Bojuwon Mustapha, ADUWO Ayomikun Elizabeth, ADEBAYO Adeosun Isaac, OLASEHINDE Sunday Adeniyi, ADUWO Olola Olayeye, ADEKANMI Aderemi Daniel, OWONIYA Babatunde, DINATU Nna Alabadan, AJEWOLE Alaba Sunday and AKANDEBOJUWON Ayoka Latifat
Adv. Artif. Intell. Mach. Learn., XX (XX):-
1. Bojuwon Mustapha: Department of Accounting, Faculty of Management Science,Federal University Oye-Ekiti
2. ADUWO Ayomikun Elizabeth: Federal Polytechnic Orogun, Delta State, Nigeria
3. ADEBAYO Adeosun Isaac: Department of Accounting, Faculty of Management Science, Federal University Oye-Ekiti Nigeria
4. OLASEHINDE Sunday Adeniyi: Department of Business Administration, Faculty of Management Science, Federal University Nigeria.
5. ADUWO Olola Olayeye: Olusegun Agagu University of Science and Technology Okitipupa Ondo State Nigeria
6. ADEKANMI Aderemi Daniel: Faculty of Management Science, Federal University Oye-Ekiti Nigeria.
7. OWONIYA Babatunde: Faculty of Management Science, Federal University Oye-Ekiti Nigeria.
8. DINATU Nna Alabadan: Department of Accounting, Faculty of Management Science, Federal University Oye-Ekiti Nigeria.
9. AJEWOLE Alaba Sunday: Internal Audit Directorate, Federal University Oye-Ekiti Nigeria.
10. AKANDEBOJUWON Ayoka Latifat: Independent National Electoral Commission, Ondo State Nigeria.
DOI: 10.54364/AAIML.2026.62285
Article History: Received on: 24-Dec-25, Accepted on: 01-Feb-26, Published on: 07-Mar-26
Corresponding Author: Bojuwon Mustapha
Email: bojuwon.mustapha@fuoye.edu.ng
Citation: BOJUWON Mustapha et al. Artificial Intelligence and Corporate Earning Management. Advances in Artificial Intelligence and Machine Learning. 2026. (Ahead of Print). https://dx.doi.org/10.54364/AAIML.2026.62285
The strengthening
and revolutionized
market surveillance through
artificial intelligence to shrivel the
stock market
rapidly becoming
ubiquitous
to detect
and predict
corporate earnings
management. This
study explores
the impact
of artificial
intelligence
on corporate
earnings management
using a
quantitative
research
design. Data
were collected
from 145
small and
medium-sized manufacturing
enterprises in
Lagos State,
Nigeria, comprising
food production
(n=113) and beverage production
(n=38) firms,
representing an 80.5% response
rate. The paper employs
Partial Least
Squares Structural
Equation Modelling
(PLS-SEM) with
a bootstrapping technique
(5000 replicate
samples) to analyse the
causal relationships
between constructs.
The findings show that all five
hypotheses are highly supported. This is because: the integration of artificial
intelligence with the existing system portrays the highest positive correlation
coefficient value with the corporate earning management, r = 0.392, t = 15.054,
p < 0.001. This is followed by the challenges in the regulation of the use
of artificial intelligence, r = 0.346, t = 15.926, p < 0.001. This study
also found that the acceptance level by the users, the cost-effectiveness, as
well as the consideration of the ethical aspect of the use of the AI,
contribute less significantly to the corporate earning management, r = 0.315, t
= 16.368, p < 0.001, r = 0.164, t = 5.637, p < 0.001, and r = 0.104, t =
4.526, p < 0.001, respectively. Hence, the model achieved excellent
explanatory values, which stood at 99%, given that the value of R2 =
0.990. This reveals that 99% of the variability that exists in corporate
earnings management can be predicted by its five variables. In this, the values
of Cronbach's exceeded 0.784 for all the variables, which reveals that each has
exhibited appropriate values for the level of error. In the study, the values
of the AVE stood beyond 0.609, which indicates that the variables possessed
appropriate values for the level of error. These results indicate that
artificial intelligence can substantially enhance the accuracy, reliability,
and timeliness of financial reporting practices and render the ethical and
regulatory considerations to be part of its responsible use.