ARTIFICIAL INTELLIGENCE IN MEGAPROJECT FINANCIAL GOVERNANCE

Authors

DOI:

https://doi.org/10.18623/rvd.v23.6227

Keywords:

Artificial Intelligence, Infrastructure Finance, Mega Projects, Risk Monitoring, Predictive Analytics

Abstract

Mega infrastructure programs implemented under national economic transformation strategies such as Saudi Vision 2030 require advanced financial risk monitoring mechanisms capable of supporting real-time decision-making across complex project environments. This study proposes an artificial intelligence–enabled financial risk monitoring framework designed to enhance early warning capability through integration of ERP-based financial indicators and predictive analytics models. The framework strengthens institutional governance, transparency, and fiscal sustainability in infrastructure investment programs.

References

CHENG, M. Y., PENG, H. L., WU, Y. W., & CHEN, T. L. (2012). Prediction of project cash flow using evolutionary support vector machine inference model. Automation in Construction, 24, 12–21.

DOLOI, H. (2013). Cost overruns and failure in project management: Understanding the roles of key stakeholders in construction projects. Journal of Construction Engineering and Management, 139(3), 267–279.

ELGHAISH, F., ABRISHAMI, S., HOSSEINI, M. R., & ABU-SAMRA, S. (2020). Artificial intelligence applications in construction project finance: A review of trends and opportunities. Automation in Construction, 118, 103265.

FLYVBJERG, B. (Ed.). (2017). The Oxford handbook of megaproject management. Oxford University Press.

GRANLUND, M. (2011). Extending AIS research to management accounting and control issues: A research note. International Journal of Accounting Information Systems, 12(1), 3–19.

INTERNATIONAL MONETARY FUND. (2022). Digital transformation and fiscal transparency in public investment systems. IMF.

KIM, H., & HAN, K. (2020). Financial distress prediction for construction companies using decision tree and ensemble learning approaches. Journal of Construction Engineering and Management, 146(3), 04019111.

KSHTERI, N. (2018). Artificial intelligence in infrastructure governance and public-sector transparency. Government Information Quarterly, 35(2), 256–267.

LOVE, P. E. D., MATTHEWS, J., FANG, C., & LUO, C. (2021). Machine learning for project analytics in construction: Opportunities for Industry 4.0 integration. Engineering, Construction and Architectural Management, 28(2), 564–584.

MOON, S., CHI, Y., & KIM, J. (2019). Data-driven predictive models for project performance forecasting in construction engineering. Automation in Construction, 107, 102925.

ORGANISATION FOR ECONOMIC CO-OPERATION AND DEVELOPMENT. (2021). Getting infrastructure right: A framework for better governance. OECD Publishing.

ORGANISATION FOR ECONOMIC CO-OPERATION AND DEVELOPMENT. (2022). Infrastructure investment for sustainable development: Policy framework and governance practices. OECD Publishing.

SAUDI ARABIA. (2016). Vision 2030 Kingdom of Saudi Arabia. Government of Saudi Arabia.

UNITED NATIONS ENVIRONMENT PROGRAMME. (2021). Sustainable infrastructure and digital transformation policy outlook. UNEP.

VASARHELYI, M. A., ALLES, M. G., & WILLIAMS, K. T. (2010). Continuous assurance for the now economy. Accounting Horizons, 24(4), 653–663.

WORLD BANK. (2020). Infrastructure governance and public investment management. World Bank.

WORLD ECONOMIC FORUM. (2021). Artificial intelligence governance framework for infrastructure systems. World Economic Forum.

ZAVADSKAS, E. K., TURSKIS, Z., & TAMOŠAITIENĖ, J. (2010). Risk assessment of construction projects using multi-criteria decision-making techniques. Archives of Civil and Mechanical Engineering, 10(2), 33–46.

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Published

2026-05-20

How to Cite

Imiyage, D. P. (2026). ARTIFICIAL INTELLIGENCE IN MEGAPROJECT FINANCIAL GOVERNANCE. Veredas Do Direito, 23(8), e236227. https://doi.org/10.18623/rvd.v23.6227