The Effect of Business Intelligence on Bank Operational Efficiency and Perceptions of Profitability

Abstract

The purpose of the study is to examine the effects of business intelligence on the bank’s operational efficiency and perceptions of profitability. The study is based on 259 responses from 27 branches of a commercial bank, employing a simple random sampling technique. This research uses the partial least square- structural equation method (PLS-SEM) method to test the hypotheses. The study verifies construct’s reliability and construct’s validity of the measurement model, and tests the fitness of the structural model. The study finds that business intelligence is positively associated with operational efficiency and profitability. Further, the study reveals that operational efficiency through business intelligence positively affects bank’s profitability. Based on competitive theory, this research states that business intelligence allows the productive entity to generate superior margins compared to its market rivals. Thus, banks can offer better options more cheaply than their rivals and thereby ensure competitive advantage. Further, based on resource-based view theory, the study argues that business intelligence as a strategic resource can provide the foundation to develop bank capabilities that can lead to superior performance over time. Therefore, the study implies business intelligence application in the banking companies and helps decision-making effectiveness for the management body of banks, academics, and policymakers.

Full text

Citation: Rahman, M.M. The Effect of
Business Intelligence on Bank
Operational Efficiency and
Perceptions of Profitability. FinTech
2023,2, 99–119. https://doi.org/
10.3390/fintech2010008
Academic Editor: David Roubaud
Received: 20 January 2023
Revised: 16 February 2023
Accepted: 20 February 2023
Published: 23 February 2023
Copyright: © 2023 by the author.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
The Effect of Business Intelligence on Bank Operational
Efficiency and Perceptions of Profitability
Md. Mominur Rahman
Department of Business Administration, Northern University Bangladesh (NUB), Dhaka 1230, Bangladesh;
mominurcou@gmail.com
Abstract:
The purpose of the study is to examine the effects of business intelligence on the bank’s
operational efficiency and perceptions of profitability. The study is based on 259 responses from
27 branches of a commercial bank, employing a simple random sampling technique. This research
uses the partial least square- structural equation method (PLS-SEM) method to test the hypotheses.
The study verifies construct’s reliability and construct’s validity of the measurement model, and tests
the fitness of the structural model. The study finds that business intelligence is positively associated
with operational efficiency and profitability. Further, the study reveals that operational efficiency
through business intelligence positively affects bank’s profitability. Based on competitive theory, this
research states that business intelligence allows the productive entity to generate superior margins
compared to its market rivals. Thus, banks can offer better options more cheaply than their rivals
and thereby ensure competitive advantage. Further, based on resource-based view theory, the study
argues that business intelligence as a strategic resource can provide the foundation to develop bank
capabilities that can lead to superior performance over time. Therefore, the study implies business
intelligence application in the banking companies and helps decision-making effectiveness for the
management body of banks, academics, and policymakers.
Keywords:
business intelligence; operationalefficiency; profitability; resource-based view; bank performance
1. Introduction
The banking and financial industries are undergoing a transformation as a result of
technological advancements [
1
–
3
]. Financial institutions now face increased competition,
evolving client needs, and the requirement for stringent control and risk management
in a very dynamic market. Simultaneously, technology has enabled the development of
sophisticated business intelligence tools [
4
]. There are technologies that the banking and
financial industry can employ to exploit consumer data in order to gain insights that can
result in more intelligent management practices and business decisions [
5
–
7
]. To that end,
there are several ways that banking and finance organisations are leveraging Business
Intelligence (BI) technologies to increase profitability, mitigate risk, and gain a competitive
edge. Business intelligence enables banks to react to changing economic conditions in both
normal and tumultuous economic times [8].
Globally, business intelligence (BI) methods and technologies help banks gain a better
understanding of their operations, their clients, and their prospects. Additionally, BI can
pave the way for efficiency by highlighting areas ripe for cost-cutting initiatives, new
business opportunities, and more. Banking business intelligence helps users to integrate
numerous and dissimilar system sets in order to present dynamic data visualisation dash-
boards that would not be capable of communicating across platforms in the absence of
banking business intelligence [
9
,
10
]. Standardising that banking information is a mammoth
undertaking that requires multiple workers to spend several weeks each month to finish.
That is the present state of play for the majority of banks attempting to implement business
intelligence in banking. Consider installing a software layer on top of all those disparate
FinTech 2023,2, 99–119. https://doi.org/10.3390/fintech2010008 https://www.mdpi.com/journal/fintech

FinTech 2023,2100
banking services data stores that connect them all and enable “live” reporting of all data at
the same time. While that may sound like the simplest remedy possible, much work must
be done to standardise the underlying data before they can be used effectively [6,11].
Banks cannot afford to simply add workers in order to increase income [
1
,
12
–
14
]. They
must always look for ways to improve the efficiency of their present employees. Banks
can utilise business intelligence tools to examine operational operations in order to help
minimise ongoing expenses and/or maximise available resources and expertise. Banks
can identify methods to improve and enhance the customer experience at the point-of-
contact by assessing the performance of branch workers who engage with the customer
base. Banks employ business intelligence technologies to monitor customer, product, and
branch profitability [
4
,
15
,
16
]. Banks are increasing profitability and tracking improvement
through effective pricing strategies and efficient business operations. Additionally, business
intelligence technologies are utilised for predictive analytics to determine which customers
may be interested in acquiring which goods, when, and how (in-person, over the web, or
direct mail) [
5
]. Banks can use this additional data to develop new and enhanced goods and
services that better fulfil client wants and increase their market competitiveness. Armed
with profitability and demographic data on its customer households, banks will have a
better idea of what a good prospect looks like and will be able to promote to them more
effectively. Cross-selling and up-selling efforts can be more successful if banks know which
customers to target [
3
,
17
]. Additionally, business intelligence systems can be used to analyse
developments outside a bank in order to develop alternative investment plans. Investors
can acquire particular insight into sentiment and build trade signals by analysing data
from social media [
18
]. Through the use of analytics and business intelligence technologies,
entirely new categories of investing are developing. Financial institutions must be as lean
and efficient as possible in today’s ultra-competitive industry. By analysing operational
processes with business intelligence tools, banks can decrease ongoing costs and maximise
available resources and knowledge [
19
]. Organisations can identify methods to improve
and enhance the customer experience at the point of contact by assessing the performance
of customer-facing staff such as sales representatives, tellers, and account managers.
A limited amount of business intelligence(BI) studies has beenfound in
Bangladesh [12,20–24]
.
Tumpa, Saifuzzaman [
20
] studied the BI covering the mental healthcare sector of Bangladesh;
Arefin, Hoque [
21
] studied on organisational culture and BI; Al-Hasan, Aktar [
22
] presented
BI model for textile industries; Babu [
12
] stated the challenges of artificial intelligence in
Bangladesh; Nahar, Naheen [
23
] studied artificial intelligence and fire surveying; and
Biswas, Rahman [
24
] stated the roles of emotional intelligence. However, there is a gap re-
garding the association of business intelligence with operational efficiency and perceptions
of profitability of banks in Bangladesh.
Furthermore, only a few studies on business intelligence were found internation-
ally [
17
,
18
,
25
–
30
]. Lim, Chen [
18
] studied on business intelligence analytics and operations
but did not link profitability; Ranjan [
25
] showed the links between BI and strategic decision
making; Elbashir, Collier [
17
] found links between BI and bank performance; Sahay and
Ranjan [
26
] studied on BI and supply chain analytics; Nofal and Yusof [
27
] researched BI
and enterprise resource planning; I¸sık, Jones [
28
] found links of BI with environmental
decision and operational efficiency; Olszak [
29
] studied the application of BI by collect-
ing qualitative data; Yiu, Yeung [
31
] links BI and profitability; and Lawrence [
30
] found
linked of BI with operational efficiency in hospitals. Thus, there is a gap in the associa-
tion of BI with bank operational efficiency and profitability in the business intelligence
literature internationally.
The study found a dearth of business intelligence studies in banking companies in
both nationally (Bangladesh) and internationally. Furthermore, Tumpa, Saifuzzaman [20],
Al-Hasan, Aktar [
22
], Biswas, Rahman [
24
], Lim, Chen [
18
], Elbashir, Collier [
17
], Ol-
szak [
29
], and Lawrence [
30
] suggested further study as BI has implications on businesses.
In Bangladesh, banking companies are going to implement BI to attain a strong business
frame. Thus, the study developed a research model (see Figure 1) that links business

FinTech 2023,2101
intelligence with the operational efficiency and profitability of banks. More specifically, the
study seeks answers to the following questions: “What is the impact of business intelligence
on the operational efficiency of banks ?” and “What is the impact of business intelligence
on the profitability of banks ?” Thus, the study aims to examine the effects of business
intelligence on the operational efficiency and profitability of banks. Figure 1shows the
conceptual model of the study.
FinTech 2023, 2, FOR PEER REVIEW 3
Al-Hasan, Aktar [22], Biswas, Rahman [24], Lim, Chen [18], Elbashir, Collier [17], Olszak
[29], and Lawrence [30] suggested further study as BI has implications on businesses. In
Bangladesh, banking companies are going to implement BI to attain a strong business
frame. Thus, the study developed a research model (see Figure 1) that links business in-
telligence with the operational efficiency and profitability of banks. More specifically, the
study seeks answers to the following questions: “What is the impact of business intelli-
gence on the operational efficiency of banks?” and “What is the impact of business intel-
ligence on the profitability of banks?” Thus, the study aims to examine the effects of busi-
ness intelligence on the operational efficiency and profitability of banks. Figure 1 shows
the conceptual model of the study.
Figure 1. Conceptual model with hypothesis developed by the author.
The study uses 259 responses from general manager, senior officers, general officers,
and employees of 27 branches of a commercial bank in Bangladesh, employing simple
random sampling technique. This research uses the partial least square- structural equa-
tion modeling (PLS-SEM) method to test the hypotheses. The study verifies construct’s
reliability by factor loadings, Cronbach’s alpha, rho-value and composite reliability while
verifying construct validity by average variance extracted and the Fornell-Larcker crite-
rion for the measurement model. Then, the study tests the structural model’s fitness
through the f-square, R-square, standardized root means square residual, and normed fit
index methods. The study finds that business intelligence is positively significant in im-
proving operational efficiency and profitability of the branches. Furthermore, the study
reveals that operational efficiency through business intelligence positively affects the
profitability of the branches. Based on competitive theory, this research states that busi-
ness intelligence allows a productive entity to generate superior margins compared to its
market rivals. Thus, banks can offer better options more cheaply than their rivals and
thereby ensure competitive advantage. Furthermore, based on resource-based view the-
ory, the study argues that business intelligence as a strategic resource can provide a foun-
dation to develop bank capabilities that can lead to superior performance over time.
This study contributes in at least four respects. First, the study shows a positive and
significant relationship between business intelligence and operational efficiency in banks.
This finding is unique in both national and international literature. Thus, management
bodies and policymakers can implement this finding in banking companies to enhance
operational efficiency. This finding somewhat complementary to Tumpa, Saifuzzaman
[20], Işık, Jones [28], Olszak [29], and Lawrence [30] who conceptualises BI in the same
direction. Furthermore, the study finds that business intelligence significantly increases
the profitability of banks. Thus, this finding will create insights for banks and particularly
for banking companies. This finding is also complementary to Arefin, Hoque [21], Biswas,
Figure 1. Conceptual model with hypothesis developed by the author.
The study uses 259 responses from general manager, senior officers, general officers,
and employees of 27 branches of a commercial bank in Bangladesh, employing simple
random sampling technique. This research uses the partial least square- structural equation
modeling (PLS-SEM) method to test the hypotheses. The study verifies construct’s reliability
by factor loadings, Cronbach’s alpha, rho-value and composite reliability while verifying
construct validity by average variance extracted and the Fornell-Larcker criterion for
the measurement model. Then, the study tests the structural model’s fitness through
the f-square, R-square, standardized root means square residual, and normed fit index
methods. The study finds that business intelligence is positively significant in improving
operational efficiency and profitability of the branches. Furthermore, the study reveals that
operational efficiency through business intelligence positively affects the profitability of
the branches. Based on competitive theory, this research states that business intelligence
allows a productive entity to generate superior margins compared to its market rivals.
Thus, banks can offer better options more cheaply than their rivals and thereby ensure
competitive advantage. Furthermore, based on resource-based view theory, the study
argues that business intelligence as a strategic resource can provide a foundation to develop
bank capabilities that can lead to superior performance over time.
This study contributes in at least four respects. First, the study shows a positive and
significant relationship between business intelligence and operational efficiency in banks.
This finding is unique in both national and international literature. Thus, management
bodies and policymakers can implement this finding in banking companies to enhance
operational efficiency. This finding somewhat complementary to Tumpa, Saifuzzaman [
20
],
I¸sık, Jones [
28
], Olszak [
29
], and Lawrence [
30
] who conceptualises BI in the same direc-
tion. Furthermore, the study finds that business intelligence significantly increases the
profitability of banks. Thus, this finding will create insights for banks and particularly for
banking companies. This finding is also complementary to Arefin, Hoque [
21
], Biswas,
Rahman [
24
], Ranjan [
25
], Elbashir, Collier [
17
], and Olszak [
29
]. Second, the study employs
resource-based view theory of business intelligence to explain the association between BI,
operational efficiency, and bank’s profitability. Thus, this research has theoretical contribu-
tion in this respect. Third, structural equation modelling through the PLS technique, which
offers an evaluation of the model fitness as to the reliability and validity of each tested

FinTech 2023,2102
construct and the overall model, was employed for the methodological contribution. Thus,
the findings of this study are derived from the best-fitted model and make a methodological
contribution to previous research [8,12,20,28,32–36].
The rest of the paper presents the literature review and hypothesis development in
Section 2, research methodology in Section 3, analysis and results in Section 4, Final two
sections cover discussion and conclusions, respectively.
2. Literature Review and Hypothesis Development
2.1. Resource Based View of Business Intelligence
The term “business intelligence” (BI) has broadened over time, from its original
definition by the Garner Group in the mid-1990s, to include a wide variety of approaches,
tools, and technologies in the realm of data collection, analysis, and reporting [
30
]. The
primary responsibility of business intelligence is to enable organisations to analyse data
in real-time to assist in pragmatism in strategic decision-making. Consequently, with
interactive access to real-time data, strategists arrive at an educated strategic conclusion
based on both current and previous data [
25
]. This information may be used for a range
of purposes, including marketing, sales promotions, future building requirements, etc. BI
is a process that utilises a broad variety of tools and applications to transform data into
useable information, which can then be transformed into actions to enable key managers to
make educated choices. BI can provide banks with a competitive advantage. According
to Barney [
37
], the resource-based theory of competitive advantage is predicated on the
suppositions that banks are diverse in their possession of critical strategic resources and
that resources are not completely movable across banks. Bank resources are defined as
strengths that businesses may utilise to formulate and execute their strategy. Physical
capital resources, human capital resources, and organisational capital resources are the
classifications of resources. Physical capital resources include physical technology, plant
and equipment, geographic position, and access to raw materials [
37
]. Human capital
resources consist of the education, experience, judgement, intelligence, relationships, and
perception of the company’s management and employees. Organisational capital resources
consist of the formal reporting structure, informal and formal planning, coordinating, and
regulating mechanisms, informal relationships between groups inside a company, and
other agents in the company’s environment [37].
In order to understand why certain businesses are more successful than others, it helps
to look at how each one uses its resources and skills to meet the demands of its customers.
According to the RBV, a company’s resources are the most important factors in determining
its performance, and they may even help it maintain an edge in the market [
38
]. Banks
may perform better if it is difficult for them to imitate those that are already successful.
Companies gain competitive advantage via the accumulation of resource and capability
combinations that are unique to them. BI systems include a number of features that allow
them to collect, link, organise, and analyse data from many sources, including consumers,
supply chains, and rivals, and then present this information as knowledge for managerial
decisions. BI systems provide companies with the information they need to effectively
plan and coordinate their management activities in response to changing operational
circumstances, marketing performance, and external variables [
17
]. Businesses may obtain
new perspectives and organisational insights from their management data with the help
of BI tools that go beyond the production of standard reports. Organisations that put BI
systems to use can gain access to valuable market and internal data in real time, providing
a competitive edge that is both temporally compressed and route dependent [27].
A company’s ability to gain a competitive edge does not just depend on its ownership
of precious, uncommon, unique, and non-substitutable resources [
39
]. However, in order
to be competitive, businesses need the ability to coordinate their resources, package them
into capabilities, and then exploit those capabilities to advance their own objectives [
40
].
Banks that make extensive use of BI systems are in a better position to strategically deploy
their resources, so synchronising their management efforts across organisational func-

FinTech 2023,2103
tions and integrating operational capacity with managerial knowledge eventually leads to
greater performance [
41
]. BI allows companies to proactively investigate data about their
operations and management in an effort to boost productivity [42].
Table A1 shows the literature review summary (See Appendix A).
2.2. Business Intelligence and Operational Efficiency
Business intelligence is a highly adaptable set of tools, technologies, applications, and
procedures for gathering, integrating, organising, and analysing data in order to provide
actionable insights [
11
,
40
]. It provides a consolidated picture of company data and can
provide historical, present, and predictive insights that transform raw numbers into action
plans. With the massive amount of data generated by corporate operations and client
interactions, experts may become overwhelmed and perplexed. As business owners or
operators, they must now more than ever learn how to decipher and control such data for
the advantage of their businesses. End-users of banks and financial services organisations
can develop interactive data visualisations using SAAS (software as a service) in the context
of business intelligence in banking [
2
]. Power BI, Tableau, Tibco Spotfire and Domo are
some of the most often used banking business intelligence solutions. Banking business
intelligence apps can be virtualised or customised to run on devoted personal servers for
financial services banks with stringent data security requirements [5,43].
Corporate intelligence is a collection of concepts used to optimise business perfor-
mance via the intelligent use of accessible data [
30
]. In business intelligence, technologies
are used to transform data acquired from many sources into relevant information for use
in the business. This shift facilitates decision-making on a strategic level. In other words,
it is conducting business using intelligent tools and technologies. There are numerous
definitions of business intelligence, but the simplest is conducting business by incorpo-
rating external intelligence [
26
,
44
]. Operational efficiency refers to the standard of work
performed by an organisation from start to finish. It encompasses all of a system’s processes.
Business intelligence is an element that contributes to a system’s operational efficiency
enhancement [
14
,
30
,
40
]. Numerous organisations are increasing their operational efficiency
through the use of business intelligence [
15
]. We are living in a data-driven era where
enterprises are implementing cutting-edge solutions to make the most of this data. With
the technological spectrum extending so far, enterprises must adapt to the environment
and stay ahead of the curve [
45
]. As a result, they are hard at work building cutting-edge
business intelligence solutions. These solutions typically involve software that enables the
creation of value from big data [43,46].
Each organisation has a few unique measures that are used by business intelligence
systems to analyse past and current data in order to derive insights and forecast the
future [
22
,
44
]. This forecasting enables businesses to develop a strategy for future use.
Every day, technology advances. It is critical for organisations to monitor these changes
and upgrade their technological capabilities. As a result, a wise, efficient, and effective
business intelligence system is required to deal with the current circumstances. There are
numerous ways in which business intelligence can benefit a bank [
2
,
40
]. It advocates for the
establishment of a robust customer relationship management policy. This method makes it
simple to decipher customer behaviour and purchasing habits. It enables CEOs to make
sound judgments. Additionally, business intelligence assists in cost reduction identify
new business prospects, and identify underperforming areas of business. Efficiency in
operations does not come easily [
13
,
15
]. It takes a committed workforce and a well-thought-
out plan to identify process bottlenecks and influence all levels of a business. Business
intelligent solutions have a significant impact on all key aspects of a business, and so have
the potential to improve operational efficiency as well [3,4,47].
According to recent bank systems and technology research, numerous banks and other
financial institutions in the United States could benefit from installing a business intelli-
gence system [
14
,
40
]. “Banks aim to leverage customer-level data on product holdings,
channel activity, and profitability to improve the targeting of online advertising and to

FinTech 2023,2104
streamline and automate the account application and funding procedures”, Chandrasekhar
and Sonar [
45
] stated. By analysing organisational data with a business intelligence solu-
tion, banks can keep improving and streamline operational efficiencies, allowing them to
not only strengthen sales and marketing strategies and develop better customer service
programmes, but also mitigate risk through the development of more appropriate risk
mitigation mechanisms. A global banking survey conducted by KPMG found a rise in both
the volume and the value of financial frauds [
17
,
19
]. This has elevated fraud prevention
and detection to the top of every bank’s priority list. As a result, when a credit union
bank situated in Canada approached Rishabh Software to design a comprehensive fraud
management system, they supplied an enhanced risk prevention business intelligence plat-
form [
15
]. It enables the client to process 1 million transactions per second with absolute
precision. The solution also made payment processing easier, alerted businesses to take
action before something bad happens, monitored in-process transactions in real-time, and
blocked deceitful credit cards and payouts in real-time [
6
,
11
,
43
,
45
]. This study postulates
the following alternative hypothesis:
H1: Business intelligence enhances bank’s operational efficiency.
2.3. Business Intelligence and Perceptions of Bank’s Profitability
The profitability of a bank serves as an indicator of the bank’s success [
10
,
40
]. In order
for a bank to make money, it must earn more money than it spends. Most of a bank’s
earnings come from service charges and interest collected on its assets, both of which are
major sources of profit [
2
]. It is possible to measure the success of a bank by gauging its
operational efficiency, as well as its ability to diversify its revenues through non-interest
income activities and cost management, by using profitability-based measurement [
15
].
As the banking system becomes more complex and integrated, so does the range of risk
variables. Banks must certainly focus their efforts on reducing fraud as a top concern. It is
critical to keep an eye out for unusual activity on your checking or credit card accounts [
9
].
The danger of lawsuits and embezzlement can be reduced by monitoring employee activity
for unusual transactions, withdrawals, expenses, and lending. Keeping track of past dues
and repayments may reveal general trends, such as a downturn in the economy. Retaining
current consumers is a very profitable and long-lasting business strategy, making business
intelligence tools one of the most valuable assets available. A bank will be able to market
the most relevant products and services to consumers’ requirements and preferences if
it has the most up-to-date information about its clients. Information can be gathered by
banks to determine which products require improvement and which can be retired [2,10].
The banking and finance industries have jumped on the personalisation bandwagon
quickly. A competitive advantage is essential because of this. Personalising consumer
interactions is easy with business intelligence tools and the data you currently have [
9
,
48
].
Market trends may be monitored to identify new investment opportunities, customer
behaviour can be predicted using analytics, and products can be customised to meet the
specific demands of each client. Customer relationship management (CRM) data can reveal
the profitability of marketing operations [
49
]. Measuring email performance, advertising
expenditure, and overall campaign success may help banks identify areas where their
messages are resonating with customers and possibilities to enhance them. In order to
estimate the success of prospective cross-selling efforts, BI systems can be used to conduct
win-loss data analysis [
1
]. Cross-selling insurance products was a goal of a financial
services company in Asia. For this, they required a smart system that could analyse CRM
data, detect customer trends, and identify the customers most likely to convert based on
previous purchases of other products [
16
]. The customer-designed business intelligence
and analytics support desk facilitates the generation of Excel-based analytics reports and
the identification of potential customers most likely to convert based on their purchasing
behaviour and profile [
45
]. Increased revenue and lower costs for pricey statistical tools
were achieved by using this method [4].

FinTech 2023,2105
Tracking individual income streams using BI solutions helps banks identify profitable
products and services and those that are not profitable [
24
]. However, the advantages do not
end there. Financial institutions can also employ business intelligence systems to analyse
massive amounts of client data in order to learn more about their customers’ banking
needs and attitudes, which they can then use to improve their products and services [
50
].
Using an example, it may be found out that customers are looking for a more efficient
method of tracking and analysing their income and expenditures. Customers may wish
receive more timely alerts from institutions [
6
], or they want an application and funding
process that is simpler and less time-consuming. Organisations can gain a competitive
edge by using these types of information to produce new and enhanced financial goods
and services that better satisfy client needs [
27
,
29
]. Credit card fraud is one of the most
common types of fraud that banks are able to detect and prevent because of the capacity
to trace client transactions. Monitoring internal communications and trading behaviour
helps companies comply with new regulatory frameworks resulting from the 2008 financial
crisis and recent insider trading instances [
9
,
31
,
51
]. Global banks may be able to better
estimate credit risk for counterparties in all asset classes if data from previously isolated
systems can be accessed. Another risk mitigation benefit of BI is the ability to accurately
estimate the risk of client loans based on crucial parameters like the borrower’s earning
capability and present financial assets, as well as fresh data sets and the current economic
climate [
52
]. Delinquency cases can be detected early with the help of BI technologies, and
prompt action can be taken to avoid them.
According to Acharya, Engle Iii [
53
], stock prices of banks crashed due to higher capital
buffers during COVID-19. COVID-19 negatively affects the profitability of banks [
54
,
55
].
During the pandemic, bank operations were hampered seriously due to lockdown, and
thereby banks were less efficient in operation [
56
]. The COVID-19 pandemic and its global
effects on practically every sector, including the healthcare system, international trade,
capital and financial markets, and the banking industry, is an unprecedented phenomenon.
Boubaker, Le [
57
] projected a 3.1% decline in the global economic growth rate and an 8.2%
decline in global trade volumes in 2020 due to COVID-19. COVID-19 created extraordinary
shocks in several areas, including the labour supply, the equity risk premia of economic
sectors, the cost of manufacturing, consumer demand, bank’s efficiency, bank’s profitability,
and government spending [54–56,58].
It is possible to visualise a customer experience strategy through the use of BI and
analytics tools in finance [
11
,
59
]. Such a strategy would improve targeted products and ser-
vices, tailor marketing campaigns, remain on top of the competition, and as a consequence,
drive profitability with the correct data processing. Tracking various revenue streams can
also reveal which items and services do not resonate with customers and which are more
profitable for the business [
7
,
10
,
60
]. Traditional financial services remain important, but
the sector is facing significant challenges due to the expansion of big data, more competi-
tion, and increased client digital expectations in every area of wealth management. From
improved internal operations and transparency to increased connection and individualised
service offers, BI applications in finance offer a number of feasible motivations for future
digital experiences in financial management [
8
,
25
,
26
]. This study postulates the following
alternative hypothesis:
H2: Business intelligence improves the profitability of banks.
2.4. Operational Efficiency and Perceptions of Bank’s Profitability
Every industry may profit from data that are easy to grasp and apply to real-world
decision-making [
61
]. Understanding the vast amounts of data available across the banking
and finance industries is no small task. Manually completing this task would be exhausting,
daunting, and take a significant amount of time. Obtaining a complete picture of your
customers and business can be difficult no matter how big or small your company is, given
how much data is scattered across numerous applications and services [
62
]. A single data
aggregate is a must-have in order to benefit from business intelligence methods. As a

FinTech 2023,2106
result, organisations are turning to software to help them evaluate and extract value from
large amounts of data. A bank’s internal organisation’s effectiveness is just as critical to
the company’s success as the experience its customers have with it [
5
,
18
,
21
]. To evaluate
a bank’s resources, procedures, and staff, business intelligence software provides a data-
backed method. In order to save money, improve customer service, and increase operational
efficiencies, banks can use business data.
Global data production now exceeds 2.5 billion gigabytes each day [
12
]. Financial
institutions can gain a competitive advantage by putting their data to use [
7
,
14
,
15
,
20
,
23
]. It
is also possible to bring together data from diverse applications using business intelligence
tools, creating a single source of information that can be used by everyone in the bank.
Competition, risk management, and changing client expectations are some of the issues
faced by the BFSI industry [
29
]. In order to gain valuable insights from customer data, they
use BI technologies. Assisting in the analysis of trends, finding patterns, and providing
real-time reporting are some of the functions of these tools. They may make use of BI’s
adaptability and transparency to enhance their financial operations and decisions. In order
to remain relevant in the banking industry, it is essential that banks evolve along with
their customers’ needs [
12
,
19
,
46
]. To provide meaningful insights, BI solutions analyse
and correlate market patterns with the data. This comprises data about the habits, wants,
and preferences of individual customers. By making it easier to manage data, banks can
provide better financial services.
Throughput volume, service delivery cycle time, mistake rates, and customer satis-
faction surveys (CSATs) are just a few of the metrics used by business intelligence in the
financial services industry to keep tabs on various departments and workers [
17
]. Organi-
sations can gain a better understanding of their operations by using this data. Let me give
you an illustration of what I mean. A bank’s business intelligence is used to understand
what customers want, and how their personnel can provide those needs [
14
]. Thus, banks
can provide a superior level of service to their clients. Pieket Weeserik and Spruit [
11
]
argued that using visual dashboards, banks can provide data visualisation services that
assist in making better decisions based on visual information. They aid in spotting patterns,
tracking corporate goals, and comparing the performance of different categories-products
and services. Financial institutions now have access to real-time, actionable data in a variety
of areas such as sales, cross-selling, and regulatory compliance [
9
,
60
]. The use of BI tools
allows financial institutions to better understand why their consumers leave them for their
competitors. What the clients desire is an easier way to measure income and spending. It
is possible to improve client retention and loyalty by improvising and providing better
products and services [
22
]. Customer segmentation, cross-selling and upselling methods,
and customer sentiment research all contribute to a seamless customer experience. In this
way, the profitability of banks can be affected by the operational efficiency through BI [
15
].
It is possible to uncover consumer behaviour patterns and potential system obstruc-
tions through the use of business intelligence (BI) in the banking sector [
45
]. These tech-
niques provide proper knowledge on resource utilisation, teller performance, counter use,
and wait times to receive a real-time snapshot of appointments with real-time visibility of
operations. In order to reach the optimum goals, banks will need to use BI tools’ visual
signals [
46
]. Data can be filtered based on a variety of criteria like geographic location,
bank branch, product or service offerings, and the type of transaction. Failure to adapt to
changes in the market and new regulations can have a negative impact on a company’s
profitability. Banks can lower the risk of losses and operational risks by establishing a new
risk-reporting system that includes data aggregation, workflows/data quality management,
and the use of technology [
3
,
5
,
40
]. Organisational change requires new technologies to help
financial service providers close efficiency gaps while also assisting with strategic decisions
in an ever-more-competitive market [16].
Money has never been easy to deal with, and it is even more difficult now because
financial services providers need to coordinate the organisation in order to create re-
siliency in their business models. One cannot do it on one’s own; it requires a well-oiled

FinTech 2023,2107
machine [4,14]
. When used with KPI dashboards and metrics correlations, BI tools make it
possible for the system’s constant exchange of transactional information between users, as
well as for unified data sharing and automatic manual reporting [
13
]. The ability to manage
performance is a well-known benefit of BI tools. Every aspect of your company’s success,
including operational procedures, team productivity, customer management patterns, tech-
nological efficiency, and so on, maybe quantified in a single score by the data you keep in
your system. This makes it possible to assess the overall health of the organisation and the
efficiency of each operational procedure [
5
]. The linked and customised world has made
money management a commodity. It is critical to swiftly and accurately analyse the vast
amounts of data generated by BFSI (banking, financial services, and insurance) services [
14
].
Real figures can assess where the business is, discover value drivers and development
prospects, and then monitor financial/non-financial KPIs against those. The potential of BI
technology is data on demand. Real-time data handling is simplified and expedited with
a well-implemented BI solution [
2
,
22
]. Analysis of investments and profitability across
different dimensions of a financial business (products, customers, services, and channels)
can be used to further strategize on valuation or growth optimization [
11
,
18
]. As a result,
financial institutions now have solid evidence to support future go-to-market strategies and
enhanced financial services in general [
1
,
3
,
5
,
31
,
61
,
63
]. This study postulates the following
alternative hypothesis:
H3: Operational efficiency improves the profitability of banks.
This study reviewed the existing studies and developed Table A1 (see Appendix A).
3. Research Methodology
The sample of the study consists of 27 branches out of 38 branches of a commercial
bank in Bangladesh. Hair, Hult [
64
] stated that a simple random sampling method ensures
the unbiased selection of samples. Thus, the study applies simple random sampling
in selecting all the branches. A random number generator (https://www.random.org/
accessed on 2 January 2022) has been used to ensure the random selection of the branches.
The researchers collected contact numbers from the website of the bank (https://www.
sonalibank.com.bd/ accessed on 25 November 2021) and communicated over the mobile
phone. While 32 branches agreed to participate in the study ultimately only 27 branches
provided us a proper timeline to proceed with them. As Alvarez, Núñez-Cortés [
65
] argued
that a 10% sample is appropriate for a PLS-SEM analysis, we reached the range of 71%
(27/38). We spread 10 questionnaires aimed at manager (1), senior officers (3), general
officers (3), and employees (3). Thus, the total respondents should be 270 (27
×
10) but
we found 7 blank and 4 partly complete questionnaires. The study uses 259 (270
−
11)
responses in the main analysis where the response rate is 96% (259/270).
The study followed a web-based survey to distribute the questionnaire to the bank
branches as Dillman, Smyth [
41
] stated that this method allows collecting data from a
large sample over a dispersed area with a relatively lower cost but higher speed. The
study applied the web and mobile survey guidelines of Dillman, Smyth [
41
] to design
and implement the survey. We used a seven-point Likert scale that ranges from seven for
“strongly agree” to one for “strongly disagree”. For the administering purposes, the study
adopted the pretesting method of Mokhtar, Jusoh [
66
]. We pretested the questionnaires with
two accounting lecturers and two cost-and-management accountants. Then, we revised the
questionnaires as per the pretesting inputs. We further modified the questionnaires while
piloting them with ten known practicing accountants. The study followed the research
ethics and guidelines as per the institutional review board. The profiles of the respondents
and banks are shown in Table 1.

FinTech 2023,2108
Table 1. Profiles of bank and respondent.
Categories Variations Freq. %
Participants’ Profile (Total 259 Respondents)
Gender
Male 168 64%
Female 91 36%
Total 259 100%
Age
Less than 30 years 83 32%
30–45 years 148 57%
More than 45 years 28 11%
Total 259 100%
Designation
General Manager 25 10%
Senior officers 74 29%
General officers 81 31%
Employees 79 30%
Total 259 100%
Bank’s Profile (Total 27 Branches)
Operating years (Bank Age)
Less than 10 years 6 22%
10–20 years 13 48%
More than 20 years 8 30%
Total 27 100%
No. of Employees
(Bank Size)
Less than 25 7 26%
25–40 9 33%
More than 40 11 41%
Total 27 100%
Source: Author’s calculation based on collected data.
There are two sections of survey instruments. Profiles of the banks and participants
are covered in section one and section two includes statements of business intelligence,
operational efficiency, and bank profitability. The study adopted three items from Nithya
and Kiruthika [
6
], two items from Lim, Chen [
18
], and three items from Ranjan [
25
] to mea-
sure the business intelligence (BI) variable. Then, to measure bank’s operational Efficiency
(OE) variable, the study adopted three items from Lim, Chen [
18
], three items from I¸sık,
Jones [
28
], and one item from Olszak [
29
]. Finally, adopting two items from Nithya and
Kiruthika [
6
], one item from Richards, Yeoh [
63
], two items from Yiu, Yeung [
31
], two items
from Owusu [
51
], and two items from Bordeleau, Mosconi [
16
], we measured Bank’s Prof-
itability (BP). This study considers two control variables branch size (number of employees)
and branch age (years of operation) as past studies argued that bank size and bank age may
affect the relationships of profitability, operational efficiency, and business intelligence.
The study used the partial least square structural equation model (PLS-SEM) to test the
relationships of the study model. According to Hair, Matthews [
67
], Sarstedt, Hair [
68
], and
Shmueli, Sarstedt [
69
], using PLS-SEM is recommended due to the fact that it is best suited
for testing hypotheses and is advanced enough to test theories as well as the goodness
of fit criterion, and because it is also competent in examining the connections between
multiple latent variables at the same time. PLS-SEM was an excellent choice for analysing
the data from this study because we constructed a conceptual model (see Figure 1) that
consisted of several variables for testing multiple associations, making it a good fit for
the data. Several aspects of model reliability and validity, including convergent validity
and discriminant validity, non-response bias and common method bias, the goodness of
fit, model performance and hypothesis testing, as well as a robustness check, have been
discussed in greater depth in the analysis section.
4. Analysis and Results
4.1. Measurement Model
Table 1represents the profile of banks and participants. A bank’s profile includes both
bank age and bank size while participant’s profile shows gender, age, and designation.
Of the respondents who filled out the questionnaires and showed interest in the study
objectives, 64% were male, and 36% were female. In the case of the age level, 32% of

FinTech 2023,2109
participants were less than 30 years old, 57% participants were 30–45 years, and 11% of
participants were older than 45 years. Thus, it is noticeable that most of the participants
were from 30–45 years of age, and the next largest group was below 30 years of age. Fur-
thermore, respondents were asked about their positions in the banks. 10% of respondents
were general managers, 29% were senior officers, 31% were general officers, and 30% were
employees. In the case of bank profiles, 22% of banks were less than 10 years old, 48% were
10–20 years old, and 30% were more than 20 years old. Furthermore, 26% of banks have
less than 25 employees, 33% have 25–40 employees, and 41% have more than 40 employees.
To test the reliability and validity of the constructs, the researchers conducted the
measurement model analysis presented in Table 2. Table 2shows the latent constructs and
their measurement items. The measurement model analysis includes the mean, standard
deviation (SD), factor loadings (FL), Cronbach’s alpha (
α
), rho-value, composite reliability
(CR), and average variance extracted (AVE). According to Table 2, the Cronbach’s alpha
for each construct is greater than 0.80, as Hair, Hult [
64
] suggested to ensure the reliability
and internal consistency of the constructs. Furthermore, following the suggestions of
Hair, Hult [
64
], Shmueli, Sarstedt [
69
], and Sarstedt, Ringle [
70
], the researchers reported
rho values that are greater than 0.81 for each construct. The internal consistency of the
measurement scales through composite reliability (CR) has also been tested. The CR values
of each scale are 0.865 for BI, 0.885 for OE, and 0.913 for BP, as Hair, Hult [
64
] and Shmueli,
Sarstedt [69] suggested.
Table 2. Laten variables and measurement statements.
Code Constructs and Items Mean SD FL * αrho CR AVE
BI Business Intelligence 4.418 1.035 0.82 0.82 0.87 0.55
BI1
“Our bank effectively uses spreadsheets as a
business intelligence to model and manipulate bank
data”
4.220 1.319 0.839
BI2 “Our bank visually appeals graphical
representations to quickly gain insights.” 4.831 1.271 0.932
BI3 “Our bank uses online platform to communicate
clients” 4.198 1.014 0.844
BI4 “Our bank uses a dashboard of quick metrics
designed to support better decisions” 3.401 1.782 0.632
BI5 “Our bank stores data of all departments in a data
warehouse” 5.108 1.281 0.732
BI6 “Our bank uses big data in strategic and tactical
decision-making processes” 4.403 1.294 0.742
BI7 “Our bank uses business intelligence for an
analytical querying of the prepared data” 4.173 1.290 0.848
BI8
“Our bank uses business intelligence to prepare key
performance indicators to the clients” 5.319 1.371 0.826
OE Operational Efficiency 5.502 1.189 0.85 0.87 0.89 0.53
OE1 “Our bank simplifies operations through business
intelligence tools” 4.948 1.014 0.758
OE2 “Our bank enhances process consistency by
business intelligence tools” 4.482 1.734 0.812
OE3 “Our bank assures timely, accurate, and relevant
user information by business intelligence tools” 5.264 1.290 0.825
OE4 “Our bank assures customer satisfaction through
efficient operational functions” 4.037 1.017 0.738
OE5
“Our bank is providing secured services by business
intelligence” 5.129 1.873 0.794
OE6 “Our bank operates functions with lower costs” 4.672 1.701 0.863
OE7 “Our bank operates functions with reduced risks” 5.112 1.939 0.882
BP Bank’s Profitability 5.016 1.004 0.86 0.86 0.91 0.70
BP1 “Our bank makes more profit after adopting
business intelligence” 4.839 1.187 0.803

FinTech 2023,2110
Table 2. Cont.
Code Constructs and Items Mean SD FL * αrho CR AVE
BP2
“Our bank generates more customer margin through
cross-selling strategy of business intelligence” 5.851 1.871 0.684
BP3 “Our bank improves net interest margin through
business intelligence adoption” 5.382 1.193 0.739
BP4 “Our bank improves return on assets through
business intelligence adoption” 5.041 1.173 0.918
BP5 “Our bank improves return on investment through
business intelligence adoption” 4.582 1.276 0.832
BP6 “Our bank assures potential profitability by
improving data analytical capabilities” 4.423 1.103 0.851
BP7 “Our bank improves return on equity through
business intelligence adoption” 5.146 1.126 0.943
BP8 “Our bank improves profitability through reducing
fraudulent activities” 4.605 1.869 0.674
BP9 “Our bank increases sales through business
intelligence adoption” 4.582 1.158 0.814
Note: SD = standard deviation, FL = factor loading, * All indicators are significant at p< 0.01. Source: Developed
by the author based on Smart PLS output.
According to Saunders, Lewis [
71
] and Sarstedt, Ringle [
72
], average variance extracted
(AVE) is a measure of the variation collected by a construct in comparison to the variance
attributable to measurement error. As a general rule, and in order to ensure appropriate
convergence, an AVE of at least 0.50 is strongly suggested [
64
,
71
,
72
]. According to Hair,
Hult [
64
], an AVE of less than 0.50 indicates that the survey items account for more mistakes
than the variance in the survey components. For each construct in any measurement model,
an AVE must be determined and must be at least 0.50 [
64
,
69
]. In the case of this study, the
AVE values for all constructs are greater than 0.50 (see Table 2).
The factor loading value of each item is greater than 0.70 except for BI4 (0.63),
BP2 (0.68), and BP8 (0.67). Sarstedt, Ringle [
72
] argued that factor loadings greater than
0.70 are acceptable for SEM estimations while Hair, Hult [
64
] suggested not to take a value
less than 0.60 for factor loading of measurement items for a path analysis. As the study
achieved better values in CR, AVE,
α
, and rho-value, and more than 0.60 for the factor
loading, BI4, BP2, and BP8 were not removed from the study statements.
Shmueli, Sarstedt [
69
] defined discriminant validity as the statistical difference be-
tween two latent variables representing distinct theoretical conceptions, and it is needed
in PLS-SEM path analysis. According to Tables 3and 4, discriminant validity has been
established as the Fornell-Larcker criterion, heterotrait-monotrait Ratio (HTMT), and cross-
loadings met the threshold value. According to Fornell and Larcker [
73
], the squared
correlations of the latent constructs should be between the squared root of AVE, and this
study met the criterion as well (see Table 3). Shmueli, Sarstedt [
69
] and Hair, Hult [
64
]
argued that HTMT is a measure of how similar two latent variables are. To demonstrate
discriminant validity, the HTMT must be clearly less than one. In the case of this study, the
HTMT value is less than 1; thus, discriminant validity has been established.
Table 3. Discriminant validity with HTMT.
BI OE BP
BI 0.742 0.541 0.440
OE 0.403 0.728 0.563
BP 0.528 0.462 0.836
Diagonal values: “Square root of AVE”. Below the diagonal: Correlation matrix. Above the
diagonal: HTMT values.
Source: Self-developed based on PLS output.

FinTech 2023,2111
Table 4. Cross loadings.
BI OE BP
BI1 0.839 0.371 0.217
BI2 0.932 0.469 0.362
BI3 0.844 0.418 0.316
BI4 0.632 0.416 0.303
BI5 0.732 0.311 0.167
BI6 0.742 0.429 0.250
BI7 0.848 0.370 0.278
BI8 0.826 0.417 0.278
OE1 0.367 0.758 0.382
OE2 0.275 0.812 0.386
OE3 0.461 0.825 0.324
OE4 0.382 0.738 0.276
OE5 0.223 0.794 0.425
OE6 0.268 0.863 0.424
OE7 0.219 0.882 0.461
BP1 0.204 0.217 0.803
BP2 0.417 0.276 0.684
BP3 0.273 0.305 0.739
BP4 0.312 0.231 0.918
BP5 0.427 0.412 0.832
BP6 0.276 0.380 0.851
BP7 0.317 0.423 0.943
BP8 0.206 0.427 0.674
BP9 0.349 0.322 0.814
Source: Author constructed based on PLS output.
4.2. Model Fitness Measures
The researchers checked the model fitness through f squared, R squared, adjusted R
squared, standardized root mean square residual (SRMR), normed ft index (NFI), and root
mean square error of approximation (RMSEA). Sarstedt, Ringle [
72
] assert that f-squared
measured variance adequately explains each independent variable in the equations. The
effect size of the f-squared needs to be more than 0.35, indicating larger effects. In the case of
this study, the f square values are greater than or equal to 0.40 (see Table 5), indicating that
the exogenous variables explain the endogenous variables with a larger effect. Following
previous studies [
64
,
69
,
74
], the path model’s goodness-of-fit is measured using the R-
squared value. R-squared is a statistical measure of how near the data are to the path line
that has been fitted to the data set [
69
]. In a path model, the R-squared statistic reflects how
much variance in a dependent variable can be explained by the independent variables [
64
].
The value of R squared should be closer to 1 [
64
,
69
]. According to Hair, Hult [
64
], a value of
at least 0.25 for R-squared should be found for the path model to explain the influence of the
exogenous variables. In the case of this study, the values of R square are 0.624 (indicating
the relationship between BI and OE) and 0.772 (representing the relationship of BP through
BI and OE). Shmueli, Sarstedt [
69
] state that R-squared must be adjusted when using a
regression model with multiple independent variables. Considering the concept of Shmueli,
Sarstedt [69], the researchers reported adjusted R-squared as well in Table 5.
Table 5. Model fit measures.
Variables f Square R Square Adj. R Square SRMR NFI RMSEA
BI
OE 0.531 0.624 0.609 0.045 0.93 0.049
BP 0.428–0.583 0.772 0.764 0.045 0.93 0.049
Source: Self-developed based on PLS output.

FinTech 2023,2112
Furthermore, the researchers represent the values of SRMR, NFI, and RMSEA as
past studies [
36
,
64
,
69
,
72
,
74
] suggested reporting. According to Hair, Hult [
64
], Shmueli,
Sarstedt [
69
], and Sarstedt, Ringle [
72
], the threshold values of SRMR should be less than
0.08, RMSEA should be less than 0.06, and NFI should be greater than 0.80. According to
Table 5, the SRMR values are below 0.05, and the RMSEA values are below 0.06, and the
NFI values are above 0.80. Thus, the model is best-fitted.
4.3. Hypothesis Testing
Table 6and Figure 2represent the path analysis and structure model output, respec-
tively. The study finds that business intelligence (BI) positively affects the operational
efficiency (OE) of banks, which is significant at a level of significance less than 0.001.
The beta coefficient of the BI and OE (BI
→
OE) relationship is 0.374, and the t-value
is 15.165. Thus, Hypothesis 1 (H1) is supported. This indicates that when practices of
business intelligence increase in banks, the operational efficiency of banks is improved.
Thus, banks can enhance their operational efficiency by 0.374% through advancing their
business intelligence by 1%.
Table 6. Analysis of path.
Relationship Coeff. (β)t-Value p-Values VIF Decision
Direct Effect
BI →OE 0.374 15.165 0.000 *** 1.114 H1 supported
BI →BP 0.369 12.486 0.000 *** 1.217 H2 supported
OE →BP 0.521 27.328 0.000 *** 1.125 H3 supported
Mediating Effect
BI →OE→BP 0.133 3.127 0.023 ** 1.211
Partial Mediation
*** significant at <1% and ** significant at <5%. Source: Smart-PLS output.
In the case of the relationship between business intelligence (BI) and bank’s profitability
(BP) (BI
→
BP), the beta coefficient is 0.369, including a t-value of 12.486, which is also
positively significant at a significance level of less than 0.001 (see Table 6). This indicates
that the greater the application of business intelligence in banks, the higher the profitability
of banks. Thus, Hypothesis 2 (H2) is supported. Therefore, it can be said that bank’s
profitability can be improved by increasing the application of business intelligence in banks.
Finally, the study tested whether the operational efficiency of banks affects bank’s
profitability. The beta coefficient of the relationship (OE
→
BP) between OE and BP is 0.521,
with a t-value of 27.328 which is also positively significant at a level of significance of less
than 0.001 (see Table 6). Thus, Hypothesis 3 (H3) is also supported. The VIF values are
below the threshold value of 3.3 indicating that issues of multicollinearity are absent in the
path model [
64
]. This study employs branch size and branch age as control variables that
may affect the relationships of profitability, operational efficiency, and business intelligence.
Figure 2shows that the branch size and branch age are not significant throughout the
structural relationships.
Furthermore, the findings of the study suggests that operational efficiency (OE) par-
tially mediates the relationship between business intelligence (BI) and bank profitability
(BP). This means that the effect of BI on BP is not entirely direct but is also partially ex-
plained by the improvement in OE due to the implementation of BI. In other words, when
banks improve their BI practices, it not only directly increases their profitability but also
indirectly increases it through improved operational efficiency. This finding highlights
the importance of operational efficiency as a mediator between BI and BP and suggests
that banks should focus on both BI and OE to improve their profitability. Overall, the
study’s findings suggest that business intelligence can positively affect a bank’s operational
efficiency and profitability, and these effects can be partially explained by the mediating
role of operational efficiency.

FinTech 2023,2113
FinTech 2023, 2, FOR PEER REVIEW 15
BI → OE→ BP 0.133 3.127 0.023 ** 1.211 Partial Mediation
*** significant at < 1% and ** significant at < 5%. Source: Smart-PLS output.
Figure 2. Author’s calculation.
In the case of the relationship between business intelligence (BI) and bank’s profita-
bility (BP) (BI → BP), the beta coefficient is 0.369, including a t-value of 12.486, which is
also positively significant at a significance level of less than 0.001 (see Table 6). This indi-
cates that the greater the application of business intelligence in banks, the higher the prof-
itability of banks. Thus, Hypothesis 2 (H2) is supported. Therefore, it can be said that
bank’s profitability can be improved by increasing the application of business intelligence
in banks.
Finally, the study tested whether the operational efficiency of banks affects bank’s
profitability. The beta coefficient of the relationship (OE → BP) between OE and BP is 0.521,
with a t-value of 27.328 which is also positively significant at a level of significance of less than
0.001 (see Table 6). Thus, Hypothesis 3 (H3) is also supported. The VIF values are below the
threshold value of 3.3 indicating that issues of multicollinearity are absent in the path model
[64]. This study employs branch size and branch age as control variables that may affect the
relationships of profitability, operational efficiency, and business intelligence. Figure 2 shows
that the branch size and branch age are not significant throughout the structural relationships.
Furthermore, the findings of the study suggests that operational efficiency (OE) partially
mediates the relationship between business intelligence (BI) and bank profitability (BP). This
means that the effect of BI on BP is not entirely direct but is also partially explained by the
improvement in OE due to the implementation of BI. In other words, when banks improve
their BI practices, it not only directly increases their profitability but also indirectly increases
it through improved operational efficiency. This finding highlights the importance of opera-
tional efficiency as a mediator between BI and BP and suggests that banks should focus on
both BI and OE to improve their profitability. Overall, the study’s findings suggest that
Figure 2. Author’s calculation.
5. Discussion
This study found that the application of business intelligence improves the operational
efficiency of banks, indicating that a 1% increase in business intelligence implementation
improves the operational efficiency of a bank by 0.374%. Data analysis at the local level is
substantially facilitated by business intelligence, which assures a high level of accuracy [
22
].
Each branch’s cash flow, personnel composition, and urgent needs can be assessed swiftly
and separately. As a result, it is an effective tool for assuring, for example, that every
bank branch has a healthy financial situation. Predictive analytics added to BI makes it a
powerful tool for increasing branch efficiency by automating formerly manual processes [
5
].
Improved data management also makes teams more available, allowing them to concentrate
on their primary business tasks on a daily basis. The promise of BI is that technology
would help organisations become more responsive and flexible, allowing them to take
advantage of new opportunities and innovate in a highly competitive market [
6
]. Among
other things, it promises to enable enterprises to analyse and exploit massive volumes of
heterogeneous data in a more efficient and precise manner. Connecting separate systems in
banking eliminates the need to manually prepare reports for each one. The use of business
intelligence in banking enables institutions to collect unprecedented amounts of data on
their consumers, allowing them to better serve their clients [
23
]. With banking BI, banks
may gain a better understanding of their consumers, allowing them to handle issues before
they arise. Business intelligence in banking eliminates the need to manually wrangle data
by connecting directly to core system databases [
23
,
63
]. Decision-makers will be able to
acquire a competitive advantage by implementing a BI solution that is company-wide.
Making decisions based on data increases the likelihood that those decisions will be correct,
as the element of guesswork is eliminated [
63
]. Everyone will be happier, wealthier, and
wiser as a result of business intelligence. The findings, thus, create insights into how the
banking industry can improve its operational efficiency.

FinTech 2023,2114
Furthermore, the study found that business intelligence increases the profitability of
banks, indicating that a bank’s profitability increases by 0.369% if there is a 1% increase
in business intelligence implementation. When companies are able to swiftly identify and
act on critical operational data, they are more likely to increase their selling efficiency and
profit margins. The good news is that most banks can afford the business intelligence (BI)
solutions required to facilitate this data analysis [
63
]. When the underlying analysis is
supported by the correct data, the forecasting load can be greatly reduced and the forecast’s
reliability greatly increased. With the help of business intelligence (BI) software, managers
have quick and easy access to historical sales data [
68
]. The ability to quickly and easily
access sales data from the past helps improve forecast accuracy as well as procurement and
inventory decision-making.
Finally, the study found that operational efficiency increases the profitability of banks,
indicating that a 1% increase in operational efficiency of banks increases the profitability by
0.521%. The key to avoiding unpleasant month-end surprises is to recognize and act on
reliable information as soon as possible through BI. To stay on top of the latest company
news, sales teams in the modern-day rely on business intelligence (BI) solutions. In these
companies, managers and sales representatives are able to quickly comprehend the large
picture and then drill down to find specific areas of concern, such as individual goods,
accounts, and/or sales regions or representatives [
26
,
61
,
66
]. Organisations that put forth
the effort to achieve these sales and marketing objectives stand to gain much from having
the appropriate information structured in an efficient manner. The appropriate BI tools may
help organisations use their data in new and more efficient ways, regardless of whether or
not they already have the data they need. Current BI technologies are easy to adopt and
pay for themselves in a matter of months for many banks [
7
]. The findings, thus, create
insights into the banking industries to improve profitability.
This research is interesting and original because it provides empirical evidence on the
positive impact of business intelligence on the operational efficiency and profitability of
banks. The study uses a comprehensive approach to measure the reliability and validity of
the constructs and test the fitness of the structural model, which enhances the credibility of
the findings. Additionally, the study takes a unique perspective by applying two theories,
competitive theory and resource-based view theory, to support the argument that business
intelligence is a strategic resource that can provide a competitive advantage and lead to
superior performance over time.
Furthermore, this study is original in the sense that it was conducted on a sample
of 27 branches of a commercial bank, which provides insights into the effects of business
intelligence at the local level. This approach is different from previous studies that mostly
focus on the effects of business intelligence at the organisational level. The study’s findings
have practical implications for the banking industry, as it recommends the use of business
intelligence as a tool to enhance decision-making effectiveness and ensure competitive
advantage. Overall, this research contributes to the growing body of literature on the impact
of business intelligence on organisational performance, particularly in the banking sector.
It provides a unique perspective and empirical evidence on the benefits of using business
intelligence and offers practical implications for the banking industry’s decision-makers.
6. Conclusions
The purpose of the study is to examine the effects of business intelligence on bank
operational efficiency and perceptions of profitability. The study uses 259 responses from
general managers, senior officers, general officers, and employees of 27 branches of a com-
mercial bank in Bangladesh, employing a simple random sampling technique. The study
finds that business intelligence is positively significant to improve operational efficiency.
This finding is somewhat consistent with Tumpa, Saifuzzaman [
20
], I¸sık, Jones [
28
], Ol-
szak [
29
], and Lawrence [
30
] who conceptualizes BI in the same direction. The study finds
that business intelligence significantly increases the profitability of banks. This finding
also adds value to the studies of Arefin, Hoque [
21
], Biswas, Rahman [
24
], Ranjan [
25
],

FinTech 2023,2115
Elbashir, Collier [
17
], and Olszak [
29
]. Furthermore, the study reveals that operational
efficiency through business intelligence positively affects the profitability of banks. The
findings indicate that business intelligence systems can ensure competitive advantage
through improved operational efficiency and increased profitability.
Anecdotal evidence on the benefits of BI systems has been lacking until now, but this
study fills that gap with empirical evidence gleaned using a PLS-SEM technique. This
empirical evidence, which comes from a developing country, is critical because there is
a lack of research on the subject in the business intelligence literature. According to this
research, BI systems can improve both operational efficiency and profitability for banks by
implementing BI solutions. This has given managers and policymakers an understanding
of the importance of using a holistic approach when analysing the impact of IT, such as
BI systems, because of the intangibility of some of the benefits. The usage of business
intelligence (BI) technologies should also be encouraged by bank managers in order to
reap financial rewards in the long run. Vendors and other decision-makers in developing
nations could make use of the study’s empirical evidence to help raise awareness about BI
systems in these countries.
The findings of this study have significant theoretical implications, especially from
the perspective of the resource-based view (RBV) theory. According to the RBV, a firm’s re-
sources and capabilities play a vital role in achieving and sustaining competitive advantage
and superior performance. This theory posits that firms with unique and valuable resources
can gain a competitive advantage over their rivals. This study shows that business intelli-
gence can be viewed as a strategic resource for banks. The study indicates that when banks
use business intelligence, they can improve their operational efficiency, which positively
affects their profitability. Moreover, the study suggests that the application of business
intelligence can lead to the development of bank capabilities, which can ultimately lead to
superior performance over time. In other words, the findings of this study suggest that
business intelligence can be considered as a strategic resource that provides a foundation
for the development of bank capabilities, which can lead to a sustainable competitive
advantage and superior performance in the long run. This is an important contribution
to the literature on RBV theory, as it demonstrates the importance of business intelligence
as a strategic resource for banks, which can contribute to their long-term success and
competitive advantage.
This study has some limitations. First, the study targets the branches of a bank (single
bank but multiple branches); thus, the results may not be applicable to other banks as
the branches are regulated under the same regulatory framework. Second, this study is
cross-sectional, and thus, a future study may choose a panel data approach such as the
study of Salehi and Arianpoor [
75
]. Third, this research uses banks from Bangladesh, and
thus, the findings are not generalizable to other economies. Fourth, the study is based
on only quantitative data. Future studies may consider mixed-method approach to make
the findings more interesting and practical [
76
–
78
]. Fifth, this research could provide
more nuanced insights into the mechanisms through which different types of business
intelligence affect bank performance. Thus, future research should explore the effects of
specific types of business intelligence, such as data mining, predictive analytics, and data
visualisation, on operational efficiency and profitability in the banking sector [
79
]. Finally,
this research could shed light on the contextual factors that influence the effectiveness of
business intelligence in the banking sector, and could inform the development of more
tailored business intelligence strategies for different types of banks. Thus, future research
could investigate how organisational factors, such as organisational culture, leadership
style, and IT infrastructure, moderate the relationship between business intelligence and
bank performance.
Funding: This research receives no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.

FinTech 2023,2116
Data Availability Statement:
Data and materials are available upon reasonable request through the
corresponding author.
Acknowledgments:
The authors would like to acknowledge the support of employees and officers
of the commercial bank’s branches, who devoted time to filling and returning questionnaires sent to
them. We are also grateful to the data collectors. We are thankful to the colleagues who assist us in
this research.
Conflicts of Interest: The author declares no conflict of interest.
Appendix A
Table A1. Review of existing studies on BI, bank’s operational efficiency and profitability.
Authors Title Period Model/Method Findings
Arefin, Hoque [21]
The impact of business intelligence
on organisation’s effectiveness: an
empirical study
2015 PLS-SEM
BI improves organisational
effectiveness. Banks with BI are more
efficient than those without it.
Babu [12]
Artificial intelligence in Bangladesh,
its applications in different sectors
and relevant challenges for the
government: an analysis
2021 Qualitative
Application of artificial intelligence
maintain banks’ policy, information
security, regulations, and operational
effectiveness.
Bhatiasevi and
Naglis [13]
Elucidating the determinants of
business intelligence adoption and
organisational performance
2018 SEM BI is positively associated with bank’s
performance and internal processing.
Elbashir,
Collier [17]
Measuring the effects of business
intelligence systems: The
relationship between business
process and organisational
performance
2008 Qualitative BI systems enhance business process
and bank performance.
Fethi and
Pasiouras [43]
Assessing bank efficiency and
performance with operational
research and artificial intelligence
techniques: A survey
2010 Qualitative
Bank efficiency and performance have
positive associations with AI.
Nithya and
Kiruthika [6]
Impact of Business Intelligence
Adoption on performance of banks:
a conceptual framework
2021 Literature BI adoption has positive impact on
bank’s performance.
Owusu [51]
Business intelligence systems and
bank performance in Ghana: The
balanced scorecard approach
2017 PLS-SEM
BI systems are not directly associated
with bank performance but they have
indirect impacts.
Richards, Yeoh [63]
Business Intelligence Effectiveness
and Corporate Performance
Management: An Empirical
Analysis
2019 Mixed-method
BI has positive associations with
corporate performance management.
BI is strongly connected to planning
but less so to measurement.
Rouhani,
Ashrafi [8]
The impact model of business
intelligence on decision support and
organisational benefits
2016 PLS-SEM
BI has a strong positive impact on
bank benefits. Banks with BI can lead
effective decision support.
Wamba-Taguimdje,
Fosso Wamba [10]
Influence of artificial intelligence on
bank performance: the business
value of AI-based transformation
projects
2020 Qualitative
There is a positive association between
artificial intelligence and bank
performance.
Yiu, Yeung [31]
The impact of business intelligence
systems on profitability and risks of
banks
2005–
2014 Qualitative
BI increases bank profitability and
reduces risks. BI improves operational
efficiency.

FinTech 2023,2117
Table A1. Cont.
Authors Title Period Model/Method Findings
Kimble and
Milolidakis [42]
Big Data and Business Intelligence:
Debunking the Myths 2015 Qualitative Big data and BI improve decision
making effectiveness.
Varshney and
Varshney [38]
Workforce agility and its links to
emotional intelligence and
workforce performance: A study of
small entrepreneurial
2020 Qualitative
Emotional intelligence improves two
performances, i.e., adaptive
performance and contextual
performance but does not impact task
performance.
Source: Author’s construction.
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