AI-Supported Financial Analysis and Iraqi Bank Performance Evaluation - Ammar Ghazi Ibrahim Al-Ezzi (Journal of Accounting Science, 2026)
General Background: The rapid digital transformation of the banking sector has increased the need for advanced tools to enhance financial performance evaluation. Specific Background: In the Iraqi banking industry, traditional financial analysis methods remain dominant despite growing interest in artificial intelligence (AI)-driven approaches. Knowledge Gap: Limited empirical evidence exists on the effectiveness of AI-supported financial analysis in improving accuracy and predictive capability within developing banking contexts. Objective: This study aims to assess the impact of AI-driven financial analysis on evaluating and predicting the financial performance of Iraqi banks. Methods: Using a descriptive–analytical approach, the study examines key financial indicators—capital adequacy, profitability, liquidity, activity, and leverage—applied to a sample of Iraqi banks based on 2023 financial statements, and employs a linear regression model as an AI technique for prediction. Results: The findings reveal a close alignment between actual and predicted values, indicating that AI-assisted analysis provides acceptable predictive accuracy while enhancing evaluation efficiency. Novelty: The study integrates traditional financial indicators with AI-based modeling in the Iraqi banking context. Implications: The results highlight the importance of adopting AI in financial analysis systems to improve decision-making quality and align with contemporary digital transformation trends.