MACROECONOMIC DETERMINANTS OF BANKING PROFITABILITY IN ROMANIA: EMPIRICAL EVIDENCE AND STRATEGIC IMPLICATIONS FOR AI ADOPTION
Daniela Iulia Maria Cărbune
”Eugeniu Carada” Doctoral School of Economic Sciences, Faculty of Economics and Business Administration, University of Craiova, Craiova, Romania carbune.daniela.n9d@student.ucv.ro
Abstract: This study examines the impact of key macroeconomic indicators on banking profitability in Romania, with the aim of establishing an empirical foundation for future research on the strategic adoption of artificial intelligence in the banking sector. Using annual data for the period 2010-2024, the analysis considers GDP growth, inflation rate and unemployment rate as determinants of profitability, measured through Return on Assets (ROA) and Return on Equity (ROE). The methodology employs multiple linear regression estimated in EViews, with HAC, robust standard errors to ensure coefficient reliability. The results indicate that the unemployment rate is the most significant macroeconomic factor negatively influencing profitability, while GDP growth and inflation show weak and statistically insignificant effects. These findings suggest that profitability in the Romanian banking sector is highly sensitive to labor market conditions, highlighting the need for enhanced credit risk prediction, portfolio monitoring and strategic decision- making tools. In this context, the research opens a relevant direction for integrating artificial intelligence models to improve forecasting accuracy, optimize asset management and strengthen institutional resilience under changing macroeconomic conditions. Furthermore, the study underscores that traditional profitability drivers, such as macroeconomic fluctuations, provide only a partial explanation of performance outcomes, reinforcing the importance of incorporating bank-specific indicators and advanced digital technologies in future analyses. By establishing a data-driven baseline of how external economic conditions affect profitability, the research creates a foundation for subsequent investigations into the role of AI-based risk assessment, default prediction and operational optimization tools, which may significantly enhance the ability of banks to anticipate economic stress, improve capital allocation and support more resilient financial strategies.
Keywords: Banking Profitability; Multiple Linear Regression; Macroeconomic Indicators; Artificial Intelligence Models
JEL classification: G21; E44; C01
