Artificial Intelligence Expert System and the Financial Performance of Deposit Money Banks (DMBs) in Nigeria
Abstract
This study investigated the effect of Artificial Intelligence Expert System in on the Financial Performance of Deposit Money Banks in Nigeria. The study analysed the secondary data of selected DMBs for the period 2015 -2023 (9 years). The data were sourced from the Annual Reports of the DMBs, the Central Bank of Nigeria (CBN) Statistical Bulletin and World Development Indicators to establish cause-effect relationships between the variables. Population of the study was the 27 DMBs in Nigeria as at 31st July, 2023, while the sample size was five (5) DMBs (Access Bank, Zenith Bank, UBA, First Bank and GT Bank). The sampling technique used was the non-probability convenience sampling method chosen based on the availability of the financial statements of the DMBs for the period under study. The study employed Error Correction Model (ECM) for time series regression to analyse equilibrium relationships in short run and long run behaviours. The findings showed that the resultant coefficients were positive and significant both during pre-Expert System adoption (coefficient = 1.25668<0.05) and 1.75328, p<0.05 for post-Expert System adoption respectively. The null hypothesis was therefore rejected and we accepted the alternate hypothesis to conclude that the deployment of AI Expert System impacted positively and significantly on the financial performance of DMBs in Nigeria. Based on the findings, we recommend that there should be strategic and realistic investment in AI Expert Systems by the DMBs to improve their financial performance.
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