Nexus among financial inclusion and sustainability in Asia: role of banking sector

Abstract

Promoting shared prosperity, economic success, and the expansion of the financial sector to individual development supported by financial inclusion to reduce poverty. Financial inclusion in Asian countries is generally considered relatively low. Thus, the purpose of this study is to explore the connection between the accessibility of financial services and the dependability of the financial sector. To accomplish the study purpose, data is incorporated from financial institutions based in Asia from 2008 to 2017. The generalized moment approach (GMM) was used to analyze the data. The study findings indicated that a more equitable distribution of wealth might be achieved by expanding access to financial services and strengthening the banking system’s capacity to weather shocks. Financial institutions might benefit from integration into the financial system by increasing profits, decreasing operational expenses, and increasing market share. Several directions for further research have been proposed based on the results of this work.

Introduction

Many nations realize that expanding access to financial services is crucial to fostering long-term growth that can support (Kim et al. 2017). The term “financial inclusion” refers to the availability and use of formal banking services, both of which are necessary for individuals and enterprises to be called “financially included” (Chiu and Lee 2020; Hasan and Liu 2022). More people will be able to utilize the services and products supplied by financial institutions if more people are encouraged to join formal financial institutions. This includes people who need to settle payments for money transfers, save more, and apply for insurance and credit. One way to achieve this goal is to expand access to conventional banking services among active market participants. Families and enterprises can establish a tax-saving shield to assist soft usage, conserve revenue impulses and retain a transaction history to obtain loans more quickly. These gains will materialize because inclusion will make it easier for individuals and businesses to set up a tax shelter that will aid in utilizing softer resources. Fund initiatives that increase the economy’s physical and human capital require a more efficient pooling of the economy’s resources inside the financial sector, which is what financial inclusion is all about. Project funding is a viable option for achieving this goal.

The rapid rise of Asia’s economy to global dominance over the last several years has captured the attention of everyone. From 1970 to 2016, the Asian continent’s GDP grew twice as fast as the world’s industrialized nations (Fang et al. 2022). The absolute poverty rate in Asia decreased dramatically during the 1990s due to the region’s robust economic development. Some analysts predict that by 2040, Asia will produce half of the global GDP (Pan et al. 2023). The growth projection holds that Asia will be the economic and financial center in the future (Wu et al. 2022).

Asia has the youngest population on the planet, and nearly one-third, 1.6 billion, of the world’s internet population resides in Asia because of China’s and India’s enormous population contribution (Ali et al. 2022; La Rovere et al. 2018). Over the last decade, Asian financial institutions have been the most successful in the world (Ali et al. 2019). In 2018, their pretax profit of USD 700 billion accounted for 37% of the total profit made by the worldwide banking sector (Ali et al. 2019).

There has been remarkable economic development in the Asian region, and as a result, financial inclusion has been a significant focus for policymakers, industry leaders, and academics (Iram et al. 2020; Ahmad et al. 2022). This is because everyone stands to gain from widespread access to financial services. Most people agree that increased access to the financial system is a powerful weapon in the fight against poverty and narrowing the wealth gap. One possible explanation for this shift in emphasis is the growth of local banking options. The term “financial inclusion” is often used to describe the effort to increase the availability of banking and other financial services to those living in low-income and underprivileged areas.

Increasing access to financial services might have unforeseen repercussions for many players and parts of the economy. Studies have mainly concentrated on the effects of expanding access to financial services on economic development, poverty reduction, and income distribution thus far. Examination of the relevant literature will show that most of the statistics go in the direction that inclusion decreases financial limitations for both people and enterprises, increases income and aids in economic growth, and lowers poverty rates; however, research has not been done on how increasing access to banking services might affect the overall health of national financial markets.

One of the primary focuses of the G-20 and the World Bank since 2010 has been increasing access to financial services as a means of reducing poverty in nations still classified as developing or underdeveloped. Economic participation is a crucial factor in the fight to end extreme poverty. High-impact multidimensional interventions, financial inclusion, investment in human development objectives, and an employment-led growth strategy are the four pillars of human development identified by the United Nations Development Programme (UNDP 2016). First, an employment-led growth plan is developed; second, financial inclusion is enhanced; third, investments are made in human development priorities; and fourth, high-impact multidimensional interventions are carried out. Expanded financial inclusion may threaten financial stability in several ways, so looking at them is an excellent way to explore new areas.

Suppose the banking system is protected by the expansion of its deposit base and the diversification of its loan portfolio due to the inclusion of previously excluded agents. In that case, this move will be beneficial. In order to lessen their reliance on high-risk forms of external borrowing, banks in developing countries may benefit from a rise in the number of local depositors. On the other hand, if new depositors are more likely to remove their funds during rough times, then expansion might be harmful to stability. This may occur if new depositors tend to cash out their funds quickly. However, data on savings deposits support the need for increased steadiness (Lahiani et al. 2021). The process of financial inclusion may have more potential for unfavorable implications on the stability of banks in the widening loan eligibility standards, according to articles from 2018 by Umair and Dilanchiev (2022)and Xiuzhen et al. (2022). This may lead to softer underwriting standards, faster credit growth, more consumer and commercial debt, a higher likelihood of business failure, and lower profits for financial institutions. The importance of sluggish credit expansion in deciding whether or not financial crises may be described as “credit booms gone bad” is a common conclusion drawn from research into the phenomenon of financial collapse. Recent events have emphasized these risks, including defaults in the US subprime mortgage business that contributed to the global financial crisis and the severity of the crisis in Greece that stemmed from the country’s fast economic development and high level of debt. Natural catastrophes, like the 2011 Japanese earthquake that killed more than 15,000 people and triggered a recession that lasted for years in that country, are another recent example that illustrates these risks (Mselmi et al. 2019).

The phrase “financial inclusion” is often used in the context of the financial sector to describe the rapid pace of growth and the introduction of new technology solutions. The introduction of new monetary systems and commodities is two such instances. The expansion of financial services is a critical component of Asia’s ongoing economic growth, and the banking industry is a crucial engine in this regard. Banks play a crucial role as go-betweens for clients and other financial organizations. By using the ecosystem, banks must boost their engagement with current customers and attract a broader range of new ones. Nonetheless, a sizable portion of the population still needs to gain entry to and cannot correctly use established forms of financial support. Particularly affected are the economically poor and those living in less accessible areas of the country. Financial inclusion might link with environmental changes as climate changes are an undeniable fact of the recent era (Ullah et al. 2020). Also, sustainable development and future energy exploration is another essential part of the financial inclusion and environmental aspect of sustainability (Li and Umair 2023).

The purpose of this study is to analyze the relationship between financial inclusion and the financial performance of banks by using data collected from several institutions. Much recent focus has been on the viability and success of Asia’s banking sector. The data used in this analysis comes from 3071 institutions from a wide range of Asian countries. This paper provides an analysis of the years 2008 through 2017.

Asia is one of the most dynamic economic regions in the world; thus, the first step in any thorough investigation into the region is to utilize the most recent data presently available at the bank level. Our findings will undoubtedly have far-reaching implications for the governments of neighboring nations. Second, we have considered vital aspects of financial inclusion in our empirical study. Indicators of the macroeconomic climate and the characteristics of individual banks are two examples of these factors. Third, we use both the generalized method of moments (GMM) strategy and ordinary least squares (OLS) regressions to verify the precision of our findings. In this way, researchers can be sure that the results are reliable. Since this is the case, potential endogeneity problems with panel data may be avoided.

Literature review

The widespread availability of financial services and sustained economic expansion is a topic of debate between the two schools of thought. There are good arguments to be made for each of these perspectives. The preliminary studies support the hypothesis that expanding access to banking and other financial services may reinforce and promote economic stability. The second piece of evidence comes from studies conducted to disprove the idea that the broader availability of financial services will increase market volatility. Each of these specialized academic journals will be introduced and summarized below.

An empirical literature review conducted by Islam et al. (2013) found that the relationship between inclusion, economic growth, and inequality is poorly understood. The best strategy thus far has been to increase people’s ability to open savings accounts and make digital payments (Hussain et al. 2018; Abid 2017). Increasing people’s access to credit, on the other hand, may have very modest impacts on alleviating poverty and reducing inequality. Liu et al. (2023) show that microcredits were disappointing even once the borrowers’ achievements were accounted for and that there is no way to minimize the significance of these results. Iram et al. (2020) suggested that people who have access to a wide range of financial services are more likely to save money and make better use of their savings. The impact of capital available on the long-term viability of Armenia’s small and medium-sized enterprises (SMEs) was studied by Jalil and Feridun (2011). Other indicators of the bank’s overall financial health were evaluated in addition to the z-score and nonperforming loan (NPL) percentage (Ikram et al. 2019b).

In conclusion, financial inclusion had a positive and substantial effect on lending to SMEs; the authors employed an unbalanced panel data system-GMM dynamic panel estimate for 2005–2011.

Abbas et al. (2022) conducted research in 2019 to learn more about the state of the banking industry and how easy access to banking services affects its overall health. Data from 2635 banks across 86 countries were analyzed for the years 2004 and 2012. Instead of using a more dynamic risk or instability evaluation approach, several financial institutions’ z-scores were employed. The study concluded that if capital is raised, deposits will grow, reducing the marginal cost of banking. However, in a financially accessible market, asymmetric information about risky borrowers may lead to a rise in the number of loans that are not returned as agreed upon.

Additionally, the research employed demographic and regional data as replacements for financial services. Their investigation uncovered data that lent credence to the idea that more accessible access to banking services is associated with more stable financial organizations.

According to Zhang et al. (2021), an inclusive financial system may increase low-interest deposits, which can help banks save money on their marginal funding costs and provide them with more bargaining leverage in the money market. If banks establish an inclusive financial system to increase low-interest deposits, these potential advantages of financial inclusion might be realized. This would happen if more people have access to financial services due to the positive effect of financial inclusion.

Xia et al. (2020) concluded that financial inclusion is a formidable tool that may help banks maintain the security of their lending system. Iqbal et al. (2019) examined 189 countries between 2004 and 2017, which reveals that countries with higher degrees of financial inclusion are less likely to be negatively impacted by significant drops in lending and borrowing. Mohsin et al. (2020b) provided empirical support to the idea that a robust financial inclusion process is linked to a stable financial market during times of crisis. The correlation between the two variables serves as empirical evidence.

Iqbal et al. (2022) proposed enhancing the financial infrastructure and introducing mobile banking to provide access to financial services for more people. These advancements in technology allow countries without the necessary physical and financial infrastructure to permit the transmission of financial information to consumers who are not currently being serviced. This creates whole new commercial possibilities for those countries. Agyekum et al. (2021) looked into the growth of financial education programs in India. They found that customers in rural areas are deterred from using digital and more advanced financial services due to a lack of access to physical and digital banking facilities, low financial literacy, low income, and no formal employment opportunities. Due to the abovementioned concerns, customers in rural areas are discouraged from using digital and complex financial services.

Internet banking is quickly replacing ATMs as the preferred means of transferring large sums of money because of its inexpensive transaction fees and convenient accessibility. Substituting online banking for the actual exchange of cash might be a step toward a cashless society.

On the other side, Lee et al. (2020) found a bidirectional connection between the two ideas, concluding that long-term social participation increases as one’s financial situation improves. In a study, Ali et al. (2019) looked at the impact of financial inclusion on poverty, economic inequality, and the security of national economies in Tunisia. Given their high rates of economic development and generally stable financial systems, the countries that make up this region provide an interesting case study. However, the area’s population is quite heterogeneous and dispersed across a broad geographical range. The provided evidence indicates that greater access to financial services is associated with greater economic security and lower income inequality. This line of thinking holds that societal factors are crucial in determining whether or not a person will have access to conventional banking services.

The social structure plays a crucial role in determining which population members have access to formal financial services. Financial inclusion helps close the income gap by giving low-income households a reason to strive toward financial security. Additionally, low-income communities actively seek and use available financial resources to improve their economic status (Zhang 2021). Different levels of financial inclusion exist between China and the BRICS (Brazil, Russia, India, China, and South Africa) nations, and Fernandes et al. (2018) validated the role of demographic characteristics (such as education, age, and income) in this regard. The disparity may be explained by the fact that China has a lower degree of financial inclusion than the other BRICS nations.

It required a more extensive study looking at how many people in China and the other BRICS (Brazil, Russia, India, and China) countries have bank accounts and what variables contribute to that level of financial inclusion. Other factors, such as GDP per capita and governance indices, have been proven relevant in predicting financial inclusion and depth among countries; however, credit information and enforcement are even more critical. Credit data and its enforcement have a more significant bearing on explaining wealth than any other factor.

Lamperti et al. (2021) found that the percentage of businesses experiencing difficulties accessing finance was lower in countries with higher rates of inclusion. This conclusion agrees with what we know about our monetary resources. Li and Luo (2019) show that increased financial inclusion helps stabilize consumption by making it less vulnerable to output swings. Higher financial inclusion rates have been found to have this impact by easing consumer spending. One indicator of this effect is the positive role that greater financial inclusion has in facilitating more consistent spending. Su et al. (2020) argued that the impact of financial inclusion on poverty is not much mitigated when accounting for per capita income. Naqvi et al. (2018) demonstrated that income disparity may be reduced by using the expansion of bank branches as a surrogate for financial inclusion.

Thomas et al. (2007) longitudinally showed a causal relationship between increased access to financial services and higher economic growth rates. This connection may lead to either upward or downward economic development. Several arguments have been made for why it is vital to aim toward a higher degree of financial inclusion. These arguments help to paint a picture of how financial inclusion could contribute to monetary security. From a macroeconomic perspective, more readily accessible credit might mean that banks and other financial institutions have access to more funding.

The banking and finance industries may benefit when more people have access to credit, and more people can join the economy, which has a multiplier effect that helps the economy grow. Recently, a renewed focus has been on the need to achieve sustainable development that benefits all segments of society. The study conducted by Chandio et al. (2020) suggested that countries’ economies thrive when their citizens have easier access to banking and other financial services. Yu (2021) highlighted the significance of financial literacy in boosting discretionary income for rural households and SMEs. Recent research has linked the size of the informal banking business to the availability of sources of credit, suggesting that efficient economic functioning may be attained with a smaller informal banking sector.

However, we doubted whether financial inclusion has a beneficial influence on security. Particular attention is paid in this field of study to perils that disproportionately affect individuals with fewer resources. Participation in the financial sector is more expensive for these communities because of the extra time and resources needed to gather information and complete transactions due to their poor credit histories and lack of collateral. The same holds for these two market factors. Financial inclusion and financial stability are two objectives that may compete with one another, or they may complement one another, as explored by Hoberg and Moon (2017). They reasoned that a population’s level of financial inclusion depends not only on its availability of financial services but also on how much its members and businesses utilize such services. It is impossible to gauge a population’s economic engagement based on whether they have access to these services.

Individuals and companies that have many potential avenues for obtaining capital do not indicate that they put that money to good use. There is a trade-off between having financial resources and feeling secure. Increased chances of default on payments are associated with easier access to credit for people and corporations.

Nasir et al. (2019) observed a conditional connection between access to financial resources and financial stability; they found it significant. However, the availability of financial resources was a prerequisite for seeing this correlation of nonperforming loans (NPLs) with risk premiums attached that were shown to have a statistically significant negative correlation with access to credit. Yandan and Zhang (2009), a separate team of researchers, constructed an equilibrium model to examine the connection between GDP, nonperforming loans, and inequality about the limits of financial inclusion. Six Asian and African countries, ranging in economic development, were surveyed to collect information at the business level. The study’s authors conclude that elements specific to each country moderate the impact and repercussions of financial inclusion and stability.

However, Zhang et al. (2012) provided an alternative, nonlinear explanation for the correlation between financial inclusion and financial stability. They claimed that insufficient data was a factor in their reasoning, yet they offered none. Degl’Innocenti et al. (2017) analyzed time-series data that included a radical departure. Three factors negatively affected financial inclusion, domestic credit, income disparity, and financial integration, and confirmed the link between financial integration, economic inequality, and domestic lending. In summary, the literature showed the relationship between financial inclusion and macroeconomic stability. Previous studies appear to have overlooked, for the most part, the role of financial inclusion in maintaining stability when looking at the Asian region and utilizing data from the bank level. Our research focuses primarily on characteristics, such as bank and macroeconomic factors, and the financial inclusion variable is significant.

Data and methodology

Data on the financial reports from Asian banks is collected from the Bank Scope database, and the financial inclusion index is built using macroeconomic variables on infrastructural and financial market growth from the World Bank’s Worldwide Economic Prosperity dataset and the International Monetary Fund (IMF). In this research study, the database covers information gathered from 3071 banks across 18 Asian nations and spans from 2008 to 2017 because of the only full data available for the period. Only Asian nations that existed during 2008–2017 and possessed the necessary data were included in the sample. This was done to ensure that the financial inclusion index could be reliably calculated. At the end of the fiscal year, the worth of every currency in the country was converted to dollars.

In the first part of regression analysis, the model is constructed that shows how access to financial services affects market stability, as presented in Eq. (1).

$$z\mathrm{scor}{\mathrm{e}}_{\mathrm{it}}=f{(\mathrm{Bank}\mathrm{characteristics})}_{\mathrm{it}}+f{(\mathrm{Macroeconomics})}_{\mathrm{it}}+f{(\mathrm{Financial}\mathrm{inclusion})}_{\mathrm{it}}+{\mathrm{e}}_{\mathrm{it}}$$ 1

where f(Bank characteristics) represents bank characteristics that could affect bank return on assets (ROA) at time t, and f(Macroeconomics) consists of macroeconomic aspects that might positively or negatively influence the bank i revenue at time t. f(Macroeconomics) includes factors like inflation and unemployment, and f(Bank characteristics) indicates bank attributes that might affect bank ROA at t. The qualities denoted may impact Bank i’s ROA at time t – (Bank characteristics). The f(Bank characteristics) variable stands for the characteristics of banks and is one of four component variables used to generate the Financial Inclusion index. Sub-indices may be calculated by dividing the total number of transactions by 100,000 and then dividing that number again by the population. These four parts may be added together to provide the whole index. To sum up, it stands for external, unpredictable factors that may or may not affect a financial institution’s Z-score. There are better ways to define it than that.

Bank’s risk assessment, an indication of economic condition

There is evidence linking stable financial markets to increased economic growth, but the current evidence is insufficient to draw any firm conclusions. Parallel to this, other scholars have questioned whether or not expanding access to financial services boosts GDP. Concerns about inequitable wealth distribution give rise to the idea that expanding access to financial services must slow down the economy. Previous empirical studies have substituted the z-score for bank stability to understand the link between the two betters. The results improved the reliability of the researchers’ inferences on the connection between the two factors. Recent research has shown that the z-score may be used to evaluate a bank’s operational risk and profitability. This means it may be used as a benchmark for determining finances’ security. Quite a few recent discussions have focused on this same topic. Some banks with the same z-score may elect to optimize their compensation structures while keeping their operations consistent.

In contrast, other banks with the same z-score may take on more hazardous projects to increase their earnings. This dissonance arises from the fact that similar financial institutions may engage in different activities, some of which optimize the pay structure. In contrast, others preserve functional consistency while having the same z-score value. In this work, authors build the z-score using techniques previously described by others, including Nassani et al. (2021) and Uchida et al. (2015), as presented in Eq. (2).

$$\mathrm{Bank}z\mathrm{scor}{\mathrm{e}}_{\mathit{it}}=\frac{ROA+EQA}{sd(ROA)}$$ 2

In financial terms, earnings per share (EQA) is the percentage of a company’s total earnings that are distributed to its shareholders. At the same time, return on assets (ROA) is the rate at which an institution’s holdings of assets generate profits. The term “return on assets” may refer to any of these figures. It is possible to find similarities between the two. The z-score is often defined as the percentage point by which returns must fall before a bank’s equity is depleted. The z-score threshold refers to this particular use of the z-score. The z-score measures the bank’s willingness to take on more risk relative to the standard deviation of its profit margins.

Constructing financial inclusion index

Using IMF data for a sample of Asian countries for 2008–2017, a study has created a financial inclusion index along two dimensions. This indicator takes into account data from 2008 to 2017. Each of these components exemplifies a unique facet of the bigger picture: complete financial integration. The first group of proxies in this piece stands in for the money supply (the “reach” of the piece), while the second group of proxies indicates the demand for currency. The essay makes use of both sets of examples interchangeably (usage). The convenience of banking services is often listed as the most critical factor. This may be measured, for example, by the number of tellers per client base or the density of ATMs. For a more nuanced assessment of the extent of financial inclusion, we may look at two separate metrics: the number of ATMs and the number of bank branches per 100,000 individuals. Both indicators are normalized by dividing the country’s population by itself. The ratio of credit cards to debit cards used by a sample of a thousand people might provide a reasonable approximation of the second dimension, utilization. In doing so, we may approximate the second dimension. Given the strong correlation between the shifts in these four indicators, we think giving each variable the same weight; in the final analysis, it is fair. As a result, we use a strategy in this study that considers the connection between the two variables.

Principal component analysis (PCA) is a valuable method for investigating connections between various types of evaluations and the information they contain. Using a technique called principal component analysis (PCA), it is possible to see that the first component accounts for most of the variation in the dataset. The following is a case in point: Additional components are added to the set and merged with the first set to finish making the orthonormal pattern begun by the first component.

Allayannis et al. (2003) analyzed the effect of financial inclusion on bank stability and concluded that their conclusions were defensible based on their empirical rigor. The financial inclusion index obtained using principal component analysis was significantly correlated with other indicators of national economies’ health. These parameters were pinned down as follows: The input data must first be normalized to get the most out of the principal component analysis. To do this, authors have set the range for each value of [0,1] that contains possible outcomes for the variable of interest. The findings of the principal components analysis are shown in Table 1, and the eigenvalue is more significant than one; the first component may explain as much as 71% of the total variance in the data. The fact that the eigenvalue is more significant than one provides the foundation for this calculation. Table 1 also used values from the other three eigenvectors as weights in calculating our monetary index since their probability is less than one because the eigenvectors’ probability is smaller than 1. Therefore, the other three eigenvectors are also less than 1.

Table 1.

Principal components analysis

Name	Indicator	Eigenvalue	Distinction	Ratio	Total
Density of banking/1000 individuals	Indicator-1	3.75232	3.24165	0.7159	0.8267
Number of ATMs/100,000 individuals	Indicator-2	0.818652	0.477524	0.1774	0.7963
Ratio of prepaid cards per 1000 individuals	Indicator-3	0.432239	0.306624	0.0803	0.8642
Ratio of credit cards per 1000 individuals	Indicator-4	0.226741	0.217724	0.1375	0.4528

Table 2 shows the financial inclusion score in all 18 Asian countries (UAE, Bahrain, Bhutan, Azerbaijan, Bangladesh, Georgia, Indonesia, India, Japan, Kazakhstan, Malaysia, Nepal, Pakistan, Vietnam, Laos, Lebanon, Singapore, and China) as the data collected the 18 Asian countries. Study findings show that the levels of financial inclusion in Vietnam and Laos are the lowest among the nations in our sample. In contrast, the levels of financial inclusion in Japan and Australia are considered the greatest.

Table 2.

Financial inclusion score in all 18 Asian countries

Nation	Mean	Stand-dev
UAE	0.256	0.11247
Bahrain	0.347	0.21335
Bhutan	0.521	0.13421
Azerbaijan	0.431	0.10214
Bangladesh	0.124	0.43254
Georgia	0.484	0.54474
Indonesia	0.314	0.52345
India	0.514	0.24187
Japan	0.584	0.00041
Kazakhstan	0.073	0.08245
Malaysia	0.321	0.03012
Nepal	0.187	0.24117
Pakistan	0.842	0.00012
Vietnam	0.546	0.10003
Laos	0.654	0.02354
Lebanon	0458	0.00658
Singapore	0.547	0.22154
China	0.845	0.00354

Descriptive statistics

Table 3 below provides the first descriptive ratings for both explanatory and outcome factors. Financial institution size is reported to have had a mean trend of 3.126 across the research period, with skewness of − 0.3245. The stand-dev for return on assets is 56.7825, while the average score for the power of markets is 444.983. When comparing loan guarantees to GDPC, the stand-dev during the research period is 0.1200 and 3215.37, whereas the financial inclusion index is 0.7325.

Table 3.

Descriptive statistics

	Stand-dev	Kurtosis	Mean	Skewness
z-value	22.42456	222.246	6.4569	9.5642
Financial institution size	3.1256	2.354	21.1256	− 0.324569
Index-k	44.25623	235.325	32.32546	21.3256
Operating gains	40.91654	876.789	645,699.25	33.789
Return on assets	56.7825	703.565	0.98249	− 24.96789
Power of markets	444.983	2154.33	65.358	33.21546
Loan guarantee	0.120037	81.3579	0.1098	7.9812
GDPC	3,215.37	2.5467	21.35	0.531289
GDPG (GDP growth)	3.985654	4.36659	4.69702	-6.129
Financial inclusion index	0.2983	2.79302	0.36544	0.54325
Infrastructure	2.39/81	2.3568	7.36402	-3.25588
Economic freedom index	0.73256	3.65488	8.62215	-4.14565
Development of financial market	0.85166	3.54644	7.24958	0.234585
No. of observations (N)			5893

A bank’s size (measured by the logarithm of total assets), capital adequacy (measured by the K index), current-year operating income (ROI), loan provision (measured by year-end provisions), return on assets (ROA), and market power are characteristics that are determined by the Lerner index. Two critical measures that may be used to evaluate the economy are the growth rate of gross domestic product and the total amount of money created per person. The two indicators are listed below. Financial market development, the economic freedom index, and physical infrastructure are all instrumental elements that may be found in GMM models. On a scale from 1 to 10, where ten indicates the most developed and one the least developed, each of the three factors is assigned a grade.

Results and discussions

Baseline method using the standard OLS technique

In the first part of our inquiry, the ordinary least squares (OLS) technique is used for regression analysis. The following is the formula for the regression model that we are using.

$$\mathrm{Financial}\mathrm{stabilit}{\mathrm{y}}_t=f\left(\mathrm{Financial}\mathrm{inclusio},{\mathrm{n}}_t,,,\mathrm{Bank}\mathrm{characteristic},{\mathrm{s}}_{\mathit{it}},,,{\mathrm{Macroconditions}}_{\mathit{jt}}\right)$$ 3

The subscripts i, j, and t signify the country i, the year t, and the calendar month j. In order to get an accurate measurement of financial inclusion, the usage of four sub-indicators is used. These sub-indicators indicate many characteristics of monetary usage as well as monetary penetration. Our baseline regression considers several factors pertaining to banks, including loan provision, bank size, operational revenue, the K index, return on assets, and market power, among others.

We employ the Wooldridge test, the Wald modified test, and the Pagan-Breusch test to assess whether or not the data include a correlation coefficient and whether or not they are heteroscedastic. According to the observations in Table 4, our model exhibits autocorrelation and heteroskedasticity in equal amounts. As a consequence of this, in order to solve this problem using the variance–covariance matrix estimator, we use the utilization of robust standard errors.

Table 4.

Wald test

Wooldridge method		Wald modified method		Pagan-Breusch method
F-test	Presence of autocorrelation	Chi-squared method	Heteroskedasticity presence	Chi-squared method	Heteroskedasticity presence
92.788a [0.01200]	Yes	7.51e + 42a [0.1010]	Yes	865.32a [0.0101]	Yes

Following the advice of Allayannis et al. (2003), our model incorporates the GDP per capita and the GDP growth rate. Concerns about multicollinearity might be raised due to the use of these two additional variables, depending on the context. The study demonstrated that these two traits have a mutually beneficial connection, especially in the sample of nations with higher levels of economic development and high levels of income per capita. The United Arab Emirates, Australia, Japan, and Singapore are now ranked first through fourth in terms of the sample’s per capita income. However, compared to nations such as Vietnam and Laos, the economies of these countries are rising at a pace that could be faster. The matrix of correlations is shown in Table 5, along with a corresponding rise in the quantity of variance (VIF). If the variance inflation factor (VIF) value is lower than 10, the model does not include any instances of multiple linear regression.

Table 5.

Regression results

	Ordinary least square method	Generalized moment method	Generalized moment method	Generalized moment method	Generalized moment method
	Model 1	Method 1	Method 2 (without GDP)	Method 3 (without GDPC	Method 4 (dummy variables for the financial hazards of 2009)
Financial inclusion index	21.598**	8.3565**	21.65**	21.36**	21.66
	− 7.21	− 3.21	− 3.62	− 2.1	− 2.92
Loan gurantee	− 2.55	− 8.965	3.548	1.236	− 5.369
	− 4.356	− 1.3689	− 0.456	− 210	− 0.98
Financial institution size	0.566**	0.6548**	− 0.369	1.369	0.3698
	− 4.658	− 0.3598	− 2.3698	− 0.6589	− 0.213
Operational revenue	− 0.0001235	0.300012	0.101021	− 0.221	0.3211
	− 3.5869	− 1.236	− 2.35	0.45	− 0.986
Index-k	1.3654*	1.35878	0.3258	0.215645	0.2134
	3.544	− 2.245	− 1.5454	− 0.23567	− 0.568
Returns on assets	0.567894**	2.67987**	1.4898**	1.89798	1.6477
	− 6.254	− 3.458	− 3.25	− 0.3598	− 0.146
Power of market	− 0.21	− 0.21001251	0.2154	− 0.11	0.0015
	− 0.2602	− 0.8865	− 0.4543	− 0.456	− 1258
Gross domestic product per capita	− 0.9884	0.21020154	0.216546	0.238549	0.1564
	− 2.65	− 3.59	− 2.464	− 1.26	− 4.682
GDP growth	0.9894	0.87974		0.564867
	− 4.97	− 0.33		− 1.53	0.5464
Infrastructures		− 2.564	− 2.54669	− 2.5844	− 2.565
		− 2.564	− 3.564	− 3.564	− 5.88
Economical freedom index		− 0.89879	− 0.56447	− 0.464	0.8988
		0.98725	0.5688	− 0.65448	− 3698
Financial market growth		2.6544*	2.5458*	3.5648*	2.56468*
		− 3.59	− 3.68	− 3.54	− 5.68
Constants	− 7.56464	− 7.5646	− 7.568	21.5464	− 4.547
	− 3.5984	− 1.58546	− 0.244	− 0.987	− 0.569
N	3704	3704	3704	3704	3704
AR-2 tests		− 2.352	− 2.3658	− 1.587	− 0.325
		− 0.543486	− 0.65486	− 0.56464	− 0.156
Hansen test		32.5464	21.6569	0.986	21.654
		0.6898	− 0.3544	− 0.899	− 0.975

GMM method

Endogeneity will always be a problem for research endeavors that employ panel data sets containing various macroeconomic factors. This is because endogeneity is a problem that cannot be controlled. For instance, a confluence of events may have an effect not only on the present state of the economy but also on the condition of the financial sector. This is because economic conditions and financial conditions are intertwined. The projected outcomes could not be accurate due to these variables that needed to be considered. A further benefit is that a significant study area, which is an advantage in its own right, is to investigate the link between financial inclusion and financial security.

A method utilized in the study demonstrated to suffer from heteroskedasticity and autocorrelation in prior empirical studies. Our trials corroborate this, and we use GMM to remedy the situation. According to studies by Abbasi and Riaz (2016) and Hsu et al. (2021), this is the case when dealing with serial correlation and unobservable heterogeneity. The Sargan, Hansen, and Arellano-Bond statistics in the GMM estimation procedure support our instrumental variables.

Results from our OLS regression analysis shown in Table 5, along with our hypothesized findings, show a correlation between financial inclusion and bank health. However, the calculations performed using the GMM will serve as the primary focus of this conversation. Results from our preferred model, model 1, which accounts for GDP growth and increase in GDP per capita, show that financial inclusion has a substantial, favorable effect on a country’s political and economic health.

In addition, this study analyzed a wide range of possible outcomes in models 2 (generalized method of moment), 3 (generalized method of moments without GDP), and 4 (generalized method of moments without GDPC) to increase the credibility of the findings. Model 2 is built first because of the multicollinearity between GDP growth and GDP per capita. Since multicollinearity with GDP per capita is conceivable, taking this precaution is preferable to the alternative. The second reason did not factor in GDP per capita while designing model 3 was because of the multicollinearity issues we anticipated would arise due to the GDP increase. Unfortunately, we had to reach this verdict due to our concerns. Model 4 is the culmination of our efforts and includes a dummy variable to account for the impact of the financial crisis of 2008.

Models 1, 2, and 3 lead us to conclude that adding financial services has a positive and statistically significant impact on stability in Asia. When the effects of the crisis are factored in, however, statistical studies show that broadening access to banking services has a chilling effect on financial security in Asia. This holds actual whether or not the crisis is considered. However, Hansen’s research demonstrates that we cannot trust the inferences made by model 4. Our research indicates that financial inclusion has significantly benefited Asia’s macroeconomic stability from 2008 to 2017. Our results are similar across all situations, leading us to this conclusion. Based on our results, models 1 and 2 show that rising levels of return on equity, GDP per capita, and complexity of financial markets all play a positive and vital role in maintaining bank stability in Asia, which in turn promotes economic growth across the region.

Discussion

This research looked at the effects of financial inclusion and the banking sector’s role in sustainability for selected 18 Asian economies. It also examined the effects of carbon policies and people management on GDP, the amount of carbon capture, and the necessity for ecologically responsible technological development. We selected 18 Asian nations to analyze because of many factors (Ikram et al. 2019a). These seven economies represent over half of the global GDP. Therefore, their efforts will determine how low CO₂ levels. Absolute emissions from Asian countries increased in 2010; China tops the list for both greenhouse gas emissions and electricity consumption per person among Asian nations, according to research (Shah et al. 2019).

Consistent subsidies of fossil fuels will lead to a “normal” rating for China’s climate change strategy. Moreover, China, Indonesia, and Nepal all have exceptional greenhouse gas emissions and oil consumption results, whereas Bhuttan has poor efficiency. This study is interesting since it looks at regional nations like Asia and analyzes their similar characteristics. The world’s leading countries might use the study’s results to shape policies that promote global stability (Mohsin et al. 2020a). The analysis assumes that the Asian areas will grow in building over the long run. Such findings provide credence to the study’s central hypothesis that efforts to improve environmental quality (such as those taken to combat climate change) correlate with higher income growth. The environmental, economic, and social well-being of the Asian region may all benefit from climate funding strategies. Bad choices emerge from using metrics that falsely suggest poor accomplishment. This first version of sustainability may provide regulatory insight by assisting nations in moving beyond narrow environmental concerns and into broader areas of environmental sustainability and by providing scores that can be utilized for such analysis and comparison. Sustainable development provides an instantaneous assessment of a firm’s efficiency concerning sustainability implications, revealing whether or not the capacity of a natural town to provide ecological services has been compromised (Mohsin et al. 2021).

Therefore, the following assumptions have been accepted, and the outcomes of our study are anticipated to hold up well over time. Statistical significance at the 5% level is indicated by Zhang and Dilanchiev (2022) and Mohsin et al. (2018). The VECM-based Tables 3 and 4 show the study’s quantitative findings, which are consistent with the literature (Chang et al. 2022; Liu et al. 2022; and Mohsin et al. 2021) and demonstrate a long-term unidirectional causality between better environments and climate financing potential. Our results are consistent with those of Yu et al. (2022) and Yumei et al. (2022). Results are consistent with previous research (Rao et al. 2022), supporting the present study’s unidirectional results and unable to establish a link to the bright future of any Asian countries.

Conclusion and policy implication

Governments throughout the globe have been monitoring the stability of their financial systems to mitigate the effects of the COVID-19 pandemic on their economy and people. A financially secure banking system is crucial for governments to have a safety net during times of crisis. Additionally, politicians, practitioners, and economists have emphasized financially inclusive growth. Financial stability and financial inclusion are two strategies that many developing nations and international financial institutions have adopted to stimulate economic growth and development. The common belief that banks and other financial institutions cannot afford to assist disadvantaged groups while maintaining their viability and the economy as a whole persists. People of lesser socioeconomic positions, smaller businesses, and people from more rural areas are all instances of this.

This study analyzes data from financial institutions across 18 Asian countries using a GMM model. The study’s data spans the years 2008–2017. The research includes data from 2008 until the present. Research study findings show various realistic-looking environments to ensure their accuracy, and the results indicate that financial inclusion is helpful to the economies of Asian nations. Financial inclusion might improve economic efficiency by encouraging more people to save and invest.

Additionally, a financially inclusive economy enables governments to implement pro-poor policies that help individuals and families escape poverty. Our research indicates that if people in rural areas save more and deposit more money in local banks, those banks may be more willing to provide credit to local businesses and individuals. One of the primary advantages of encouraging financial inclusion is that it helps to safeguard the retail deposit base. Banks should fortify and stabilize their operating income by focusing on disadvantaged communities and the capital market for funding. Financial inclusion primarily helps to improve sector stability via the prompt and suitable supply of financial solutions and products to families and SMEs.

A bank’s bottom line might see improvement with a more holistic strategy, one that makes use of less expensive demand deposit choices. While moral hazard is an issue for financial institutions in general, it may be reduced if banks adopt a more tailored approach to lending based on the requirements of individual borrowers and businesses. Increasing people’s access to financial services has the potential to build a safer monetary system. The development of every economy depends on its citizens’ ability to enter the financial system. Financial inclusion and system security are two sides of the same coin, and both must be considered when drafting and executing regulations.

Financial inclusion could be fruitful as a means to fortify Asia’s banking system. In one of the fastest-growing economies in the world, it is essential to reach individuals who are underserved by the financial system, such as the underprivileged, those living in rural regions, and small businesses. If banking services were more accessible to the unbanked, financial institutions would have a greater chance of securing new and dependable sources of money for their lending activities. This would improve the financial standing of individual banks and the industry as a whole. If keeping microaccounts is not viable for banks, economic development officials may opt to lower their exorbitant expenses. Even little incentives to open accounts may stimulate demand more than financial education. Innovations in the financial industry and technological advances are reducing the high-fixed costs of administering a limited number of accounts and payments.

Based on the results, it is recommended that policies must be implemented to increase energy efficiency, a critical enabler for several Sustainable Development Goals (SDGs) such as sustainable energy and the environment. The Sustainable Development Goals focus primarily on increasing efficiency in energy use and providing widespread access to alternative and renewable energy sources. The USA must thus prioritize energy efficiency by boosting its spending on energy infrastructure. Those who are most at risk will be helped by increased energy efficiency because it will allow them to take advantage of the economic possibilities presented by the shift to a low-carbon future. More efficient use of energy resources would also lessen environmental costs and dangers. Energy efficiency is also much impacted by the state of the economy, so fixing that would be a huge help. Financing for the energy-saving initiative must come from the financial system. Expanding access to capital will help expand renewable energy’s contribution to the global energy mix. The World Bank cites financial inclusion as a critical facilitator for realizing one of the Sustainable Development Goals (i.e., access to reliable, low-cost energy). Investment in renewable energy resources via R&D may be bolstered with the help of a stable financial system, which is expected to have a favorable effect on energy efficiency. There is an urgent need to implement energy-related initiatives to boost production that relies heavily on renewable energy. Additionally, there is a need to broaden the availability of financial services to consumers. Improvements in the energy sector by the public and corporate sectors will only be practical if people become involved.

Investment in energy sources, financial inclusion, industrial production, and trade openness are all examined, but only in the context of their effects on energy efficiency in Asian countries. We investigated the hypothesis that, in the case of Asia, energy efficiency is significantly influenced by investment in energy sources in addition to other explanatory factors. Europe and other regions/groups of nations like the OECD, G8, and BRICS may all benefit from this study’s findings. Investment in energy sources and energy efficiency are two areas that require further investigation into the causes and effects of this connection.

Author contribution

Mohammad Maruf Hasan Conceptualization, methodology software, Data Creation, and Preparation of original draft : Zheng Lu: Conceptualization, Methodology, Software, and Date collection, Revision of the Manuscript.

Data Availability

The data that support the findings of this study are openly available upon request.

Declarations

Ethical approval and consent to participate

The authors declared that they have no known competing financial interests or personal relationships, which seem to affect the work reported in this article. We declare that we have no human participants, human data, or human issues.

Consent for publication

We do not have any individual person’s data in any form.

Competing interests

The authors declare no competing interests.

Preprint Service

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