This study investigates the main financial technologies adopted by banks to improve their financial performance. The study population consists of commercial banks listed on the Amman Stock Exchange and Abu Dhabi Securities Exchange, and includes financial information and data from 2012 to 2020. A total of 115 questionnaires, consisting of five questionnaires for each bank, were distributed to the study population in Jordan and the United Arab Emirates. The dependent variable is financial performance, while the independent variable is financial technology (FinTech). Multiple linear regression analysis was conducted to test the hypotheses. The results showed that FinTech has a positive effect on both total deposit and net profits. This study recommends that banks be encouraged to adopt inclusive strategies to attain sustainable development.

This research will examine the impact of the adoption of financial technology on conventional banks’ financial performances. The research will place emphasis on the listed commercial banks at Amman Stock Exchange—ASE, using financial data for the period 2012–2020. The main study tool was a questionnaire that focuses on three main dimensions: financial inclusion—(FI), alternative payment methods—(APMs) and automation—(Auto). A total of 115 questionnaires were distributed to all commercial banks listed at Amman Stock Exchange—ASE. Multivariate regression analysis was employed to test the impact of the FinTech dimension as a proxy for independent variables on Jordanian commercial bank’s financial performance as a proxy for dependent variables. Based on the analysis results, the study concludes that all three FinTech dimensions: FI, APMs and Auto. reflected a positive significant impact on Jordanian commercial bank’s financial performance indicators (total deposit, total loans and net profit margin). Therefore, banks in general should invest more and more into financial technology tools and applications, in order to recruit potential clients and retain their current clients, to be able to sustain under fierce competition within the banking sector.

All economic sectors and individuals in Jordan rely mainly on banks to cover their shortage in money, thus banking sector plays a vital role in enhancing investments and economic development by standing in the middle between deficit units and surplus units. The major goal of this study is to identify what are the main drivers that impact lending behavior in Jordan. Using panel data and applying multi regression analysis on (13) Jordanian Conventional banks and two Islamic banks for the period (2000-2013) that are covered in this research, we found that lending behavior is statistically significantly affected by internal factors (DV, IR and net profit after tax) and it is also affected significantly by external factor (RR, GDP, IFR, OWDR and Red. R). Also the analysis indicated that OWDR and Red. R as a proxy for monetary policy did have a negative impact on lending behavior but not significantly proven. The study also reached to a conclusion that the amount of loans and advances extended by Jordanian banks is not affected by rate of interest. We recommend that Jordanian banks’ management should take into consideration internal specific factors as well as external specific factor with more care while formulating their lending policy, moreover central bank in cooperation with the Jordanian banking sector should work in more productive relationship in order to enhance more the economic growth.

Investor’s psychological behavior normally seek to invest in companies that are characterized by stable and positive dividend stream. Dividend policy is related to the decision of whether to distribute or not to distribute cash to shareholder. This type of decision is not taken in isolation from other related financial factors, as such decision is considered an integrated part of the company’s overall financial decisions. This study aims at investigating the apropos of accounting information indicators (financial indicators) and their role in determining cash dividend policy adopted by companies listed within the major sectors of Amman Stock exchange. Extracting the accounting information indicators pertaining to the three main sectors (Banking, Industrial and Services) of ASE, and by applying the simple linear regression statistical approach, the results indicated that the dividend policy adopted by the three sectors were mainly determined by accounting information indicators and that the impact of these indicators on cash dividend policy vary due to dissimilarity of the sectors’ nature, whereas the results pointed out that different indicator affect different sector, which means that the impact of the accounting information is not identical on cash dividend policy decision.

Predicting credit risk is vital for banks as it safeguards financial stability, minimizes default losses, optimizes capital, and ensures regulatory compliance. This study aims to predict credit risk (High/Low) in commercial banks by integrating machine learning with traditional econometric approaches. The Structural Learning in Vague Environments (SLAVE) fuzzy rule-based model handles ambiguity in financial decisions, while the eXtreme Gradient Boosting (XGBoost) uncovers non-linear patterns among predictors. Input variables—profitability, liquidity risk, ESG (environmental, social, and governance) score, and monetary freedom—were selected via multicollinearity tests and three panel regression models, including ordinary least squares (OLS), fixed effects, and random effects models. The empirical investigation uses a panel dataset of forty commercial banks across seven Middle Eastern countries from 2014 to 2023, yielding 400 observations. Regression results reveal that profitability and ESG score significantly reduce credit risk. Liquidity risk and monetary freedom increase credit risk. XGBoost combined with the SHapley Additive exPlanations (SHAP)-based interpretation identifies ESG Score as the most influential predictor. The SLAVE model was evaluated using three data splits: 70/30, 80/20, and 90/10. The 80/20 split achieved the highest accuracy, with superior performance in identifying low-risk banks. Stronger ESG performance and stable monetary environments contribute to fostering sustainable banking and reducing credit risk, making these indicators valuable for risk management frameworks in the Middle Eastern banking sector.