Granger Causality on Digital Capital Expenditure, Bank Size and Credit Risk Management of Deposit Money Banks in Nigeria

Authors

ONYEBUENYI, Ikenna Charles

Department of Finance, Babcock University, Ilishan Remo-Ogun State (Nigeria)

OGBOI, Charles

Department of Finance, Babcock University, Ilishan Remo-Ogun State (Nigeria)

OMOSEBI, Tolulope Ruth

Department of Finance, Babcock University, Ilishan Remo-Ogun State (Nigeria)

Article Information

DOI: 10.47772/IJRISS.2026.10200368

Subject Category: FINANCE

Volume/Issue: 10/2 | Page No: 4976-4987

Publication Timeline

Submitted: 2026-02-22

Accepted: 2026-02-27

Published: 2026-03-11

Abstract

In the rapidly evolving global financial ecosystem, banking institutions are increasingly integrating advanced technological infrastructures into their core operational frameworks. While digital capital expenditure can influence financial stability, the precise direction and strength of this relationship in the Nigerian context remains under-researched and poorly understood. This study sought to investigate the causal relationship between digital capital expenditure and the credit risk management of Nigerian deposit money banks, considering the role of bank size. This study employed correlation analysis and pairwise Granger causality techniques. Correlation analysis is essential for understanding the strength and direction of the linear relationship between digital investments and credit risk indicators. Pairwise Granger causality, on the other hand, allows for testing the temporal direction of influence between variables, thereby addressing the question of whether changes in digital infrastructure investments “Granger-cause” changes in credit risk outcomes, or vice versa. This study utilized secondary data sourced from the published audited annual reports and financial statements of 12 selected deposit money banks in Nigeria over a 10-year period (2015–2024). A panel data causality analysis was employed to examine the directional effect of digital capital expenditure on credit risk metrics. Granger causality test results revealed that digital capital expenditure significantly influences the non-performing loan ratio (F = 4.1721, p = 0.0185), but there is no reverse causality (F = 0.1884, p = 0.8286). Furthermore, digital capital expenditure significantly impacts the capital adequacy ratio (F = 8.6627, p = 0.0004), while the capital adequacy ratio does not influence digital capital expenditure (F = 0.6098, p = 0.5457). Bank size significantly affects capital adequacy (F = 4.1809, p = 0.0183), but the reverse does not hold (F = 0.8170, p = 0.4450). The study concluded that digital capital expenditure and bank size have strong predictive power over credit risk management behavior, while the reverse does not hold. It is therefore recommended that bank management proactively prioritize investments in digital infrastructure. Since digital capital expenditure has a unidirectional impact on improving asset quality and solvency, institutions must treat these investments as a core strategic risk management tool rather than mere operational costs to enhance overall financial stability.

Keywords

In the rapidly evolving global financial ecosystem

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References

1. Adeagbo, O., Eniya, J. O., Ojeogwu, C. I., Uzuh, S. A., & Okoh, J. I. (2024). Digital banking and risk exposure in Access Bank Plc, Lagos State, Nigeria: Imperative for management. African Banking and Finance Review Journal. [Google Scholar] [Crossref]

2. Akhter, N. (2023). Determinants of commercial bank’s non-performing loans in Bangladesh: An empirical evidence. Cogent Economics & Finance, 11(1), Article 2194128. [Google Scholar] [Crossref]

3. Ally, O. J., Kulindwa, Y. J., & Mataba, L. (2025). Financial technology and credit risk management: the case of non-performing loans in Tanzanian banks. Cogent Economics & Finance, 13(1), Article 2459188. [Google Scholar] [Crossref]

4. Aminipour, A., Bagheri, A., Kordlouie, H., & Houshmand, M. (2024). Testing the impact of banking financial technology on risk management based on the dynamic panel data approach for the group of banks and financial institutions admitted to the Tehran Stock Exchange. Agricultural Marketing and Commercialization, 8(2). [Google Scholar] [Crossref]

5. Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277-297. [Google Scholar] [Crossref]

6. Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68(1), 29-51. [Google Scholar] [Crossref]

7. Babarinde, G. F. (2023). Impact of digital finance on banks’ credit allocation in Nigeria. Journal of Public Administration, Finance and Law, 29, 69–80. [Google Scholar] [Crossref]

8. Begimkulov, E. (2024). The empirical measurement of competition and digitalization for the banking sector of Kyrgyzstan: Impacts of efficiency, profitability, and stability. Journal of Eastern European and Central Asian Research, 11(5), Article 1614. [Google Scholar] [Crossref]

9. Blundell, R., & Bond, S. (2023). Initial conditions and Blundell–Bond estimators. Journal of Econometrics, 234, 101–110. [Google Scholar] [Crossref]

10. Central Bank of Nigeria. (2024). e-Payment Statistics (2018–2024). CBN. [Google Scholar] [Crossref]

11. Chen, Q., & Shen, C. (2024). How FinTech affects bank systemic risk: Evidence from China. Journal of Financial Services Research, 65(1), 77–101. [Google Scholar] [Crossref]

12. Chen, S., Li, Y., Wang, J., & Wu, F. (2023). Does digital transformation reduce bank risk? Evidence from China. International Review of Economics & Finance, 88, 134–147. [Google Scholar] [Crossref]

13. d’Avernas, A., Eisfeldt, A. L., & Huang, C. (2024). The deposit business at large vs. small banks (FDIC Center for Financial Research Working Paper No. 2024-02). [Google Scholar] [Crossref]

14. Do, T. D., Pham, H. A. T., Thalassinos, E. I., & Le, H. A. (2022). The impact of digital transformation on performance: Evidence from Vietnamese commercial banks by different sizes. Journal of Risk and Financial Management, 15(1), 21. [Google Scholar] [Crossref]

15. EFInA. (2018). Improving digitised microcredit in Nigeria – A use case for credit bureau and collateral registry. Enhancing Financial Innovation & Access. [Google Scholar] [Crossref]

16. Girma, A. G., & Huseynov, F. (2024). The causal relationship between FinTech, financial inclusion, and income inequality in African economies. Journal of Risk and Financial Management, 17(1), 2. [Google Scholar] [Crossref]

17. Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424–438. [Google Scholar] [Crossref]

18. Gujarati, D. N., & Porter, D. C. (2020). Basic Econometrics. McGraw-Hill. [Google Scholar] [Crossref]

19. Iwedi, M. (2024). Digital banking technology and the operational efficiency of banks in Nigeria. African Banking and Finance Review Journal. [Google Scholar] [Crossref]

20. Metawa, N., Itani, R., Metawa, S., & Elgayar, A. (2023). The impact of digitalization on credit risk: The mediating role of financial inclusion (National Bank of Egypt (NBE) case study). Economic Research-Ekonomska Istraživanja, 36(2), Article 2178018. [Google Scholar] [Crossref]

21. Moloi, T., & Iredele, O. O. (2019). Risk management in the digital era: The case of Nigerian banks. In A. Tjoa, L. M. Camarinha-Matos, & D. H. Afsarmanesh (Eds.), Digital transformation in business and society (pp. 229–246). Springer. [Google Scholar] [Crossref]

22. Natufe, O. K., & Evbayiro-Osagie, E. I. (2023). Credit risk management and the financial performance of deposit money banks: Some new evidence. Journal of Risk and Financial Management, 16(7), 302. [Google Scholar] [Crossref]

23. Nguyen, T. L., Tran, S., & Ho, T. (2022). Fintech credit, bank regulations and bank performance: A cross-country analysis. Asia-Pacific Journal of Business Administration, 14(4), 534–551. [Google Scholar] [Crossref]

24. Nigeria Inter-Bank Settlement System. (2024). Industry e-payment statistics and reports. NIBSS. [Google Scholar] [Crossref]

25. Ochenge, R. O. (2023). The effect of FinTech development on bank risktaking: Evidence from Kenya (KBA Centre for Research on Financial Markets and Policy Working Paper Series No. 72). Kenya Bankers Association. [Google Scholar] [Crossref]

26. Ololade, B. M., Salawu, R. O., & Olatunji, O. O. (2023). Risk management and performance of deposit money banks in Nigeria: A re-examination. Banks and Bank Systems, 18(2), 113–126. [Google Scholar] [Crossref]

27. Stock, J. H., & Watson, M. W. (2019). Introduction to Econometrics. Pearson. [Google Scholar] [Crossref]

28. Wang, D., Liu, Y., Xu, Y., & Liu, L. (2022). Fintech, macroprudential supervision and systematic risk in China's banks. China Finance and Economic Review, 11(1), 69–90. [Google Scholar] [Crossref]

29. Xu, Y., Abdul-Mohsin, A., & Yang, F. (2025). Market concentration, digital transformation, and bank credit risk in China: evidence from GMM estimation. Humanities and Social Sciences Communications, 12(1), Article 990. [Google Scholar] [Crossref]

30. Yang, F., & Masron, T. A. (2024). The effect of digital transformation on the cost of China commercial banks. SN Business & Economics, 4(4), 48. [Google Scholar] [Crossref]

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