Effects of Artificial Intelligence on the Accuracy and Efficiency of Budget Forecasting of the Ministry of Finance of Ethiopia
Abstract:
Accurate and efficient budget forecasting is essential for effective public financial management and evidence based decision making. This study examined the effects of Artificial Intelligence on the accuracy and efficiency of budget forecasting of the Ministry of Finance of Ethiopia. A mixed research design was employed. The data was collected from 181 employees using a structured questionnaire and 15 respondents via semi structured interviews and open-ended responses. The data was analyzed using descriptive statistics and regression analysis. The findings revealed that Artificial Intelligence significantly improves forecasting efficiency by reducing forecasting time, enabling rapid analysis of budget scenarios and supporting improved decision making. However, Artificial Intelligence had a limited effect on forecasting accuracy compared to its strong impact on efficiency. Although some improvements in accuracy were observed, challenges such as poor data quality, limited technical skills, inadequate training and low confidence in Artificial Intelligence outputs continue to affect forecasting performance. The qualitative findings supported the quantitative results indicating that employees recognize Artificial Intelligence as useful for improving efficiency but believe that further improvements are required before substantial gains in forecasting accuracy can be achieved. The study implied that Artificial Intelligence has strong potential to improve budget forecasting of the Ministry of Finance of Ethiopia by enhancing efficiency and supporting evidence based decision making. However, achieving significant improvements in forecasting accuracy requires cleaner data, continuous staff training, improved technological infrastructure and stronger leadership support for Artificial Intelligence adoption.References:
[1].
S, Norvig P. Artificial intelligence: a
modern approach. 4th ed. Hoboken (NJ): Pearson; 2021.
[2].
Maslej N, Fattorini
L, Perrault R, et al. The AI index report 2024. Stanford (CA):
Stanford Institute for Human Centered Artificial Intelligence; 2024. Available
from: https://aiindex.stanford.edu/report/
[3].
U.S. Department of the Treasury.
Artificial intelligence in tax administration: compliance and fraud detection
systems. Washington (DC): U.S. Department of the Treasury; 2024.
[4].
UK Parliament. The role of artificial
intelligence in tax administration and public expenditure control. London (UK):
UK Parliament; 2025.
[5].
Ministry of Economy and Finance, Republic
of Korea. dBrain+: next-generation budget and accounting system - AI-based
fiscal management. Open Fiscal Data Portal. Sejong (South Korea): Ministry of
Economy and Finance; 2026. Available from: https://www.openfiscaldata.go.kr
[6].
Reserve Bank of India. Report on AI
applications in public financial management. Mumbai (India): Reserve Bank of
India; 2025.
[7].
World Bank. Brazil: enhancing public
sector performance: strengthening public financial and procurement management.
Washington (DC): World Bank; 2024. Available from: https://documents.worldbank.org/
[8].
Kenya Revenue Authority. Annual report and
financial statements 2023. Nairobi (Kenya): Kenya Revenue Authority; 2023.
[9].
International Monetary Fund. Fiscal
monitor: digitalization and public finances. Washington (DC): International
Monetary Fund; 2023. Available from: https://www.imf.org/en/Publications/FM
[10]. African
Development Bank. Digital transformation of public procurement in Ghana:
AI-powered analytics for enhanced governance. Abidjan (Côte d'Ivoire): African
Development Bank; 2023.
[11]. Center
for Global Development. Machine learning and social protection: lessons from
Togo's NOVISSI program. Washington (DC): Center for Global Development; 2023.
Available from: https://www.cgdev.org/
[12]. Ministry
of Innovation and Technology. Digital Ethiopia 2025 strategy: progress report.
Addis Ababa (Ethiopia): Ministry of Innovation and Technology; 2025.
[13]. World
Bank. Ethiopia public finance review: towards more transparent and accountable
public finance. Washington (DC): World Bank; 2022. Available from: https://documents.worldbank.org/
[14]. Davis
FD. Perceived usefulness, perceived ease of use, and user acceptance of
information technology. MIS Q. 1989;13(3):319-40.
[15]. Rogers
EM. Diffusion of innovations. 5th ed. New York (NY): Free Press; 2003.
[16]. Office
of the Auditor General. Annual audit report on the state of federal government
finance. Addis Ababa (Ethiopia): Office of the Auditor General; 2022.
[17]. Andrews
M, Pritchett L, Woolcock M. Building state capability: evidence, analysis,
action. Oxford (UK): Oxford University Press; 2014.
[18]. Manda
MI, Backhouse J. Smart governance for inclusive socio-economic transformation
in South Africa: are we there yet? In: Pieterse E, Van Donk M, editors. Public
administration and information technology. Cham (Switzerland): Springer; 2019.
p. 87-107.
[19]. Achieng
M, Mutua J. Institutional readiness and the localization of AI in African
public sectors. Gov Inf Q. 2023;40(1):101776. Available from: https://doi.org/10.1016/j.giq.2022.101776
[20]. Australian
Taxation Office. AFCX data-matching program overview (Gazette Notice
C2025G00386). Canberra (Australia): Australian Taxation Office; 2025. Available
from: https://www.ato.gov.au
[21]. Department
of Finance Canada. Spring Economic Update 2026: economic and fiscal
projections. Ottawa (Canada): Department of Finance Canada; 2026. Available
from: https://www.canada.ca/en/department-finance.html
[22]. International
Monetary Fund. Harnessing emerging digital technologies toward a new frontier
of public financial management. Technical Notes and Manuals. Washington (DC):
International Monetary Fund; 2026. No. 2026/006. Available from: https://www.imf.org/en/Publications/TNM
[23]. Bethelhem
K. Assessment on the challenges and prospects of integrated financial
management information system implementation in Ethiopia [master's thesis].
Addis Ababa (Ethiopia): Addis Ababa University; 2020.
[24]. Creswell
JW, Plano Clark VL. Designing and conducting mixed methods research. 3rd ed.
Thousand Oaks (CA): SAGE Publications; 2018.
[25]. Johnson
RB, Onwuegbuzie AJ. Mixed methods research: a research paradigm whose time has
come. Educ Res. 2004;33(7):14-26.
[26]. Saunders
MNK, Lewis P. Research methods for business students. 8th ed. London (UK):
Pearson; 2019.
[27]. Cohen
L, Manion L, Morrison K. Research methods in education. 5th ed. London (UK):
RoutledgeFalmer; 2003.
[28]. Tavakol
M, Dennick R. Making sense of Cronbach's alpha. Int J Med Educ. 2011;2:53-5.
[29]. Nunnally
JC. Psychometric theory. 2nd ed. New York (NY): McGraw-Hill; 1978.
[30]. Lincoln
YS, Guba EG. Naturalistic inquiry. Beverly Hills (CA): SAGE Publications; 1985.
[31]. Field
A. Discovering statistics using IBM SPSS statistics. 5th ed. Thousand Oaks
(CA): SAGE Publications; 2018.
[32]. Holmes
W, Bialik M, Fadel C. Artificial intelligence in education: promises and
implications for teaching and learning. Boston (MA): Center for Curriculum
Redesign; 2019.
[33]. Osei-Mensah B, Asiamah EO, Sackey R. Strategic communication and artificial intelligence: reviewing emerging innovations and future directions. Arch Bus Res. 2023;11(1):85-102.
