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The Role of AI-Based Green Banking Technologies in Ensuring Bank Stability:
The Moderating Influence of Climate Change
Conference Paper · February 2024
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The Role of AI-Based Green Banking Technologies in Ensuring Bank Stability:
The Moderating Influence of Climate Change
Ritika Dhyani1, Lucky Gupta2, Deepak Tomar3, Archita Srivastava4
1
Student, Department of Management Studies, Raj Kumar Goel Institute of Technology,
Ghaziabad
2
Assistant Professor, Department of Management Studies, Raj Kumar Goel Institute
of Technology, Ghaziabad
3
Assistant Professor, Department of Management Studies, Raj Kumar Goel Institute
of Technology, Ghaziabad
4
Assistant Professor, Department of Management, INMANTEC INSTITUTIONS
(IAMT), Ghaziabad
ABSTRACT
The presence of an unstable financial system might lead to unfavorable economic
consequences. This research used structural equation modeling (SEM) methods utilizing
SMART-PLS to evaluate the data collected from a sample of 380 important respondents from
banks. These respondents are actively engaged in the adoption of green banking technology
and the implementation of climate change efforts, namely in the area of renewable energy. The
present study has discovered that the implementation of environmentally friendly banking
technologies, such as chatbots, facial recognition, and fraud detection, has had a notable and
favorable impact on the stability of banks as well as their responsiveness toward climate change
initiatives. Furthermore, it has been observed that the relationship between artificial
intelligence-based green banking technologies and bank stability is partially and positively
influenced by mediation. This work has importance for both managers and policymakers,
including both practical and theoretical implications.
KEYWORDS:
Bank Stability, Artificial intelligence (AI), Chatbot Technology, Face Recognition Systems,
Fraud Detection Mechanisms, and Renewable Energy Sources.
1. INTRODUCTION
According to scholarly sources (Flejterski, 20197; Čihák, 2007)4, the presence of a stable
financial system enables effective resource allocation, risk management, employment
promotion, and prevention of changes in the values of physical or financial assets, which might
impact employment and the monetary system. According to Das et al. (2010)5, a financial
system is considered to function within a state of stability when it is capable of rectifying
economic imbalances that arise either internally or as a result of significant unforeseen events.
Financial stability is subject to various risks, such as economic uncertainty (Altig et al., 2020)2,
country governance (Kamran et al., 2019)9, market concentration (Kamran et al., 2019)9,
financial literacy (Philippas and Avdoulas, 2020)15, financial inclusion, and corporate social
responsibility (Jahmane and Gaies, 2020)8. Nevertheless, the implications of climate change
have recently emerged as a novel prospective peril to the worldwide financial system.
Consequently, in recent years, several central banks and financial regulators have guided
investors and financial institutions, urging them to assess their vulnerability to financial risks
linked to climate change (Battiston et al., 2021)3.
The dynamic nature of the environment poses a significant challenge for modern economies,
both in terms of their sustainability on Earth and their potential to maintain economic and
financial stability. Based on the analysis conducted by the World Bank, it is projected that the
nations with the highest level of development would experience the most significant
consequences as a result of climatic changes. Climate change has the potential to lead to
elevated temperatures, rising sea levels, and more frequent and intense weather phenomena,
including earthquakes, floods, and other natural calamities. These changes have increased the
probability of experiencing a shortage in the availability of food and potable water, a rise in the
incidence of diseases, and the exacerbation of poverty and hunger. Changes to the climate not
only impact the livelihoods of individuals living in less developed countries but also influence
several global organizations.
In contemporary times, a considerable number of companies, particularly those in the banking
sector, are actively advocating for the adoption of green finance practices in their projects. The
primary objective behind this initiative is to encourage the implementation of environmentally
sustainable projects, therefore mitigating the adverse effects of climate change on their
economic activities. The recent change in banking policy has created novel prospects for
technology companies to introduce innovative services. These include the incorporation of
blockchain technology, the utilization of artificial intelligence for identity verification and
information dissemination, and the application of big-data analytics to assess the
creditworthiness of individuals. This shift has been driven by the increasing digitization of
various sectors, such as education, banking, and e-commerce (Vaishya et al., 202020; Nguyen
et al., 2020; Naudé, 2020)13. Nevertheless, the impact of emerging technologies such as green
banking on financial stability remains uncertain. Hence, the primary aim of this study is to
examine the use of Artificial Intelligence (AI) in the context of green banking technologies,
specifically about climate change, and assess its influence on the financial stability of the
INDIA.
Furthermore, a growing number of countries are transitioning towards the utilization of
alternative energy sources, specifically renewable energy, to mitigate environmental pollution
and minimize the detrimental impacts of climate change on their economic systems.
Nevertheless, the extent to which renewable energy effectively mediates the association
between green banking technologies (IA) and financial stability remains uncertain.
Data was gathered from a sample of 380 bank representatives, specifically those holding
positions such as technology heads, deposit managers, credit managers, and general bank
representatives. These individuals were actively engaged in the adoption and execution of
green banking initiatives or the utilization of artificial intelligence applications, including
chatbots, facial recognition banking apps, and fraud detection systems. The data collection took
place within the context of the climate crisis. The findings indicate that the implementation of
green banking technologies has a notable and favorable impact on the financial stability of
banks, as measured by indicators such as return on assets (ROA) and return on equity (ROE).
Moreover, the incorporation of renewable energy sources has been seen to have a major and
beneficial role in moderating the association between the stability of banks in INDIA and the
adoption of green banking technologies.
This study has significance for several stakeholders, including central banks, commercial
banks, bank management, and policymakers involved in formulating climate change policies.
The following document has many key sections, including a comprehensive literature review,
a detailed study methodology, the obtained findings, an in-depth discussion, and potential
future implications. The subsequent portion of this manuscript will expound upon the literature
review.
2. LITERATURE REVIEW
A limited body of literature exists that examines the connection between green banking
technology, climate change, and the financial stability of commercial banks. Several notable
research papers have identified the key factors contributing to the stability of banks
(Laskowska, 2018 11; Zhang et al., 2022 22; Shaumya and Arulrajah, 2016 17; Sharma and
Choubey, 2022 16). According to Sharma and Choubey (2022)16, the stability of the stock
market and the larger economy have a crucial role in determining the dependability of the
banking system. The individual had the belief that the market index played a crucial role in
assessing the overall stability of commercial banks. According to the findings of Laskowska
(2018) 11, there exists a negative correlation between the economic climate and the stability of
banks. In their study, Shaumya and Arulrajah, 2016 17 investigated the importance of early
warning indicators, the consequences of regional spillovers, and the influence of the economic
cycle on the precarious conditions of banks as probable factors contributing to these scenarios.
In their study, Muhammad and Zaheer (2012) 12 examined many characteristics related to the
firm, the economy, and the governance structure to assess the stability of the bank. It has been
shown that the bank's stability is negatively impacted by the quality of the organization's
governance.
The influence of economic variables, such as GDP growth, inflation, and organizational quality,
on bank stability is a subject of interest. However, research is scarce on the potential impact of
green banking technologies on bank stability. Specifically, it remains unclear if the
implementation of renewable energy projects by banks might mitigate the link between green
banking technology and bank stability.
The existing body of literature extensively supports the notion that the integration of novel
artificial intelligence technologies inside the financial industry has yielded substantial
enhancements to the banking system over a prolonged period. For example, the use of artificial
intelligence (AI)-based face recognition technology has facilitated the process of creating
accounts and mitigated instances of fraudulent activities within the banking industry (Kaya et
al., 2019 10; Soni, 2019 19; Smith and Nobanee, 2020 18; Donepudi, 2017 6; Abdulla et al., 2020
1
). Nevertheless, the extent of these technologies' impact on the climate change process remains
uncertain and requires more investigation.
Thus, the present research posited the following hypothesis:
H1 - The implementation of environmentally friendly banking technology (AI) has had a
substantial and favorable impact on the stability of banks.
H2 - The link between green banking technology and financial stability is positively influenced
by climate change activities.
H3 - Climate change measures have a substantial and beneficial impact on financial stability.
H4 - The introduction of green banking technology, namely artificial intelligence (AI), has had
a substantial and favorable impact on the advancement of renewable energy efforts.
3. DATA AND RESEARCH METHODOLOGY
The current research gathers data from a sample of 380 participants who are representatives
from the banking industry. These individuals hold various positions within the sector, including
technology leaders, deposit managers, credit managers, and bank representatives. Initially, a
total of 560 questionnaires were issued. However, only 380 replies were obtained from various
institutions, including both commercial and private banks, across the INDIA. The statistical
software SPSS was used to examine the association between the independent variable of
climate change, the mediator variable of artificial intelligence (namely chatbots, face
recognition banking applications, and fraud detection systems), and the dependent variable of
bank stability, as measured by return on equity (ROE) and return on assets (ROA).
(THE FIGURE DEMONSTRATES THE USE OF A CONCEPTUAL FRAMEWORK.)
MODERATE VARIABLES
CLIMATE CHANGE INITIATIVES
RENEWABLE ENERGY
H4
INDEPENDENT VARIABLES
AI-BASED GREEN BANKING DEPENDENT VARIABLES
TECHNOLOGY
BANK STABILITY,
Chatbot Technology, Face
Recognition Systems, Fraud H2 ROA, ROE
Detection
Figure 1
4. FINDINGS AND ANALYSIS
The present component of the research presents an analysis of the obtained findings.
Initial Assessment
Before performing the survey on a broad scale, a pretest methodology is used to assess the
questionnaire's quality (Willis, 2016) 21. This phenomenon leads to a decrease in ambiguity and
a reduction in redundancy within the survey constructs.
Experimental Validation
Before conducting data collection on a broader scale, we conducted preliminary tests on the
survey questions to assess the validity of the proposed model. When performing pilot testing,
it is important to assess the validity and reliability of the recommended constructs. Obtaining
a significant quantity of data for the pilot testing proved to be difficult because the sample
frame only included individuals who were gainfully employed. Nevertheless, a sufficiently
representative sample of bank managers from the neighboring area was deemed sufficient to
authenticate the data used in the study. The main objective of our study was to ascertain the
reliability of the materials used in the research. Our team was responsible for formulating the
inquiries for the web-based questionnaire, and we established contact with participants by
providing them with a hyperlink to access the survey. The total number of respondents was 55,
out of which 42 provided correct answers. To ensure the establishment of a satisfactory level
of reliability, the obtained data underwent validation using the SmartPLS program. The
findings revealed that the whole framework had notably favorable outcomes, with reliabilities
beyond the acceptable threshold. For instance, the coefficient alpha exceeded 0.7, with several
values surpassing 0.9. In a similar vein, it is worth noting that the factor loadings exhibit values
over 0.70, with a considerable number of loadings surpassing the threshold of 0.90.
The findings from the pilot test are as follows:
Construct Cronbach’s Alpha Means (SD) Factor
s (α) Loading
Range
C 0.827 0.826 3.55 (1.17) 0.899-0.925
B
AI-Based
Banking
F 0.829
Green
R
F 0.826
D
Climate change 0.935 3.90 (1.2) 0.822-0.841
Bank- Stability 0.936 3.39 (1.01) 0.927-0.956
Table 1
Estimation of the Measurement Model
The Concept of Reliability
In the subsequent phase, factor loadings were ascertained using the use of SMARTPLs. The
results are shown in Table 5.3, which illustrates the range of factor loadings for the items about
the relevant structures. All of the observed factor loadings had values above the established
threshold for acceptability, which was defined as being more than 0.70. The loading of the
components for each of the builds is shown in Table 5-5.
Initially, the whole sample dataset was analyzed in conjunction with the anchor points. then,
the mean value of each proposed construct was computed and then compared to the
aforementioned values. Before embarking on a comprehensive examination of the
constructions, this first step was undertaken. The results of the investigation are shown in Table
3-2, providing an overview of the many constructs examined. It has been determined that none
of the constructs provide insight into significant issues, such as a lack of diversity.
The mean and anchor point for each construct are essential components in quantitative
research. These measures serve to provide a central tendency and a reference point,
respectively, for the variables being studied. The mean is the average value of a construct,
indicating the typical or most common score. On the other hand, the anchor point is a fixed
reference.
The mean and anchor point are illustrated in the next table, Table No. 2
Constructs Anchor points Means
AI-Based CB 7-Strongly Agree, 1-Strongly Disagree 4.64 4.64
Green 4.62
Banking FR 4.66
FD
Climate change 7-Strongly Agree, 1-Strongly 4.74
Disagree
Bank Stability 7-Strongly Agree, 1-Strongly 4.59
Disagree
Table 2
The subsequent stage included using Confirmatory Factor Analysis (CFA) to ascertain the
extent to which a certain component exhibited a leading role. Each element of the construct
was assigned to its latent variable. It was determined that the factor loading for each item in
the construct exceeded the threshold of 0.70, which represents the top boundary of the
permissible range. The details of factor loading, as well as the number of items used to represent
each construct in this study, may be found in Table 3.
Constructs items Factor Loading Range
AI-Based CB 7 21 0.621-0.822 0.548-0.822
Green FR 7 0.591-0.772
Banking FD 7 0.548-0.807
Climate Change 6 0.524-0.890
Bank- Stability 6 0.599-0.831
Table 3
The applied idea in this study underwent testing to assess its convergent validity and reliability.
The evaluation was conducted utilizing established metrics such as "Cronbach's alpha," "factor
loadings," and "average variance extracted" (AVE) (Hair et al., 1998). A confirmatory factor
analysis (CFA) was conducted to assess the validity of the items. The results revealed that all
of the constructs exhibited a satisfactory level of reliability, as shown by Cronbach's alpha
coefficients above 0.70. It was determined that the factor loading for each construction
component exceeded the established threshold, which was defined as factor loadings above
0.60. Finally, the average variance extracted (AVE) for each construct indicates that the value
of each construct is above the recommended threshold level, implying that the AVE is
statistically significant, surpassing the value of 0.50. When the AVE (Average Variance
Extracted) exceeds 0.50, it indicates that the latent factor is responsible for a minimum of fifty
percent of the variability seen among the items.
The construction of reliability and validity is a fundamental aspect of the field of research
methodology. Reliability refers to the consistency and stability of measurement, indicating the
extent to which a measurement tool or instrument produces consistent results throughout. Table
No 4 illustrates the parameters
Average Variance
Cronbach's Alpha Composite
Reliability Extracted (AVE)
Climate Change 0.775 0.807 0.844
Bank Stability 0.834 0.851 0.880
CB 0.833 0.846 0.875
FR 0.836 0.852 0.876
FD 0.867 0.875 0.898
Table 4
To ascertain the factor loadings of the variables in the study, a Confirmatory Factor Analysis
(CFA) was conducted using SmartPLS (Hair et al., 2010). The results are shown in Table 3.4
as seen below. The use of Confirmatory Factor Analysis (CFA) is often used to assess the
soundness of the item-factor structure and to facilitate training for factor association analysis.
The findings of the confirmatory factor analysis (CFA) indicate that the indices assessing the
overall adequacy of the proposed model are valid. This is because the resulting values are
within the designated threshold range for loading values, which ranges from 0.628 to 0.856.
The Cronbach's Alpha values of the connected constructions are all above the minimal
threshold value of 0.70. According to the data shown in Table 3.4, both the CR and AVE
values have attained a level that may be deemed satisfactory. Consequently, it was determined
that all instruments used in the construction were reliable. Except for one indicator, all of the
readings are above the indicated threshold, hence suggesting the absence of cross-loading
issues. As a result, this particular component was omitted from the construction process.
5. HYPOTHESIS TESTING
The presentation of the control variables' findings precedes the discussion of the suggested
hypothesized link.
Conducting Hypothesis Testing - (H1-H4)
Hypothesis 1: The use of green banking technology has a favorable impact on a firm's climate
change activities.
The findings obtained from the structural analysis provide support for the outcomes. The
variable GBT (Hl: β = 0.238, p <. 001, β = 0.313, p <. 001, β = 0.333, p <. 001) demonstrates
statistical significance as a predictor of CC.
Hypothesis 2: The adoption of green banking technology has a substantial impact on the
stability of banks. The findings of the study indicate that GBT (H2: β = 0.406, p <. 001, β =
0.288, p <. 001, β = 0.113, p < 0.001) serves as a statistically significant predictor of bank
stability.
Hypothesis 3: The presence of climate change, namely in the form of renewable energy, has a
favorable impact on the stability of banks.
The findings of this study also indicate that CC is a significant predictor of BS, supporting
Hypothesis 3 (β = 0.138, p < .001).
Hypothesis 4: The link between green banking technology and bank stability is mediated by
climate change efforts.
The findings of the structural analysis provide evidence that CC plays a mediating role in the
link between the GBT and BS (H4: β = 0.033, p <. 059, β = 0.043, p <. 001, β = 0.046, p <.
001). Hence, the results of the structural analysis provide empirical evidence in favor of all the
hypotheses (Hl-H4), confirming their validity.
Hypothesis Path Path Coefficient p-value Result
H1 CB → CC 0.238 2.639 Supported
FR → CC 0.313 4.325 Supported
FD → CC 0.333 5.572 Supported
H2 CB → BS 0.406 5.978 Supported
FR → BS 0.288 4.732 Supported
FD → BS 0.113 2.110 Supported
H3 CC → BS 0.138 3.006 Supported
H4 CB→ CC → BS 0.033 1.892 Not Supported
CB → CC → BS 0.043 2.422 Supported
CB → CC → BS 0.046 2.586 Supported
TABLE 5 (RESULTS OF HYPOTHESIS)
6. CONCLUSION
A robust financial system has the ability to effectively allocate resources, evaluate and mitigate
financial risk, bolster employment rates, and prevent variations in the values of physical or
financial assets that might impact employment levels or the monetary system. The rapid
fluctuations in climatic patterns have resulted in increased instability in the banking sector,
posing significant risks and hindering banks' capacity to produce profits. Hence, financial
institutions are increasingly embracing technological advancements to mitigate operational
expenses and promote environmental sustainability. Nevertheless, it is important to note that
the existing body of research in this domain remains significantly constrained. This research
study gathered data from a sample of 380 bank representatives, specifically those holding
positions such as technology heads, deposit managers, credit managers, and general bank
representatives. These individuals were actively engaged in the process of adopting and
implementing green banking initiatives or artificial intelligence applications (such as chatbots,
facial recognition banking apps, and fraud detection systems) in response to the climate crisis.
The findings indicate that the implementation of green banking technologies has a notable and
favorable impact on the financial stability of banks, as measured by indicators such as return
on assets (ROA) and return on equity (ROE). However, it is noteworthy that the presence of
renewable energy adoption has a major and beneficial role in moderating the association
between the stability of banks in INDIA and the use of green banking technologies. The present
investigation is subject to many limitations. Firstly, it is important to note that this research is
limited to INDIA. Secondly, the dataset used in this study consists of only 380 accurately
recorded replies. In order to enhance the robustness of the study, future researchers may
consider augmenting the response rate and expanding the sample size to include a different
nation, hence facilitating the generalizability of the findings.
7. RESPONDENTS ANALYSIS
Descriptive statics No. Of respondents Frequency
Percentage
Position in the company IT Heads 113 29.7
General Manager 127 33.4
Sales/Marketing Manager 40 10.5
Export Manager 20 5.3
Business Development Manager 8 2.1
Managing Director 9 2.4
Production managers 10 2.6
Quality Assurance Manager 4 1.1
Others (e.g., R&D manager, human resource 49 12.9
manager, etc.)
Total (N=380) 380 100
Ethnic USA 122 32.1
INDIA 234 61.6
CANADA 15 3.9
Others 9 2.4
Total (N=380) 380 100
TABLE 5
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