From Borrowing to Building: A Systematic Literature Review of Data-Driven Strategies for Cultivating Better Money Habits through Consumer Credit

Deborah O. Oyeyemi1, Abdoulkarim H. Moussa2, & Victor O. Abioye3
1Business Analytics and Information Management, University of Delaware, USA
2Business Administration, Temple University, USA
3French and Italian Studies, University of Maryland, USA
DOI – http://doi.org/10.37502/IJSMR.2025.81004

Abstract

The cultivation of sound money habits is central to individual financial wellbeing and broader economic resilience. In the digital era, consumer credit has evolved from traditional lending instruments into data-driven platforms that both enable and influence financial behavior. This systematic literature review synthesizes academic and industry research on how alternative data sources, machine learning techniques, and behavioral insights are integrated into credit systems to foster healthier money habits. Findings highlight three major domains: (1) the use of social, behavioral, and transactional data to expand financial inclusion and improve credit risk prediction; (2) the role of behavioral economics, including personality traits, self-control, and financial literacy, in shaping borrowing and repayment behaviors; and (3) the impact of information design, disclosure practices, and technological feedback mechanisms on financial literacy and long-term behavior change. While these innovations hold significant promise for more equitable credit access and personalized financial guidance, they also raise pressing concerns about data privacy, algorithmic bias, and the ethical use of consumer information. The review concludes that adaptive feedback loops and transparent AI systems represent the most promising avenues for sustainable habit formation, but achieving balance between innovation and consumer protection remains a critical policy and industry challenge.

Keywords: Consumer Credit; Data-Driven Finance; Financial Literacy; Behavioral Economics; Alternative Data; Machine Learning in Credit Scoring

References

  • Chioma Susan Nwaimo, Ayodeji Enoch Adegbola, & Mayokun Daniel Adegbola. (2024). Predictive analytics for financial inclusion: Using machine learning to improve credit access for under banked populations. In Computer Science & IT Research Journal (Vol. 5, Issue 6, pp. 1358–1373). Fair East Publishers. https://doi.org/10.51594/csitrj.v5i6.1201
  • (N.d.-a). https://www.semanticscholar.org/paper/51946068
  • Widagdo, B., & Roz, K. (2022). The role of personality traits, financial literacy and behavior on investment intentions and family support as a moderating variable. In Investment Management and Financial Innovations (Vol. 19, Issue 2, pp. 143–153). LLC CPC Business Perspectives. https://doi.org/10.21511/imfi.19(2).2022.12
  • Blanke, J. M. (2020). Protection for ‘Inferences Drawn’: A Comparison Between the General Data Protection Regulation and the California Consumer Privacy Act. In Global Privacy Law Review (Vol. 1, Issue Issue 2, pp. 81–92). Kluwer Law International BV. https://doi.org/10.54648/gplr2020080
  • Davis III, K. M., & Ruotsalo, T. (2025). Physiological Data: Challenges for Privacy and Ethics. In Computer (Vol. 58, Issue 1, pp. 33–44). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/mc.2024.3404994
  • Hershfield, H. E., Sussman, A. B., O’Brien, R. L., & Bryan, C. J. (2015). Leveraging Psychological Insights to Encourage the Responsible Use of Consumer Debt. In Perspectives on Psychological Science (Vol. 10, Issue 6, pp. 749–752). SAGE Publications. https://doi.org/10.1177/1745691615598514
  • Gurusamy, R., & B, A. (2023). Digital payment technology and consumer behaviour – Saving, spending patterns: Are saving and spending patterns a concern? In Journal of Information Technology Teaching Cases (Vol. 14, Issue 2, pp. 223–226). SAGE Publications. https://doi.org/10.1177/20438869231178846
  • Kamil, I., Ariani, M., & Irawan, I. A. (2024). The influence of lifestyle and financial literacy on online paylater system and its impact on spending behavior. In Journal of Economics and Business Letters (Vol. 4, Issue 2, pp. 51–62). Privietlab. https://doi.org/10.55942/jebl.v4i2.285
  • Zhao, H., Peng, H., & Li, W. (2022). Analysis of Factors Affecting Individuals’ Online Consumer Credit Behavior: Evidence From China. In Frontiers in Psychology (Vol. 13). Frontiers Media SA. https://doi.org/10.3389/fpsyg.2022.922571
  • Suhadolnik, N., Ueyama, J., & Da Silva, S. (2023). Machine Learning for Enhanced Credit Risk Assessment: An Empirical Approach. In Journal of Risk and Financial Management (Vol. 16, Issue 12, p. 496). MDPI AG. https://doi.org/10.3390/jrfm16120496
  • (N.d.-b). https://doi.org/10.12759/HSR.42.2017.1.52-76
  • Fetvadjiev, V. H., & He, J. (2019). The longitudinal links of personality traits, values, and well-being and self-esteem: A five-wave study of a nationally representative sample. In Journal of Personality and Social Psychology (Vol. 117, Issue 2, pp. 448–464). American Psychological Association (APA). https://doi.org/10.1037/pspp0000212
  • Tulin, M., Lancee, B., & Volker, B. (2018). Personality and Social Capital. In Social Psychology Quarterly (Vol. 81, Issue 4, pp. 295–318). SAGE Publications. https://doi.org/10.1177/0190272518804533
  • Hilbig, B. E., Glöckner, A., & Zettler, I. (2014). Personality and prosocial behavior: Linking basic traits and social value orientations. In Journal of Personality and Social Psychology (Vol. 107, Issue 3, pp. 529–539). American Psychological Association (APA). https://doi.org/10.1037/a0036074
  • Cole, C., Schwarzbach, C., & Weston, H. (2023). Limitations of german insurance disclosures to improve consumer understanding, with lessons for U.S. insurance practices. In K. McCullough (Ed.), Journal of Insurance Regulation. National Association of Insurance Commissioners. https://doi.org/10.52227/23497.2016
  • O’Rourke, D., & Ringer, A. (2015). The Impact of Sustainability Information on Consumer Decision Making. In Journal of Industrial Ecology (Vol. 20, Issue 4, pp. 882–892). Wiley. https://doi.org/10.1111/jiec.12310
  • Fong, J., & Hunter Antill, M. (2020). Can Facing the Truth Improve Outcomes? Effects of Information in Consumer Finance. In SSRN Electronic Journal. Elsevier BV. https://doi.org/10.2139/ssrn.3502442
  • Paul‐Ebhohimhen, V., & Avenell, A. (2007). Systematic review of the use of financial incentives in treatments for obesity and overweight. In Obesity Reviews (Vol. 9, Issue 4, pp. 355–367). Wiley. https://doi.org/10.1111/j.1467-789x.2007.00409.x
  • (N.d.-c). https://doi.org/10.1037//0022-006X.63.5.793
  • Redfern, J., Enright, G., Hyun, K., Raadsma, S., Allman-Farinell, M., Innes-Hughes, C., Khanal, S., Lukeis, S., Rissel, C., Chai, H. C., & Gyani, A. (2019). 4225Effectiveness of a behavioural incentive scheme linked to goal achievement in overweight children: a multicenter cluster randomized controlled trial. In European Heart Journal (Vol. 40, Issue Supplement_1). Oxford University Press (OUP). https://doi.org/10.1093/eurheartj/ehz745.0132
  • Moller, A. C., McFadden, H. G., Hedeker, D., & Spring, B. (2012). Financial Motivation Undermines Maintenance in an Intensive Diet and Activity Intervention. In Journal of Obesity (Vol. 2012, pp. 1–8). Wiley. https://doi.org/10.1155/2012/740519