Multidisciplinary Subjects
Macroeconomic volatility and the value of AI analytics in Nigerian digital lending
1 Post Graduate School. Lagos State University, Ojo-Lagos, Nigeria
Abstract
Artificial intelligence can help digital lenders assess borrowers, but its value for credit risk management may vary with economic conditions. This study examines whether perceived macroeconomic volatility weakens the relationship between AI-driven predictive analytics and credit risk management performance in Nigeria. Drawing on contingency theory, it analyses survey responses from 185 digital lending professionals in Lagos State using partial least squares structural equation modelling with 5,000 bootstrap resamples. AI analytics was positively associated with perceived performance (β = 0.382, p < 0.001), while its interaction with macroeconomic volatility was negative and statistically significant (β = −0.163, p = 0.006; f² = 0.063). Alternative data utilization and regulatory compliance also showed positive associations with performance. Together, the predictors explained 68.4 percent of the variance in the outcome. The findings suggest that the relationship between analytics and risk management performance becomes weaker as perceived economic instability increases. They support a contingency perspective in which the benefits of technology depend partly on its operating environment. For digital lenders, the findings highlight the importance of model validation, borrower affordability and timely adjustments to lending policy.
Keywords: Artificial intelligence; digital lending; macroeconomic volatility; credit risk management; contingency theory; Nigeria
References
- Berg, T., Burg, V., Gombović, A., & Puri, M. (2020). On the rise of FinTechs: Credit scoring using digital footprints. The Review of Financial Studies, 33(7), 2845–2897. https://doi.org/10.1093/rfs/hhz099
- Donaldson, L. (2001). The contingency theory of organizations. SAGE Publications.
- Fuster, A., Goldsmith-Pinkham, P., Ramadorai, T., & Walther, A. (2022). Predictably unequal? The effects of machine learning on credit markets. The Journal of Finance, 77(1), 5–47. https://doi.org/10.1111/jofi.13090
- Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. (2014). A survey on concept drift adaptation. ACM Computing Surveys, 46(4), Article 44. https://doi.org/10.1145/2523813
- Gambacorta, L., Huang, Y., Qiu, H., & Wang, J. (2019). How do machine learning and non-traditional data affect credit scoring? New evidence from a Chinese fintech firm (BIS Working Papers No. 834). Bank for International Settlements. https://www.bis.org/publ/work834.pdf
- Gambacorta, L., Huang, Y., Qiu, H., & Wang, J. (2024). How do machine learning and non-traditional data affect credit scoring? New evidence from a Chinese fintech firm. Journal of Financial Stability, 73, Article 101284. https://doi.org/10.1016/j.jfs.2024.101284
- Gambacorta, L., Sabatini, F., & Schiaffi, S. (2025). Artificial intelligence and relationship lending (BIS Working Papers No. 1244). Bank for International Settlements. https://www.bis.org/publ/work1244.htm
- Gupta, M., & George, J. F. (2016). Toward the development of a big data analytics capability. Information & Management, 53(8), 1049–1064. https://doi.org/10.1016/j.im.2016.07.004
- Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
- Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115–135. https://doi.org/10.1007/s11747-014-0403-8
- International Monetary Fund. (2025). Nigeria: 2025 Article IV consultation—Press release; staff report; and statement by the Executive Director for Nigeria (IMF Country Report No. 25/157). https://www.imf.org/en/publications/cr/issues/2025/07/01/nigeria-2025-article-iv-consultation-press-release-staff-report-and-statement-by-the-568220
- Jagtiani, J., & Lemieux, C. (2019). The roles of alternative data and machine learning in fintech lending: Evidence from the LendingClub consumer platform. Financial Management, 48(4), 1009–1029. https://doi.org/10.1111/fima.12295
- Kock, N., & Hadaya, P. (2018). Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods. Information Systems Journal, 28(1), 227–261. https://doi.org/10.1111/isj.12131
- Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
- Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322–2347. https://doi.org/10.1108/EJM-02-2019-0189
- Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533. https://doi.org/10.1002/(SICI)1097-0266(199708)18:73.0.CO;2-Z