The assessment of total risk-weighted assets (LTRWAs) in the banking sector is of the utmost importance. It serves as a critical component for regulatory compliance, risk management, and capital adequacy. By accurately assessing LTRWAs, banks can effectively meet regulatory requirements, efficiently allocate capital resources, and proactively manage risks. Moreover, the accurate assessment of LTRWAs supports performance evaluation and fosters investor confidence in the financial stability of banks. This study presents statistical analyses and machine learning methods to identify factors influencing LTRWAs. Data from Bahrain, Jordan, Qatar, the United Arab Emirates, and Yemen, spanning from 2010 to 2021, was utilized. Various statistical tests and models, including ordinary least squares, fixed effect, random effect, correlation, variance inflation factor, tolerance tests, and fintech models, were conducted. The results indicated significant impacts of the unemployment rate, inflation rate, natural logarithm of the loan-to-asset ratio, and natural logarithm of total assets on LTRWAs in regression models. The dataset was divided into a training group (90% of the data) and a testing group (10% of the data) to evaluate the predictive capabilities of various fintech models, including an adaptive network-based fuzzy inference system (ANFIS), a hybrid neural fuzzy inference system (HyFIS), a fuzzy system with the heuristic gradient descent (FS.HGD), and fuzzy inference rules with the descent method (FIR.DM) models. The selection of the optimal model is contingent upon assessing its performance according to specific error criteria. The HyFIS model outperformed others with lower errors in predicting LTRWAs. Independent t-tests confirmed statistically significant differences between original and predicted LTRWA for all models, with HyFIS showing closer predictions. This study provides valuable insights into LTRWA prediction using advanced statistical and machine learning techniques, based on a dataset from multiple countries and years.

American scientific publishing group

Aim: This study examines Arab and international bank profitability—Return on Assets (ROA) and Return on Equity (ROE)—determinants. It highlights regional performance differences and the main financial drivers of profitability, focusing on macroeconomic shocks. The paper compares Arab and international bank performance and provides new profitability driver insights. It also shows that linear modelling is insufficient to describe ROE in Arab countries and that nonlinear modelling is needed. The discussion suggests ways policymakers and banks can boost profitability and financial resilience.Method: The paper uses “multiple linear regression (MLR)” with panel data from heterogeneous countries over several years. Interest income, capital adequacy, non-interest income, cost-to-income ratio, loan-to-deposit ratio, and non-performing loans are tested for their effect on ROA and ROE in the MLR specifications. Time and regional trends are tested to account for economic crises like the 2008 global financial crisis (GFC) and the 2020 COVID-19 pandemic.Results: Findings exhibit very high regional variation in bank profitability. African countries—e.g., Botswana, Ethiopia, and Malawi—ranked better than European and Asian counterparts on both ROA and ROE. Arab countries such as Iraq, Syria, and Saudi Arabia exhibited high ROA, while Yemen, Egypt, and Djibouti showed high ROE. The research could not find a valid linear regression model for the ROE in the Arab nations, implying complex, non-linear dynamics. The major determinants of profitability are interest income, capital adequacy, non-interest income, and cost-to-income ratio. The macroeconomic shocks also tended to decrease profitability significantly by region.

American scientific publishing group