Evolving Role of Financial Literacy in AI-Mediated Financial Ecosystems: A Human-Centric Theory of Contextual Financial Judgment

Conventional financial literacy agendas are based on the assumption of information paucity, where consumers actively seek out data and are completely dependent on human cognitive processing for their financial decision making. However, the rapid proliferation of Artificial Intelligence (AI), Financial Technology (FinTech) and algorithmic recommendation systems has fundamentally transformed personal finance. Consumers no longer are passive information seekers; but are constantly exposed to algorithmically curated financial content, personalized product and investment recommendations and behavioural nudges. Thus, the central challenge in financial literacy has pivoted from merely accessing financial data to critically appraising AI-mediated financial content and outputs.
Addressing this gap, the paper advances a human-centric re-interpretation of financial literacy, by demonstrating that although the foundational developmental architecture of financial literacy remains intact across its six core dimensions (Financial Exposure, Importance, Awareness, Knowledge, Attitude and Behaviour), its operational role advances in an AI-mediated environment. Synthesizing Dual-Process Theory, Extended Unified Theory of Acceptance and Use of Technology (UTAUT2), Behavioural Finance, Human Agency Theory, Explainable AI (XAI) and Adaptive Decision-Making Theory; the study establishes Contextual Financial Judgment (CFJ) - an emergent, integrative human capability arising from the convergence of Financial Awareness, In-depth Financial Knowledge and Financial Attitude. The CFJ acts as a System 2 (second-order) integrative capability - a cognitive circuit breaker through which individuals evaluate AI-generated recommendations by considering personal circumstances, financial goals and long-term targets prior to decision making.
Additionally, the paper outlines a broad conceptual framework illustrating the way in which the CFJ preserves human agency and redefines the professional financial planner as a Contextual Judgment Facilitator who supports reflective human-AI collaboration. Finally, the study provides a preliminary operational measurement matrix to guide empirical scale development and policy interventions.

Financial Literacy, Financial Planning, Human–AI Collaboration, Contextual Financial Judgment, Human Agency

1. Amershi, S., Weld, D., Vorvoreanu, M., Fournier, A., Simard, R., Caruana, C., Hopkins, B., Weisberg, M., Suh, H., Billingsley, W., Nushi, B., Chintagunta, S., Sullivan, J., Kiciman, E., Horvitz, E., & Meek, C. (2019). Guidelines for human-AI interaction. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1–13). Association for Computing Machinery. https://doi.org/10.1145/3290605.3300233 [Google Scholar] [Crossref]

2. Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, R., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012 [Google Scholar] [Crossref]

3. Atkinson, A., & Messy, F.-A. (2012). Measuring financial literacy: Results of the OECD/International Network on Financial Education (INFE) pilot study (OECD Working Papers on Finance, Insurance and Private Pensions, No. 15). OECD Publishing. https://doi.org/10.1787/5k9csfs90fr4-en [Google Scholar] [Crossref]

4. Baker, T., & Dellaert, B. G. C. (2018). Regulating robo advice across the financial services industry. Iowa Law Review, 103(2), 713–750. [Google Scholar] [Crossref]

5. Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52(1), 1–26. https://doi.org/10.1146/annurev.psych.52.1.1 [Google Scholar] [Crossref]

6. Barberis, N., & Thaler, R. (2003). A survey of behavioral finance. Handbook of the Economics of Finance, 1, 1053–1123. https://doi.org/10.1016/S1574-0102(03)01027-6 [Google Scholar] [Crossref]

7. Bartlett, R., Morse, A., Stanton, R., & Wallace, N. (2022). Consumer-lending discrimination in the FinTech era. Journal of Financial Economics, 143(1), 30–56. https://doi.org/10.1016/j.jfineco.2021.05.047 [Google Scholar] [Crossref]

8. Belanche, D., Casaló, L. V., & Flavián, C. (2019). Artificial Intelligence in financial services: Perception of risk and trust in robo-advisors. Service Business, 13(1), 141–165. https://doi.org/10.1007/s11628-018-00393-8 [Google Scholar] [Crossref]

9. Bi, R., & Fox, J. (2017). The role of financial planners in mitigating information asymmetry. Journal of Financial Counseling and Planning, 28(1), 45–58. https://doi.org/10.1891/1052-3073.28.1.45 [Google Scholar] [Crossref]

10. Bongomin, G. O. C., Ntayi, J. M., Munene, J. C., & Malinga, C. A. (2018). Financial literacy in emerging economies: Do all components matter for financial inclusion of poor households in rural Uganda? Managerial Finance, 44(12), 1302–1318. https://doi.org/10.1108/MF-04-2017-0117 [Google Scholar] [Crossref]

11. Breque, M., De Nul, L., & Petridis, A. (2021). Industry 5.0: Towards a sustainable, human-centric and resilient European industry. European Commission, Directorate-General for Research and Innovation. https://doi.org/10.2777/308401 [Google Scholar] [Crossref]

12. Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company. [Google Scholar] [Crossref]

13. Cao, L. (2022). AI in finance: A review. ACM Computing Surveys, 54(8), 1–38. https://doi.org/10.1145/3510414 [Google Scholar] [Crossref]

14. Chen, Y., Wu, X., & Zhang, S. (2018). Automation and Artificial Intelligence in Finance: Innovations and Challenges. Journal of Finance and Technology, 23(1), 1–15. [Google Scholar] [Crossref]

15. Chen, P., Wu, L., & Wang, L. (2023). AI fairness in data management and analytics: A review on challenges, methodologies and applications. Applied Sciences, 13(18), 10258. https://doi.org/10.3390/app131810258 [Google Scholar] [Crossref]

16. Cummings, M. L. (2014). Automation bias in intelligent time-critical decision support systems. Journal of Cognitive Engineering and Decision Making, 8(3), 257–262. https://doi.org/10.1177/1555343414539564 [Google Scholar] [Crossref]

17. Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42. https://doi.org/10.1007/s11747-019-00696-0 [Google Scholar] [Crossref]

18. Dellermann, F., Ebel, P., Söllner, M., & Leimeister, J. M. (2019). Hybrid intelligence. Business & Information Systems Engineering, 61(5), 637–643. https://doi.org/10.1007/s12599-019-00595-2 [Google Scholar] [Crossref]

19. Dellermann, F., Calma, A., Lipush, N., Weber, T., Söllner, M., & Leimeister, J. M. (2021). The future of work with hybrid intelligence: A taxonomy of human-AI collaboration. Journal of Information Technology, 36(4), 384–405. https://doi.org/10.1177/02683962211026402 [Google Scholar] [Crossref]

20. Dietvorst, B. J., Simmons, J. P., & Massey, C. (2018). Overcoming algorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them. Management Science, 64(3), 1155–1170. https://doi.org/10.1287/mnsc.2016.2643 [Google Scholar] [Crossref]

21. Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., … Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002 [Google Scholar] [Crossref]

22. Emirbayer, M., & Mische, A. (1998). What is agency? American Journal of Sociology, 103(4), 962–1023. https://doi.org/10.1086/231294 [Google Scholar] [Crossref]

23. European Commission. (2021). Industry 5.0: Human-centric, sustainable and resilient (Policy Brief). Directorate-General for Research and Innovation. https://doi.org/10.2777/080866 [Google Scholar] [Crossref]

24. Evans, J. St. B. T., & Stanovich, K. E. (2013). Dual-process theories of higher cognition: Advancing the debate. Perspectives on Psychological Science, 8(3), 223–241. https://doi.org/10.1177/1745691612460685 [Google Scholar] [Crossref]

25. Filippi, S., Guagliano, C., & Nicotra, M. (2022). The digital transformation of financial planning: Human wealth managers vs. robo-advisors. Journal of Financial Services Research, 61(2), 201–224. https://doi.org/10.1007/s10693-021-00368-x [Google Scholar] [Crossref]

26. Fornero, E., & Monticone, C. (2011). Financial literacy and pension plan participation in Italy. Journal of Pension Economics & Finance, 10(4), 547–564. https://doi.org/10.1017/S147474721100047X [Google Scholar] [Crossref]

27. Ghobakhloo, M., Iranmanesh, M., Mubarak, M. F., Foroughi, B., Hashemi-Petroudi, S. H., & Yadegaridehkordi, E. (2022). Industry 5.0: A review of the literature, and a framework for future research. Journal of Manufacturing Systems, 65, 412–424. https://doi.org/10.1016/j.jmsy.2022.10.014 [Google Scholar] [Crossref]

28. Gomber, P., Koch, J. A., & Siering, M. (2017). Digital Finance and FinTech: Current research and future research directions. Journal of Business Economics, 87(5), 537–580. https://doi.org/10.1007/s11573-017-0852-x [Google Scholar] [Crossref]

29. Gorai, S. K., & Maurya, A. (2025). Artificial intelligence in personal finance management: Opportunities and challenges. IOSR Journal of Computer Engineering, 27(1), 7–17. https://doi.org/10.9790/0661-2701010717 [Google Scholar] [Crossref]

30. Huang, M.-H., & Rust, R. T. (2021). Engaged to a robot? The role of AI in service. Journal of Service Research, 24(1), 3–16. https://doi.org/10.1177/1094670520902266 [Google Scholar] [Crossref]

31. Huston, S. J. (2010). Measuring financial literacy. The Journal of Consumer Affairs, 44(2), 296–316. https://doi.org/10.1111/j.1745-6606.2010.01170.x [Google Scholar] [Crossref]

32. Jarrahi, M. H. (2018). Artificial intelligence and the future of work: A human-AI symbiosis perspective. Business Horizons, 61(4), 577–586. https://doi.org/10.1016/j.bushor.2018.03.007 [Google Scholar] [Crossref]

33. Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux. [Google Scholar] [Crossref]

34. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291. https://doi.org/10.2307/1914185 [Google Scholar] [Crossref]

35. Khandani, A. E., Kim, A. J., & Lo, A. W. (2010). Consumer credit-risk models via machine-learning algorithms. Journal of Banking & Finance, 34(11), 2767–2787. https://doi.org/10.1016/j.jbankfin.2010.06.001 [Google Scholar] [Crossref]

36. Kinder, G., & Galvan, M. (2014). Life planning for financial planners. Harvard University Press. [Google Scholar] [Crossref]

37. Lasi, H., Fettke, P., Kemper, H. G., Feld, T., & Hoffmann, M. (2014). Industry 4.0. Business & Information Systems Engineering, 6(4), 239–242. https://doi.org/10.1007/s12599-014-0334-4 [Google Scholar] [Crossref]

38. Lipton, Z. C. (2018). The mythos of model interpretability. ACM SIGKDD Explorations Newsletter, 20(1), 1–14. https://doi.org/10.1145/3236386.3236392 [Google Scholar] [Crossref]

39. Lu, Y., Zheng, H., Chand, S., Xia, W., Liu, Z., Xu, X., Wang, L., & Wang, Y. (2022). Outlook on human-centric AI in Industry 5.0. IEEE Transactions on Industrial Informatics, 18(12), 8654–8662. https://doi.org/10.1109/TII.2022.3160885 [Google Scholar] [Crossref]

40. Lusardi, A., & Mitchell, O. S. (2014). The economic importance of financial literacy: Theory and evidence. Journal of Economic Literature, 52(1), 5–44. https://doi.org/10.1257/jel.52.1.5 [Google Scholar] [Crossref]

41. Lusardi, A., & Tufano, P. (2015). Debt literacy, financial experiences, and overindebtedness. Journal of Pension Economics & Finance, 14(4), 332–368. https://doi.org/10.1017/S1474747215000239 [Google Scholar] [Crossref]

42. Moghar, A., & Hamiche, M. (2020). Stock market prediction using LSTM recurrent neural network. Procedia Computer Science, 170, 1168–1173. https://doi.org/10.1016/j.procs.2020.03.049 [Google Scholar] [Crossref]

43. Muggleton, N., Parpart, P., Newall, P., Leake, D., & Stewart, N. (2022). The impact of automated decision tools on consumer financial behavior and awareness. Journal of Behavioral and Experimental Finance, 34, 100652. https://doi.org/10.1016/j.jbef.2022.100652 [Google Scholar] [Crossref]

44. OECD. (2020). OECD/INFE 2020 international survey of adult financial literacy. OECD Publishing. [Google Scholar] [Crossref]

45. Overton, R. (2008). Theories of the financial planning profession. Journal of Personal Finance, 7(1), 13–41. [Google Scholar] [Crossref]

46. Oyasiji, O., Okesiji, A., Imediegwu, C. C., Elebe, O., & Filani, O. M. (2023). Ethical AI in financial decision-making: Transparency, bias, and regulation. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 9(5), 453–471. https://doi.org/10.32628/CSEIT2390558 [Google Scholar] [Crossref]

47. Panos, G. A., & Wilson, J. O. (2020). Financial literacy and financial technology: What is the relationship? European Journal of Finance, 26(4–5), 319–323. https://doi.org/10.1080/1351847X.2019.1702204 [Google Scholar] [Crossref]

48. Parasuraman, R., & Manzey, D. H. (2010). Complacency and bias in human use of automation: An attentional and cognitive degradation analysis. Human Factors, 52(3), 381–410. https://doi.org/10.1177/0018720810376055 [Google Scholar] [Crossref]

49. Patel, R., Sharma, K., & Gupta, M. (2021). Digital financial interventions and real-time literacy: Evaluating consumer awareness in tech-driven financial ecosystems. Journal of Financial Services Marketing, 26(3), 145–158. https://doi.org/10.1057/s41264-021-00092-2 [Google Scholar] [Crossref]

50. Payne, J. W., Bettman, J. R., & Johnson, E. J. (1993). The adaptive decision maker. Cambridge University Press. https://doi.org/10.1017/CBO9781139173933 [Google Scholar] [Crossref]

51. Pompian, M. M. (2012). Behavioral finance and wealth management: How to build optimal portfolios that account for investor biases (2nd ed.). John Wiley & Sons. https://doi.org/10.1002/9781119202417 [Google Scholar] [Crossref]

52. Raisch, S., & Krakowski, S. (2021). Artificial Intelligence and Management: The Automation–Augmentation Paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072 [Google Scholar] [Crossref]

53. Raman, N. (2023). Explainable AI for safe AI in finance: Cognitive transparency, trust, and human-in-the-loop decision-making. ACM/IEEE Workshop on Explainable Artificial Intelligence in Finance (XAIFIN 2023). [Google Scholar] [Crossref]

54. Remund, D. L. (2010). Financial literacy explicated: The case for a clearer definition in an increasingly complex economy. The Journal of Consumer Affairs, 44(2), 276–295. https://doi.org/10.1111/j.1745-6606.2010.01169.x [Google Scholar] [Crossref]

55. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?": Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135–1144). Association for Computing Machinery. https://doi.org/10.1145/2939672.2939778 [Google Scholar] [Crossref]

56. Rooij Van, M., Lusardi, A., & Alessie, R. (2011). Financial literacy and stock market participation. Journal of Financial Economics, 101(2), 449–472. https://doi.org/10.1016/j.jfineco.2011.03.006 [Google Scholar] [Crossref]

57. Seeber, I., Bittner, E., Briggs, R. O., de Vreede, T., de Vreede, G. J., Elkins, A., Maier, R., Merz, A. B., Oeste-Reiß, S., Randrup, N., Schwabe, G., & Söllner, M. (2020). Machines as teammates: A research agenda on AI in team collaboration. Information & Management, 57(2), 103174. https://doi.org/10.1016/j.im.2019.103174 [Google Scholar] [Crossref]

58. Shefrin, H. (2002). Beyond greed and fear: Understanding behavioral finance and the psychology of investing. Oxford University Press. https://doi.org/10.1093/0195161211.001.0001 [Google Scholar] [Crossref]

59. Shrestha, Y. R., Ben-Menahem, S. M., & von Krogh, G. (2019). Organizational decision-making structures in the age of artificial intelligence. California Management Review, 61(4), 66–83. https://doi.org/10.1177/0008125619862257 [Google Scholar] [Crossref]

60. Sironi, P. (2016). FinTech innovation: From robo-advisors to goal-based investing and gamification. John Wiley & Sons. https://doi.org/10.1002/9781119226987 [Google Scholar] [Crossref]

61. Skitka, L. J., Mosier, K., & Burdick, M. D. (2000). Accountability and automation bias. International Journal of Human-Computer Studies, 52(4), 701–717. https://doi.org/10.1006/ijhc.1999.0349 [Google Scholar] [Crossref]

62. Smith, J., & Johnson, E. (2020). Artificial Intelligence Techniques for Credit Risk Assessment: A Review. International Journal of Intelligent Systems, 30(5), 789–805. [Google Scholar] [Crossref]

63. Tay, L. (2020). AI in personal finance: A comprehensive review of its applications and future. Journal of Digital Finance, 7(3), 45–62. [Google Scholar] [Crossref]

64. Thakor, A. V. (2020). Fintech and banking: What do we know? Journal of Financial Intermediation, 41, 100833. https://doi.org/10.1016/j.jfi.2019.100833 [Google Scholar] [Crossref]

65. Vassilakopoulou, P., Chaniotakis, G., & Reilly, P. (2023). Practicing human agency in AI-mediated environments. Journal of Information Technology, 38(3), 260–275. https://doi.org/10.1177/02683962231174983 [Google Scholar] [Crossref]

66. Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412 [Google Scholar] [Crossref]

67. Wang, D., Lim, J., Yang, F., & Sun, C. (2019). Designing explainable AI systems for human-AI collaboration. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1–12). Association for Computing Machinery. https://doi.org/10.1145/3290605.3300831 [Google Scholar] [Crossref]

68. Warschauer, T. (2002). The role of university financial planning programs. Financial Services Review, 11(4), 345–354. [Google Scholar] [Crossref]

69. Wilson, H. J., & Daugherty, P. R. (2018). Collaborative intelligence: Humans and AI are joining forces. Harvard Business Review, 96(4), 114–123. [Google Scholar] [Crossref]

70. Xiao, J. J. (2008). Applying behavior theories to financial behavior. In J. J. Xiao (Ed.), Handbook of consumer finance research (pp. 69–81). Springer. https://doi.org/10.1007/978-0-387-75734-6_5 [Google Scholar] [Crossref]

71. Xiao, J. J., & Porto, N. (2017). Financial Education and Financial Satisfaction: Financial Literacy, Behaviour and Capability as Mediators. International Journal of Bank Marketing, 35(5), 805–817. https://doi.org/10.1108/IJBM-01-2016-0009 [Google Scholar] [Crossref]

72. Xiao, J. J., & O’Neill, B. (2018). Prolonging financial literacy. Journal of Consumer Affairs, 52(1), 108–121. https://doi.org/10.1111/joca.12168 [Google Scholar] [Crossref]

73. Xu, X., Lu, Y., Vogel-Heuser, B., & Wang, L. (2021). Industry 4.0 and Industry 5.0—Inception, conception and perception. Journal of Manufacturing Systems, 61, 530–535. https://doi.org/10.1016/j.jmsy.2021.10.006 [Google Scholar] [Crossref]

• Empowering SME Performance Through Social Media: The Mediating Role of Entrepreneurial Orientation in Malaysian Manufacturing SMEs
• Effective School Bullying Prevention Strategies: A Thematic Review
• Impact of Workplace Conflict on the Operational Performance of Deposit Money Banks in Enugu State.
• Towards Digital Financial Solutions: A Conceptual Model of SME Strategies and Strategic Development
• Effect of Work-Life Balance and Affective Commitment on Employee Performance in the Hotel Industry in Plateau State