologies to design specific choice architecture that may support more risk-aware decision-making of vulnerable socioeconomic groups. Lastly, we discuss the ethics of utilizing nudges in vulnerable populations, the limitations of our approach, and how our paper can pave way for future research to improve decision-making for socioeconomically vulnerable individuals.

Suggested Citation

  • Christian T. Elbaek & Ifeatu Uzodinma & Zilia Ismagilova & Panagiotis Mitkidis, 2022. "Suppetia ex machina: How can AI technologies aid financial decision-making of people with low socioeconomic status?," Journal of Behavioral Economics for Policy, Society for the Advancement of Behavioral Economics (SABE), vol. 6(S1), pages 49-57, July.
    • Handle: RePEc:beh:jbepv1:v:6:y:2022:i:s1:p:49-57
    as

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    References listed on IDEAS

    as
    1. Dean Karlan & Margaret McConnell & Sendhil Mullainathan & Jonathan Zinman, 2016. "Getting to the Top of Mind: How Reminders Increase Saving," Management Science, INFORMS, vol. 62(12), pages 3393-3411, December.
    2. Giorgia Barboni & Alessandra Cassar & Timothee Demont, 2017. "Financial exclusion in developed countries: a field experiment among migrants and low-income people in Italy," Journal of Behavioral Economics for Policy, Society for the Advancement of Behavioral Economics (SABE), vol. 1(2), pages 39-49, September.
    3. Belleflamme, Paul & Vergote, Wouter, 2016. "Monopoly price discrimination and privacy: The hidden cost of hiding," Economics Letters, Elsevier, vol. 149(C), pages 141-144.
    4. Alessandro Acquisti & Hal R. Varian, 2005. "Conditioning Prices on Purchase History," Marketing Science, INFORMS, vol. 24(3), pages 367-381, May.
    5. Richard H. Thaler & Shlomo Benartzi, 2004. "Save More Tomorrow (TM): Using Behavioral Economics to Increase Employee Saving," Journal of Political Economy, University of Chicago Press, vol. 112(S1), pages 164-187, February.
    6. Cass R. Sunstein, 2018. "The rise of behavioral economics: Richard Thaler's misbehaving," Journal of Behavioral Economics for Policy, Society for the Advancement of Behavioral Economics (SABE), vol. 2(1), pages 53-57, March.
    7. Garrick Blalock & David R. Just & Daniel H. Simon, 2007. "Hitting the Jackpot or Hitting the Skids: Entertainment, Poverty, and the Demand for State Lotteries," American Journal of Economics and Sociology, Wiley Blackwell, vol. 66(3), pages 545-570, July.
    8. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    9. Shabnam Mousavi & Reza Kheirandish, 2017. "Policy making with behavioral insight," Journal of Behavioral Economics for Policy, Society for the Advancement of Behavioral Economics (SABE), vol. 1(1), pages 41-46, February.
    10. Leandro S. Carvalho & Stephan Meier & Stephanie W. Wang, 2016. "Poverty and Economic Decision-Making: Evidence from Changes in Financial Resources at Payday," American Economic Review, American Economic Association, vol. 106(2), pages 260-284, February.
    11. Katharina Dowling & Daniel Guhl & Daniel Klapper & Martin Spann & Lucas Stich & Narine Yegoryan, 2020. "Behavioral biases in marketing," Journal of the Academy of Marketing Science, Springer, vol. 48(3), pages 449-477, May.
    12. Rune V. Lesner, 2018. "The long-term effect of childhood poverty," Journal of Population Economics, Springer;European Society for Population Economics, vol. 31(3), pages 969-1004, July.
    13. Edmond Awad & Sohan Dsouza & Richard Kim & Jonathan Schulz & Joseph Henrich & Azim Shariff & Jean-François Bonnefon & Iyad Rahwan, 2018. "The Moral Machine experiment," Nature, Nature, vol. 563(7729), pages 59-64, November.
    14. Adam, Martin & Wessel, Michael & Benlian, Alexander, 2020. "AI-based chatbots in customer service and their effects on user compliance," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 119304, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    15. Spears Dean, 2011. "Economic Decision-Making in Poverty Depletes Behavioral Control," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 11(1), pages 1-44, December.
    16. George Loewenstein, 2000. "Emotions in Economic Theory and Economic Behavior," American Economic Review, American Economic Association, vol. 90(2), pages 426-432, May.
    17. Abhijit V. Banerjee & Sendhil Mullainathan, 2008. "Limited Attention and Income Distribution," American Economic Review, American Economic Association, vol. 98(2), pages 489-493, May.
    18. Ulrich Schmidt & Levent Neyse & Milda Aleknonyte, 2019. "Income inequality and risk taking: the impact of social comparison information," Theory and Decision, Springer, vol. 87(3), pages 283-297, October.
    19. Camerer, Colin & Weber, Martin, 1992. "Recent Developments in Modeling Preferences: Uncertainty and Ambiguity," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 325-370, October.
    20. Richard H. Thaler, 2000. "From Homo Economicus to Homo Sapiens," Journal of Economic Perspectives, American Economic Association, vol. 14(1), pages 133-141, Winter.
    21. Parker, Jeffrey R. & Lehmann, Donald R., 2011. "When Shelf-Based Scarcity Impacts Consumer Preferences," Journal of Retailing, Elsevier, vol. 87(2), pages 142-155.
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    More about this item

    Keywords

    decision-making; SES; AI; nudging; choice architecture;
    All these keywords.

    JEL classification:

    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • I30 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • P46 - Political Economy and Comparative Economic Systems - - Other Economic Systems - - - Consumer Economics; Health; Education and Training; Welfare, Income, Wealth, and Poverty

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