The sustainability-linked discussion has gained international importance, with the banking sector being an essential pillar of the new economy, particularly through channeling financial resources to environmentally friendly economic activities. It is, however, still unclear if ESG is profitable, both for the economy and banks. This paper aims at filling this gap by presenting, from a macroeconomic perspective, the impact of ESG efforts and the banking sector’s contribution to a sustainable economy. Using panel regression models with fixed effects, the study investigates the impact of ESG factors and banking activity on economic growth. The results show a negative relationship between country-level ESG scores and economic growth, both in the short and long run, while increased financial intermediation by the banking sector, used as a proxy of potential green lending activity, does not necessarily enhance economic growth. Through delving into the interplay between the ESG score, economic development, and banking activity, this research could serve as a discussion point for economists, bankers, and policymakers when designing the economic and financial strategies for transitioning to a green economy.

The growing interest in ESG has increased the desire to understand how it influences financial decisions regarding capital structure. Using econometric techniques, the research analyzes how ESG factors influence financing structure, focusing on a sample of financial companies in Europe, North America and Asia. The results indicated that capital structure decisions are influenced by ESG, although there are differences across regions. In general, environmental and governance issues are positively correlated with leverage, emphasizing the idea of improved credit worthiness and better financing conditions. On the other hand, companies that are more socially focused rely more on equity due to the higher trust obtained from investors, lower risk perceptions and a strategic preference for conservatism. The findings highlight companies’ trade-offs when it comes to sustainability goals and financing structure, with ESG playing a dual role in market positioning and financial resilience. The current study contributes to the existing literature on sustainable finance by providing evidence-based information that ESG influences the capital structure of financial firms. It is particularly useful for investors, policy makers and financial institutions that strive to maintain a balance between sustainability and efficient capital management.

This paper evaluates the risk scoring performance of three main machine learning models: XGBoost, Random Forest and Support Vector Machines. Various evaluation metrics are used in this process: feature importance, predictive score distributions, and Receiver Operating Characteristic curves. When compared to other models, the results show that XGBoost is both more accurate and has a higher capability than others in distinguishing risk levels with different levels of prediction reliability. In addition, the interaction between loan amounts and scores obtained from prediction experiments was considered and more information were provided. The results demonstrate the benefits of boosting algorithms such as scoring tools for applications based on scoring and suggest which is the optimal models to use when modeling financial risk assessment.