Does AI private investment really matter for financial institutions efficiency? Evidence from a country panel

The main objective of this paper is to disclose the real impact that private AI investment has on different dimensions of financial institution efficiency index at a country level. The dataset analyzed corresponds to three dimensions that the International Monetary Fund uses to measure the financial institution's efficiency of a country. A total of twenty-four countries were analyzed between 2014 and 2021. The methodology employed in this paper is dynamic panel data which, after applying several tests, was the better option to treat the dataset. More specifically, the System-GMM estimator in two steps was applied to make a reliable statistical inference and ensure the robustness of the estimates. The results of the paper show a negative correlation between AI private investment and bank overhead costs, which denotes the positive effect that AI applications have in lessening financial institutions costs. The main contribution of this work is two-fold. Firstly, we have shown the positive effect between AI private investment and financial institution efficiency, reinforcing the current area of research in AI. Secondly, due to the novelty of the topic and the econometric approach, this study is of utmost importance nowadays and of high relevance for investors and the financial sector.