Using Artificial Intelligence to Predict the Financial Gearing’s Capability to Achieve Financial Sustainability

The current study aims to use artificial intelligence to predict financial sustainability based on one of the most crucial artificial intelligence techniques, namely multi-layer neural networks, for a sample of commercial banks listed on the Iraqi Stock Exchange for the period (2013–2022). This is accomplished by adopting the gearing ratio (leverage) within the financial structure as a proxy for measuring financial gearing. Here, three indicators of the financial and economic dimension of financial sustainability were adopted: the market value added index, the earnings per share index, and the credit index, as a proxy for measuring financial sustainability. In order to achieve the goal of the study, the researcher relied on a main hypothesis, which is “the ability of artificial layered neural networks to predict financial sustainability through financial gearing ratios”. The study reveals several conclusions, the most important of which is the use of neural networks to predict the values of the growth rate in market value added, the earnings per share index, and the credit index. The study gave relatively accurate results, and the experimental results of the study varied among the three approved indicators of financial sustainability. The study also recommended, within the future studies section, the necessity of comparing the results of different models, as is the case in time series models, to determine the pros and cons of modern models, especially those relying on artificial intelligence.