Transforming financial reporting and auditing through artificial intelligence: A Zimbabwean institutional perspective

https://doi.org/10.35912/ijfam.v7i3.3207
Published: Dec 8, 2025

Abstract:

Purpose: This study investigates the potential of Artificial Intelligence (AI) to enhance financial reporting and auditing practices in Zimbabwean institutions amid economic volatility and increasing transparency demands.

Research Methodology: The primary aim is to evaluate how AI-driven tools can improve the accuracy and reliability of financial information in both public and private sectors. Utilizing a mixed-methods approach, the research combines structured surveys of accounting professionals with in-depth interviews of stakeholders, including regulators and IT experts. Quantitative data were analyzed using descriptive statistics and regression models, while qualitative insights provided a deeper understanding of institutional readiness and implementation barriers.

Results: The findings indicate that AI adoption in Zimbabwe is still in its early stages, with growing awareness of its benefits, such as automation and predictive analytics. However, challenges like limited digital infrastructure, high costs, skill shortages, and regulatory uncertainty impede widespread adoption.

Conclusions: The study concludes that while AI holds transformative potential for financial reporting and auditing, a strategic and phased approach is crucial for successful integration.

Limitations: Include a small sample size in certain sectors and reliance on self-reported data, which may introduce bias.

Contributions: Despite these challenges, the research contributes significantly to the literature on AI in accounting in emerging economies, offering policy recommendations and practical frameworks to assist Zimbabwean institutions in leveraging AI for improved financial governance and oversight.

Keywords:
1. Artificial Intelligence
2. Auditing
3. Digital Transformation
4. Financial Reporting
5. Institutional Readiness
Authors:
1 . Kudakwashe Munyepwa
Manicaland State University of Applied Sciences, Zimbabwe
2 . Charity Ranganayi
Manicaland State University of Applied Sciences, Zimbabwe
3 . Liberty Mudzengerere
Manicaland State University of Applied Sciences, Zimbabwe
4 . Noah Mutongereni
Manicaland State University of Applied Sciences, Zimbabwe
5 . Norah Chishamiso Gwesu
Manicaland State University of Applied Sciences, Zimbabwe
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How to Cite
Munyepwa, K., Ranganayi, C., Mudzengerere, L., Mutongereni, N. ., & Gwesu, N. C. (2025). Transforming financial reporting and auditing through artificial intelligence: A Zimbabwean institutional perspective. International Journal of Financial, Accounting, and Management, 7(3), 503–517. https://doi.org/10.35912/ijfam.v7i3.3207
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Issue & Section
Vol. 7 No. 3 (2025): December
Articles
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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author Biographies

Charity Ranganayi, Manicaland State University of Applied Sciences, Zimbabwe

Lecturer

Applied Accounting 

Liberty Mudzengerere, Manicaland State University of Applied Sciences, Zimbabwe

Lecturer

Applied Accounting Sciences

Noah Mutongereni, Manicaland State University of Applied Sciences, Zimbabwe

Director 

Quality Assurance Services

Norah Chishamiso Gwesu, Manicaland State University of Applied Sciences, Zimbabwe

Lecturer

Applied Accounting 

References

    Appelbaum, D., Kogan, A., & Vasarhelyi, M. A. (2017). Big data and analytics in the modern audit engagement: Research needs. Auditing: A Journal of Practice & Theory, 36(4), 1-27. doi:https://doi.org/10.2308/ajpt-51684

    Chadha, P., Gera, R., Khera, G., & Sharma, M. (2023). Challenges of Artificial Intelligence Adoption for Financial Inclusion Artificial Intelligence, Fintech, and Financial Inclusion (1st ed., pp. 135-160). Boca Raton: CRC Press.

    Chilunjika, S. R., & Chilunjika, A. (2024). Artificial Intelligence and Human Resource Management in Zimbabwe’s Public Health Sector: Opportunities and Challenges. African Journal of Development Studies, 14(4).

    Dako, O. F., Onalaja, T. A., Nwachukwu, P. S., Bankole, F. A., & Lateefat, T. (2020). Big data analytics improving audit quality, providing deeper financial insights, and strengthening compliance reliability. J Front Multidiscip Res, 1(2), 64-80. doi:https://doi.org/10.54660/.jfmr.2020.1.2.64-80

    Deloitte. (2020). AI in audit: The future of audit is intelligent. Retrieved from https://www2.deloitte.com/

    DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American sociological review, 48(2), 147-160. doi:https://doi.org/10.4324/9781315247533-34

    Ikponmwoba, S. O., Chima, O. K., Ezeilo, O. J., Ojonugwa, B. M., Ochefu, A., & Adesuyi, M. O. (2020). Conceptual Framework for Improving Bank Reconciliation Accuracy Using Intelligent Audit Controls. Journal of Frontiers in Multidisciplinary Research, 1(1), 57-70. doi:https://doi.org/10.54660/.IJFMR.2020.1.1.57-70

    Ismail, M. K. A. H., Rajiun, A. A. M. A., Shahnaz, S., Kamal, B. M., Azmidal, N. I., Nizam, N. I. S., . . . Deraman, N. A. (2024). AI-Powered Internal Auditing: Transforming the Profession for a New Era. International Journal of Research and Innovation in Social Science, 8(10), 2406-2413. doi:https://doi.org/10.47772/ijriss.2024.8100199

    Katekwe, P. (2025). A Conceptual Approach to Change Management in the Adoption of Artificial Intelligence for Records Management in Zimbabwe's Public Sector Artificial Intelligence in Records and Information Management (pp. 153-180): IGI Global Scientific Publishing.

    Kokina, J., Blanchette, S., Davenport, T. H., & Pachamanova, D. (2025). Challenges and opportunities for artificial intelligence in auditing: Evidence from the field. International Journal of Accounting Information Systems, 56. doi:https://doi.org/10.1016/j.accinf.2025.100734

    Kour, M., & Schutte, D. P. (2024). Artificial Intelligence and Accounting: Ethical, Legal and Social Implications (1 ed.). London: Routledge.

    Leocádio, D., Malheiro, L., & Reis, J. (2024). Artificial intelligence in auditing: A conceptual framework for auditing practices. Administrative Sciences, 14(10), 1-16. doi:https://doi.org/10.3390/admsci14100238

    Maguraushe, K., & Matanda, J. M. (2024). Leveraging Artificial Intelligence for Enhanced Credit Risk Management: Case of Zimbabwe’s Commercial Banks Sustainable Finance and Business in Sub-Saharan Africa (pp. 295-311): Springer.

    Maicon, R. M. (2023). Adapting change management strategies for the AI Era: Lessons from large-scale IT integrations. World Journal of Advanced Research and Reviews, 19(3), 1604-1629. doi:https://doi.org/10.30574/wjarr.2023.19.3.1556

    Mangwanya, M. G. (2025). Barriers to Digital Transformation In Zimbabwean Local Governments. Journal of Economic and Social Development, 12(2), 12-21.

    Maphosa, V. (2024). An overview of cybersecurity in Zimbabwe’s financial services sector. F1000Research, 12, 1251. doi:https://doi.org/10.12688/f1000research.132823.2

    Matenda, F. R., Sibanda, M., Chikodza, E., & Gumbo, V. (2023). Default prediction for audited and unaudited private firms under economic and financial stress: evidence from Zimbabwe. Afro-Asian Journal of Finance and Accounting, 13(1), 85-124. doi:https://doi.org/10.1504/AAJFA.2023.128617

    Mheuka, T. R. J. (2024). Assessing the Influence of Internal Audit on Financial and Operational Performance: A Case of Pharmaceuticals in Zimbabwe. Available at SSRN 5283296. doi:https://dx.doi.org/10.2139/ssrn.5283296

    Mienye, I. D., Sun, Y., & Ileberi, E. (2024). Artificial intelligence and sustainable development in Africa: A comprehensive review. Machine Learning with Applications, 18. doi:https://doi.org/10.1016/j.mlwa.2024.100591

    Mpofu, F. Y. (2024). Prospects, Challenges, And Implications Of Deploying Artificial Intelligence In Tax Administration In Developing Countries. Studia Universitatis Babes Bolyai-Negotia, 69(3), 39-78. doi:https://doi.org/10.24193/subbnegotia.2024.3.03

    Shava, E., & Mhlanga, D. (2023). Mitigating bureaucratic inefficiencies through blockchain technology in Africa. Frontiers in Blockchain, 6, 1-11. doi:https://doi.org/10.3389/fbloc.2023.1053555

    Thakkar, H., Fanuel, G. C., Datta, S., Bhadra, P., & Dabhade, S. B. (2025). Optimizing Internal Audit Practices for Combatting Occupational Fraud: A Study of Data Analytic Tool Integration in Zimbabwean Listed Companies. International Research Journal of Multidisciplinary Scope, 6(1), 22-36. doi:https://doi.org/10.47857/irjms.2025.v06i01.02164

    ul Haq, F., Suki, N. M., Zaigham, H., Masood, A., & Rajput, A. (2025). Exploring AI adoption and SME performance in resource-constrained environments: A TOE–RBV perspective with mediation and moderation effects. Journal of Digital Economy. doi:https://doi.org/10.1016/j.jdec.2025.07.002

    Yoon, K., Hoogduin, L., & Zhang, L. (2015). Big data as complementary audit evidence. Accounting horizons, 29(2), 431-438. doi:https://doi.org/10.2308/acch-51076

    Zakaria, N. H., Hassan, R., Othman, M. R., Zakaria, Z., & Kasim, S. (2017). A review on classification of the urban poverty using the artificial intelligence method. Journal of Asian Scientific Research, 7(11), 450. doi:https://doi.org/10.18488/journal.2.2017.711.450.458

  1. Appelbaum, D., Kogan, A., & Vasarhelyi, M. A. (2017). Big data and analytics in the modern audit engagement: Research needs. Auditing: A Journal of Practice & Theory, 36(4), 1-27. doi:https://doi.org/10.2308/ajpt-51684
  2. Chadha, P., Gera, R., Khera, G., & Sharma, M. (2023). Challenges of Artificial Intelligence Adoption for Financial Inclusion Artificial Intelligence, Fintech, and Financial Inclusion (1st ed., pp. 135-160). Boca Raton: CRC Press.
  3. Chilunjika, S. R., & Chilunjika, A. (2024). Artificial Intelligence and Human Resource Management in Zimbabwe’s Public Health Sector: Opportunities and Challenges. African Journal of Development Studies, 14(4).
  4. Dako, O. F., Onalaja, T. A., Nwachukwu, P. S., Bankole, F. A., & Lateefat, T. (2020). Big data analytics improving audit quality, providing deeper financial insights, and strengthening compliance reliability. J Front Multidiscip Res, 1(2), 64-80. doi:https://doi.org/10.54660/.jfmr.2020.1.2.64-80
  5. Deloitte. (2020). AI in audit: The future of audit is intelligent. Retrieved from https://www2.deloitte.com/
  6. DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American sociological review, 48(2), 147-160. doi:https://doi.org/10.4324/9781315247533-34
  7. Ikponmwoba, S. O., Chima, O. K., Ezeilo, O. J., Ojonugwa, B. M., Ochefu, A., & Adesuyi, M. O. (2020). Conceptual Framework for Improving Bank Reconciliation Accuracy Using Intelligent Audit Controls. Journal of Frontiers in Multidisciplinary Research, 1(1), 57-70. doi:https://doi.org/10.54660/.IJFMR.2020.1.1.57-70
  8. Ismail, M. K. A. H., Rajiun, A. A. M. A., Shahnaz, S., Kamal, B. M., Azmidal, N. I., Nizam, N. I. S., . . . Deraman, N. A. (2024). AI-Powered Internal Auditing: Transforming the Profession for a New Era. International Journal of Research and Innovation in Social Science, 8(10), 2406-2413. doi:https://doi.org/10.47772/ijriss.2024.8100199
  9. Katekwe, P. (2025). A Conceptual Approach to Change Management in the Adoption of Artificial Intelligence for Records Management in Zimbabwe's Public Sector Artificial Intelligence in Records and Information Management (pp. 153-180): IGI Global Scientific Publishing.
  10. Kokina, J., Blanchette, S., Davenport, T. H., & Pachamanova, D. (2025). Challenges and opportunities for artificial intelligence in auditing: Evidence from the field. International Journal of Accounting Information Systems, 56. doi:https://doi.org/10.1016/j.accinf.2025.100734
  11. Kour, M., & Schutte, D. P. (2024). Artificial Intelligence and Accounting: Ethical, Legal and Social Implications (1 ed.). London: Routledge.
  12. Leocádio, D., Malheiro, L., & Reis, J. (2024). Artificial intelligence in auditing: A conceptual framework for auditing practices. Administrative Sciences, 14(10), 1-16. doi:https://doi.org/10.3390/admsci14100238
  13. Maguraushe, K., & Matanda, J. M. (2024). Leveraging Artificial Intelligence for Enhanced Credit Risk Management: Case of Zimbabwe’s Commercial Banks Sustainable Finance and Business in Sub-Saharan Africa (pp. 295-311): Springer.
  14. Maicon, R. M. (2023). Adapting change management strategies for the AI Era: Lessons from large-scale IT integrations. World Journal of Advanced Research and Reviews, 19(3), 1604-1629. doi:https://doi.org/10.30574/wjarr.2023.19.3.1556
  15. Mangwanya, M. G. (2025). Barriers to Digital Transformation In Zimbabwean Local Governments. Journal of Economic and Social Development, 12(2), 12-21.
  16. Maphosa, V. (2024). An overview of cybersecurity in Zimbabwe’s financial services sector. F1000Research, 12, 1251. doi:https://doi.org/10.12688/f1000research.132823.2
  17. Matenda, F. R., Sibanda, M., Chikodza, E., & Gumbo, V. (2023). Default prediction for audited and unaudited private firms under economic and financial stress: evidence from Zimbabwe. Afro-Asian Journal of Finance and Accounting, 13(1), 85-124. doi:https://doi.org/10.1504/AAJFA.2023.128617
  18. Mheuka, T. R. J. (2024). Assessing the Influence of Internal Audit on Financial and Operational Performance: A Case of Pharmaceuticals in Zimbabwe. Available at SSRN 5283296. doi:https://dx.doi.org/10.2139/ssrn.5283296
  19. Mienye, I. D., Sun, Y., & Ileberi, E. (2024). Artificial intelligence and sustainable development in Africa: A comprehensive review. Machine Learning with Applications, 18. doi:https://doi.org/10.1016/j.mlwa.2024.100591
  20. Mpofu, F. Y. (2024). Prospects, Challenges, And Implications Of Deploying Artificial Intelligence In Tax Administration In Developing Countries. Studia Universitatis Babes Bolyai-Negotia, 69(3), 39-78. doi:https://doi.org/10.24193/subbnegotia.2024.3.03
  21. Shava, E., & Mhlanga, D. (2023). Mitigating bureaucratic inefficiencies through blockchain technology in Africa. Frontiers in Blockchain, 6, 1-11. doi:https://doi.org/10.3389/fbloc.2023.1053555
  22. Thakkar, H., Fanuel, G. C., Datta, S., Bhadra, P., & Dabhade, S. B. (2025). Optimizing Internal Audit Practices for Combatting Occupational Fraud: A Study of Data Analytic Tool Integration in Zimbabwean Listed Companies. International Research Journal of Multidisciplinary Scope, 6(1), 22-36. doi:https://doi.org/10.47857/irjms.2025.v06i01.02164
  23. ul Haq, F., Suki, N. M., Zaigham, H., Masood, A., & Rajput, A. (2025). Exploring AI adoption and SME performance in resource-constrained environments: A TOE–RBV perspective with mediation and moderation effects. Journal of Digital Economy. doi:https://doi.org/10.1016/j.jdec.2025.07.002
  24. Yoon, K., Hoogduin, L., & Zhang, L. (2015). Big data as complementary audit evidence. Accounting horizons, 29(2), 431-438. doi:https://doi.org/10.2308/acch-51076
  25. Zakaria, N. H., Hassan, R., Othman, M. R., Zakaria, Z., & Kasim, S. (2017). A review on classification of the urban poverty using the artificial intelligence method. Journal of Asian Scientific Research, 7(11), 450. doi:https://doi.org/10.18488/journal.2.2017.711.450.458