Financial inclusion and social protection have been recognised as the primary essential stimuli from the potential they carry as avenues for economic development, especially with respect to reduction in poverty and inequalities, the creation of employment and the enhancement overall welfare and livelihood. However, inclusive access to financial resources and equitable access to social protection interventions have remained a significant concern in Nigeria. In addition, the emergence of the COVID-19 pandemic exposed the weakness of Nigeria in all sectors of the economy such as energy, health, education and food systems and low-level inclusive access to financial resources and social protection coverage. On the other hand, this study argues that financial inclusion and social protection has the potential to mitigation shocks orchestrated by the COVID-19 pandemic. This study empirically examines how social protection interventions and access to financial resources responded to COVID-19 pandemic. The study made use of data sourced from the World Bank’s COVID-19 national longitudinal phone survey 2020 and applied the logit regression. The findings show that social protection and access to financial resources significantly associated with the likelihood of shock mitigation during the COVID-19 pandemic. The results show that social protection intervention reduces the probability of being severely affected by shocks by 0.431. Given this result, the study recommends that the government should put more effort into proper social protection intervention to mitigate the effect of the COVID-19 pandemic.

This research aims to analyze the relationship between financial literacy variables and financial inclusion, the relationship between financial literacy variables and financial technology, and the relationship between financial technology variables and financial inclusion. The analysis of this research is to learn more about how financial literacy and the use of financial technology influence financial inclusion. This type of research is associative quantitative. Next, the relationship between these variables is explained using statistical formulas. Consequently, the term for this research is “quantitative research”. The study population is the number of people who use financial services. For this sampling, the purposive random sampling method was used. The following criteria are determined in sampling: 1) Minimum age 17 years, this is intended to take the minimum age standard in sampling and is considered capable of understanding the contents of the questionnaire statements. 2) Have ever used financial services. In this study, 11 question items were used to measure 3 variables, so this study used the largest range, namely 231 respondents. The intervention variable will be used as a reference for the Partial Least Square (PLS) method to analyze this research data. This study uses a causal model (causal modelling, relationships, and influence) or path analysis. The hypothesis that will be discussed in this research is tested using the Structural Equation Model (SEM), which is operated with Smart PLS. The results of this research show that financial literacy has a positive and significant impact on financial inclusion in society. Financial literacy has a positive and significant impact on financial technology. financial technology has a positive and significant impact on financial inclusion, financial technology can offset the impact of financial literacy on financial inclusion. The results of this research are used as input for the community so that they pay more attention to their internal human resources related to financial products that can be used for investment. With knowledge of the right financial products, it is hoped that they can create good financial behaviour so that an awareness of the importance of carrying out good financial planning. For financial institutions, it is hoped that this can increase easy access to financial products and services, in particular credit for businesses as additional capital for the community.

In order to meet the Sustainable Development Goals (SDGs) of the United Nations and address the growing global concern for ecologically responsible activities, this study examines the role that French financial institutions play in financing a green future and promoting sustainable development (SD). Through semi-structured interviews with twelve participants from banks and Fintech companies, the research investigates their familiarity with green financing commitments to international organizations and associations, their views on the growth potential of green finance, and the provision of green finance products. Additionally, it explores the connection between green finance and its positive influence on SD. Data analysis was performed using NVivo 12. The findings highlight a strong commitment to green finance and sustainable practices among these institutions, emphasizing the significance of integration and utilization of green finance products across various sectors. This research emphasizes the crucial role of financial institutions in France in driving a greener and more sustainable future through green finance.

This study thoroughly examined the use of different machine learning models to predict financial distress in Indonesian companies by utilizing the Financial Ratio dataset collected from the Indonesia Stock Exchange (IDX), which includes financial indicators from various companies across multiple industries spanning a decade. By partitioning the data into training and test sets and utilizing SMOTE and RUS approaches, the issue of class imbalances was effectively managed, guaranteeing the dependability and impartiality of the model’s training and assessment. Creating first models was crucial in establishing a benchmark for performance measurements. Various models, including Decision Trees, XGBoost, Random Forest, LSTM, and Support Vector Machine (SVM) were assessed. The ensemble models, including XGBoost and Random Forest, showed better performance when combined with SMOTE. The findings of this research validate the efficacy of ensemble methods in forecasting financial distress. Specifically, the XGBClassifier and Random Forest Classifier demonstrate dependable and resilient performance. The feature importance analysis revealed the significance of financial indicators. Interest_coverage and operating_margin, for instance, were crucial for the predictive capabilities of the models. Both companies and regulators can utilize the findings of this investigation. To forecast financial distress, the XGB classifier and the Random Forest classifier could be employed. In addition, it is important for them to take into account the interest coverage ratio and operating margin ratio, as these finansial ratios play a critical role in assessing their performance. The findings of this research confirm the effectiveness of ensemble methods in financial distress prediction. The XGBClassifier and RandomForestClassifier demonstrate reliable and robust performance. Feature importance analysis highlights the significance of financial indicators, such as interest coverage ratio and operating margin ratio, which are crucial to the predictive ability of the models. These findings can be utilized by companies and regulators to predict financial distress.

This study analysed the behaviour of both economic and financial profitability of credit unions belonging to segment 1 in Ecuador, as well as its determinants. For this purpose, data from the financial statements of a sample of 30 credit unions between 2016 and 2022 were used by means of a multiple linear regression methodology using panel data with fixed effects after applying the Hausman test. The findings of this research showed that current liquidity and non-performing loans have a negative and significant effect on both economic and financial profitability while the past due portfolio has a positive and significant impact on the generation of profitability of the financial institutions under study. In addition, it was revealed that the rate of outflow absorption has a negative relationship with economic profitability but a positive relationship with financial profitability. Unlike previous research in the Ecuadorian context, this research is pioneering in presenting results that indicate that the determinants traditionally considered for nonfinancial institutions and banks are also valid for credit unions, even though they are organisations with different characteristics from the rest.

India has experienced notable advancements in trade liberalization, innovation tactics, urbanization, financial expansion, and sophisticated economic development. Researchers are focusing more on how much energy consumption of both renewable and non-renewable accounts for overall system energy consumption in light of these dynamics. In order to gain an understanding of this important and contentious issue, we aim to examine the impact of trade openness, inventions, urbanization, financial expansion, economic development, and carbon emissions affected the usage of renewable and non-renewable energy (REU and N-REU) in India between 1980 and 2020. We apply the econometric approach involving unit root tests, FE-OLS, D-OLS, and FM-OLS, and a new Quantile Regression approach (QR). The empirical results demonstrate that trade openness, urbanization and CO₂ emissions are statistically significant and negatively linked with renewable energy utilization. In contrast, technological innovations, financial development, and economic development in India have become a source of increase in renewable energy utilization. Technological innovations were considered negatively and statistically significant in connection with non-renewable energy utilization, whereas the trade, urbanization, financial growth, economic growth, and carbon emissions have been established that positively and statistically significant influence non-renewable energy utilization. The empirical results of this study offer some policy recommendations. For instance, as financial markets are the primary drivers of economic growth and the renewable energy sector in India, they should be supported in order to reduce CO₂ emissions.