Artificial Intelligence as a Catalyst for Enhancing Financial Reporting Quality of Listed Deposit Money Banks in Nigeria - Ibrahim, Jimoh; Olowookere, Johnson Kolawole (Lembaga Penerbit Fakultas Ekonomi dan Bisns, Universitas Trisakti, 2026)
Factors such as inadequate information, inaccurate accounting estimates, recurring crises, and corporate financial scandals often arise from weak managerial judgment. This study therefore examines the extent to which Artificial Intelligence (AI) influences the financial reporting quality (FRQ) of listed deposit money banks (DMBs) in Osun State, Nigeria. Specifically, it investigates how expert systems, machine learning, and neural networks affect the FRQ of these banks. The study employed a cross sectional survey design, distributing questionnaires to 151 out of 243 employees across 10 selected DMBs. Partial Least Squares Structural Equation Modeling (PLS SEM) was used to analyze the data. The results reveal that AI—proxied by expert systems, machine learning, and neural networks—has a significant and positive effect on the FRQ of the selected banks. The study concludes that AI applications substantially enhance the efficiency of financial reporting processes and improve the overall financial reporting quality of listed banks in Nigeria. The study is anchored on Grand Theory, which explains how decision making processes can be enhanced through the use of AI tools. The theory supports the notion that AI enables more informed decisions by analyzing large volumes of data. Practically, the findings suggest that banks can leverage AI solutions to improve the quality and timeliness of financial reporting, thereby enhancing operational efficiency and reducing errors