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International Management Review Vol. 17 No. 1 2021
9
Applications of Artificial Intelligence on Customer Experience and Service
Quality of the Banking Sector
Meganathan Kumar Satheesh and Samala Nagaraj
Woxsen University, Hyderabad, India
sath3eesh@gmail.com
raajsamala.phd@gmail.com
[Abstract] The present article highlights the significance of what and how artificial intelligence (AI) and
its applications enhance the customer experience by elevating the quality of the service delivered by the
banking industry. A systematic study of the literature concerning the various emerging applications of
artificial intelligence and its impact on the banking sector is presented in this research paper. A thorough
review of the existing literature is systematically undertaken to discuss the applications of AI in banking.
Artificial intelligence certainly enhances the banking experience of millions of customers and employees
in the banking sector. AI facilitates various processes to reduce the employees’ workload by furnishing
credit score checking, system failure prediction, emergency alarm systems, fraud detection, phishing
website detection, liquidity risk assessment, customer loyalty evaluation, and intelligence systems. On the
other hand, customer experience is upgraded through diverse applications, namely, mobile banking,
chatbots, and augmented reality.
[Keywords] banking sector, artificial intelligence, customer experience, service quality
Introduction
The banking sector has been undergoing keen competition (Javalgi et al., 1989; Joshi et al., 2010; Fu et al.,
2014) because of the globalization of the world economy, which makes the banking environment unstable
(Joshi et al., 2010) and fragile (Berger et al., 2008). There are various stages involved in the banking
industry, from processing the customers’ loan applications to ensuring safe banking transactions for
customers until they maintain their services with the banks. Customers are looking for better service
wherever they collaborate with the products and services; in other words, a better customer experience is
the demand that the customers put forward. As the technology has evolved in the past decades, industries
have embarked on using state-of-the-art technology, namely, artificial intelligence, whereby superior
quality of service can be delivered to customers. The banking industry’s importance and its influence on
the country’s development are discussed in section 2; how artificial intelligence and its different
applications improve the processes involved in the banking industry will be discussed in section 3. The
conclusion will occur in section 4.
Background Study
Banking and Economy
Banking is a kind of exceptional industry that deals with capital to multiply money regardless of risk
(Ghodselahi & Amirmadhi, 2011). Banking institutions have a significant impact on the economy of a
country (Park, 2012) and, also, the financial stability and sustainable development (Gutierrez et al., 2009)
of a country. Thus, banks should scrutinize loan processing methods to segregate useful applications from
the total number of applications. A well-established loan application process will help the banks discourage
the sanction of loans to loss-generating projects that will lead to non-performing assets at a later point in
time and will enhance the process of the loan allocation to the right projects. Approving loans to non-
profitable projects will indicate a poor investment of resources, which affects the banks’ performance and
the economic growth of a country. Because providing loans is one of the banks’ critical functions, failing
in that core activity will severely affect the banks (Park, 2012).
Besides, banks have to lend the money to the borrowers to profit (Ince & Aktan, 2009) that will
contribute to the growth of financial activities, economic development activities, and industrial activities
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(Cetorelli & Gambera, 1999). At the same time, bank loan availability will drop significantly if the bank
crisis happens, which leads to a reduction in loan supply offered by the bank (Hubber, 2018). In India,
public sector banks possess more than three-fourth of total assets belonging to the entire banking sector.
India’s state bank has seventeen percent of the total commercial banking assets (Goldberg, 2009). Banking
institutions can perform effectively in providing loans to individuals and firms as per their demand if the
bank’s market share is quite large. When banks charge high-interest rates, plan to achieve high margins,
decide to decrease the loan supply, the economic growth and creation of jobs will be affected, and the
unemployment rate will be increased (Feldmann, 2015). In addition, if the entry barrier is high in the
banking industry, the initial expense will be higher. As a result, the high-interest rate will be charged to
make a profit, and foreign banks will hesitate to come (Claessens & Laevan, 2005).
Gross domestic product (GDP) is a measurement of economic development, the monetary value of
output (finished goods) of production of various industries within a country for a particular time (Atay &
Apak, 2013). The GDP estimation is profoundly affected when the banking industry output is exaggerated
(Oulton, 2013). Non-performing assets should be recovered to strengthen the banking industry in a stable
manner (Tan & Floros, 2012), which will contribute to economic activities.
The International Presence of Banks
With the expansion across the globe, banks have a crucial role in stabilizing a country’s economy during a
crisis (Goldberg, 2009). Less competition in the banking industry will make it operate at a high cost and
deliver poor quality of services, which reduces the need for outside finance and causes a decline in
industrial growth (Claessens & Laevan, 2005). Because banks have international branches, financial
systems are integrated across the globe. This international presence will make the transactions from one
country to another country effortless and fill the gap that is not served by local banks, making the banks
provide better customer service (Goldberg, 2009). Industries that depend on more outside financing will
have faster growth rates due to the competitive nature of the bank and finance institutions (Claessens &
Laevan, 2005). A banking institution is a significant source of funds for companies to gain external funds
from which they can run the business smoothly (Campiglio, 2016).
Banking Crisis
When depositors withdraw money from the bank due to the perception that the bank is untrustworthy, the
bank system will fail. Without deposits from customers, it is difficult for banks to run their business
irrespective of whether the situation is normal or a crisis (Kunt et al., 2000). Depositors always look for the
right functioning banks and higher interest rates to invest their money (Goldberg, 2009). During a crisis,
the bank loan issue will reduce drastically, and bank assets will drop, paving the way for a reduction in
output and investment growth. The crisis affects the growth of output volume in the year of crisis and the
following year. After a few years of crisis, the growth of output can be recovered, but the recovery may not
be possible for the banks’ credit within that timeframe concerning growth. Bank loans to borrowers will be
dropped significantly if a bank crisis happens, which leads to a reduction in loans supplied by a bank (Huber,
2018). The banking interest rate for deposits will be higher during the crisis and afterwards gain more
deposits and maintain the existing deposits (Kunt et al., 2000). The inflation rate is inversely proportional
to the country’s output, and it is observed that both the inflation rate and output growth are correlated
negatively (Haslag, 1995). Despite an increase in the interest rate, the interest rate should be higher than
the inflation rate to lure customers into making deposits. However, there is no significant difference in
interest rates before and after a crisis and no proof that banks have given a higher interest rate than the
inflation rate (Kunt et al., 2000). Banks that have maintained liquidity, even after depositors withdraw their
funds, will need help from the central bank and its authorities (Kunt et al., 2000).
Customer Experience and Service Quality
Customers are intrigued by availing themselves of newly launched products and services created by banks
to quickly execute the banking operations (Laketa et al., 2015). Based on the quality of service delivered to
the customers, the banks’ success is determined, and the bank’s differentiation from its competitors is
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identified. Customer satisfaction, which decides the survival and the success of the organization in the
competitive environment, is an essential indicator for evaluating the performance of the organization,
especially retail banking, which is dependent on customer loyalty to run its business profitably by luring
new customers and maintaining existing customers (Dahari et al., 2015). Despite the banks’ more
considerable efforts, most customers are not satisfied with banks’ banking services. Due to the growing
competition in the banking sector, banks have taken steps to enhance their service quality as per the
customer demand and to intensify the service in a reliable way (Johnston, 1997).
Nowadays, businesses should segment their customers to deliver the best service to them as per their
various needs, which helps to treat each customer effectively. Consumer behavior should be monitored to
understand the consumers well (Samala & Satheesh, 2020) and serve them better. The customer relationship
management methodology merges the marketing strategy with processes; functions performed within the
company and network connections outside the company are developed to maintain the existing customers
in the highly competitive market by ascertaining and understanding its needs. Banking institutions can
efficaciously capitalize on customer relationship management to serve the customers better if they focus
on four significant elements:
• preserving existing customers
• enticing new customers
• motivating customers to have profound collaboration with the bank and updating customers with
the banks’ new services (Laketa et al., 2015)
Additionally, the banking industry can gain more deposits from retail depositors if it treats them properly
(Puri & Rocholl, 2008).
Banking Process and Technology
Banking services, namely, automated teller machines (ATM), internet banking, credit cards, and banking
apps have been used by millions of customers every day, and, interestingly, the number keeps increasing
(Sundarkumar & Ravi, 2015). This kind of service needs a huge workforce, incurs a high cost, and is time-
consuming. Conventional banking needs many workers to perform tasks, such as receiving deposits,
approving loans, and making money transfers, but this has been changed entirely by internet banking, which
is smoother and faster with information technology (IT). Internet banking that revolutionizes the banking
sector has a vital role in serving the individual customer better to maintain a long-term relationship through
the dispensation of banking products through various channels, namely, ATMs, internet banking services,
mobile banking, and others. The internet, which makes financial and banking services more competitive,
has helped to have many e-banking products from ATMs to credit and debit cards. This e-banking, which
loosens entry barriers, gives each customer access anywhere globally and saves time for customers and
bank managers, who have changed manual and paperwork to paperless work with the help of technology
and communication (Atay & Apak, 2013).
Scope of Artificial Intelligence and its Application in the Banking Industry
Credit Score
Banks should borrow money after critically evaluating credit scores to customers applying for loans (Eletter
et al., 2010). The banking industry makes a profit, irrespective of the risk, because it controls and manages
the risk. Among various risks, credit risk is one of the major risks, giving more attention to getting away
from total system failure, since it is not easy to compensate (Ghodselahi & Amirmadhi, 2011). This
classification discerns between a functional score that has a high probability not to default and a bad score
that has a high chance of defaulting. Classification and regression tree models are made with a decision-
tree algorithm, which is one of the artificial intelligence techniques used for classification problems. This
technique gives better outcomes in evaluating credit scores than other techniques, namely, logistic
regression and discriminant analysis (Ince & Aktan, 2009). Approving loans is a crucial decision for the
bank’s profit and marketing strategies; this is not easy when different lending approaches are followed by
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competing banks and frequent changes occur in consumers’ borrowing behavior. The loan application can
be classified into two categories: positive credit risk and the applicants’ negative credit risk. The positive
credit risk indicates a high probability of applicants’ failing to repay the loan.
In contrast, the negative credit risk shows a low probability of applicants’ failing to repay the loan. The
bank managers who are overwhelmed with customer data should make the right decision to approve the
application with negative credit risk and deny the application with positive credit risk. Artificial intelligence
has helped managers make better decisions (Eletter et al., 2010).
As mentioned earlier, classification is the technique used to classify the applicant’s good credit and
bad credit. This credit score is applicable for companies, municipalities, states, financial institutions, and
so on. The value obtained from credit score processing is used by debt givers, bond buyers, and government
officers. The risk involved is inversely proportional to the credit score value based on the various indicators,
such as the applicant’s economic condition, capital involved, collateral offered by the applicant, the
applicant’s capacity, and the applicant’s behavior history (Ghodselahi & Amirmadhi, 2011). According to
the relationship between the bank and company, the credit limit allocated for that company is decided along
with various important factors, namely, the owner, the company, and the banks’ activity if it is a big company.
When it comes to small companies, the owner’s activity plays a more important role in deciding the credit
limit than other factors because a high chance of credit will be possible if the expert knowledge is shown
by the owner in the business (Fernando et al., 2011).
The most commonly used models are logistic regression and linear discriminant analysis. The linear
relationship of the two variables, which are not required for multivariate normality assumption and the latter,
also has a drawback in assuming that the variables are linear. However, in reality, the variables are non-
linear. Artificial intelligence techniques, such as decision trees, genetic algorithms, artificial neural
networks, and support vector machines, give better results than traditional statistical methods. Three models,
namely, the support vector machine, neural network, and decision tree, are used for classification along
with the fuzzy C-Means clustering technique. However, hybrid approaches have been outperforming the
above-mentioned specific methods concerning prediction accuracy (Ghodselahi & Amirmadhi, 2011).
Predicting risk and making the right decision for the approval of credit will help avoid situations like
bankruptcy and fraud activities (Moro et al., 2015).
Credit Card Fraudulent Activities
Fraudulent activities in credit card transactions are executed for various reasons, namely, improper
deployment method to deal with tens and thousands of transactions and the wrong approach in classifying
cost rates to the transaction of a different specified amount. Moreover, the data in which the model is
performed are skewed, and the transformation of unlabeled data into labeled data is an unreasonable and
prolonged-time-taking process, as well. K reverse nearest neighborhood is used to eliminate the outliers,
considered noise labels, after applying stratified random sampling to deal with 20% of the unbalanced data
present in the original dataset. To perform the dataset classification, a method that minimizes the
dimensionality will be executed first, and then a support vector machine (SVM), a particular one-class
support vector machine (OCSVM), is used. This OCSVM differs from SVM in training the dataset because
the former uses only one class to train. Its performance is significantly better compared to the group method
of data handling (GMDH) and probabilistic neural network (PNN) in the detection rate of fraudulent
claims while using a hybrid under-sampling approach (Sundarkumar & Ravi, 2015).
Cloud Security
Many cloud computing-related projects are facing a severely high rate of failure due to security issues.
There are five stages in modeling cloud computing: cloud deployment models, cloud risk management
models, mobility and banking applications, and cloud service models. Besides, there are eight risk
management sections in effective cloud computing: risk planning, risk analysis, risk identification, risk
prioritization, risk evaluation, risk treatment, risk control, and risk communication and documentation.
Levenberg-Marquardt based backpropagation is used in the data collected from the Cloud Delphi technique
for assigning the probability of occurrence and building a network analysis. After building the network,
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training of the data is performed, followed by the testing of the data. Finally, the prediction cloud security
model is executed by an artificial neural network (ANN) (Elzamly et al., 2017).
Phishing Websites
Phishing websites allure people into disclosing their usernames and password, which will be used for
various illegal transactions. The data mining algorithm, one of the artificial intelligence techniques, is used
to detect those phishing websites. The prediction of these websites is also possible with associative and
classification algorithms. The various estimations have been revealed that the cost per victim is continually
increasing. Majorly, emails are used to lure the banking customers into falling into the trap, which is
promoted by regularly sending spam emails to many people. The data mining technique will help get the
required information pertinent to the user from the tons of data available. There are 27 major feature vectors,
a conglomerate of different indicators, such as URL & domain identity, security and encryption, source
code and Javascript, page style and content, web address bar, and the human social factor. Various
approaches, namely, PART, PRISM, JRip, C4.5, MCAR, and CBA, are performed by Aburrous M et al.
(2010a) to determine the best approach; MCAR outnumbered others in accuracy and speed among all other
methods. The fuzzy data mining algorithm is used to identify phishing websites, particularly e-banking
websites, automatically, but finding a critical feature to achieve this goal is not always easy with this
technique (Aburrous M et al., 2010b).
Banking Failure
Banking failure will happen when the banks are not making a profit; this is due to various reasons: high
competition in the market, emerging non-banking institutions, unexpected threats to loan portfolios, and
financial distress. Big banks’ failure is dangerous, as it will lead to a bulge in the whole financial system.
In 1980, big banks failed to maintain security against non-performing loans, which is one reason for bank
failure and system collapse (Boyd & Gertler, 1994). Predicting risk and making the right decision for credit
approval will help avoid negative situations like bankruptcy and fraud (Mora et al., 2015).
Financial soundness indicators (FSI) are used to measure banks’ financial vulnerabilities, which are
classified into two leading indicators, such as the encouraging indicators and the corset indicators. It can be
abridged by different criteria, such as capital adequacy, asset quality, management quality, earning ability,
liquidity, and sensitivity. Three models, namely, discriminant, logit, and profit analyses, were introduced to
reveal banking failures in advance by three years. “Adoptive Neuro-Fuzzy inference system (ANFIS)” is
one of the techniques applied in finance, which is useful for predicting the failure of the events in the
banking system (Messai & Gallali, 2015). Banks assist in the economic stability of a country and reinforce
the country’s financial system. Fuzzy logic and neural network techniques are the apparent techniques used
to find the banks’ efficiency and productivity (Sharma et al., 2013).
In addition to this, three models were executed to predict the currency crisis, and those models are
logit regression, decision tree, and artificial neural network (ANN). The unpleasant situation can be
estimated from the ratio of the non-performing loans to the total gross loans. The non-performing loans are
the significant indicators of the probable financial crisis. ANN is performed with the main variables of a
bank’s distress: loan loss reserves of non-performing loans, return on equity average, and loan loss
provision to gross loan ratio (Messai & Gallali, 2015). The neural network gives a better percentage in
predicting the failure of banks according to Messai and Gallali (2015).
Alarm System
Artificial intelligence gives a better solution than a conventional emergency button alarming system in
improving the banking system security from robberies in the banks and the ATMs. This system performs in
three stages: artificial vision first takes a photo for image processing to get the features. An artificial neural
network (ANN) classifies the event from the obtained pattern and gives the warning messages status. Based
on the classification of the neural network, the output class is determined. If the output is 1, this means the
alarm should be activated, and a warning message should be sent using GSM technology (Ortiz et al., 2016)
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Mobile Banking
Mobile banking is famous nowadays; sixty-five members out of one-hundred member groups are using it,
and most people use mobile payment. Most of the customers have positive opinions about online payment
services, which attract the customer from the conventional card transaction and assist in enhancing banking
services due to maximizing revenue generation. This transformation of user experience helps collect and
analyze the user-generated data for delivering better service to each customer as per the patterns or insights
extracted from that data (Dubey, 2019).
Mobile devices facilitate mobile banking services that customers prefer due to comfort and
convenience, and they are, also, preferred by banking institutions to maintain good relations with them.
However, ninety-one percent of participants who have attended a study conducted by KMPG recently
responded that they did not use their mobile phones to do banking, not even once. The findings of the study
clearly show that customers should be segmented per their preference. The segmented customers should be
targeted to understand what makes them use mobile banking services and what kind of expectation they
have in their perception. Along with convenient services provided in mobile devices, banks can also utilize
the offered service to serve the customer better. This device that banks can use to deploy customer
relationship management is operated for customers’ purposes (Awasthi & Sangle, 2013). Recently, AI-
enabled mobile banking (Payne et al., 2018) has gotten some customer attraction, which the banks can
utilize to gain more customers
Customer Loyalty
The relationship between bankers and customers is paramount to keeping hold of existing customers and
enhancing customer loyalty. The customer relationship will be healthy if the banking institutions fulfill the
customers’ needs and expectations, which change over the period. Customer loyalty can be improved if
the customers are attracted by the excellent quality services provided at low prices. Customer loyalty can
be predicted in the banking industry by using an artificial neural network already used by other industries
for the same purpose. After collecting the data, essential variables should be taken from all the available
variables using factor analysis, making the data ready for further modeling. In this prediction model, feed-
forward backpropagation is used in the algorithm, along with the artificial neural network. K-fold cross-
validation is used, where K subsets are obtained from the data’s categorization during the dataset training.
The algorithm’s performance can be evaluated from the coefficient of efficiency and root mean square error
after testing the dataset. The obtained result of predicting customer loyalty from the artificial neural
network has proven that high accuracy is possible (Kishada et al., 2016).
Liquidity Risk Assessment
Banking institutions are prone to various technological and financial risks; these risks comprise the market,
operational, and credit risk. Banks have to increase the profitability, which leads to more investment from
the shareholders and ensures the liquidity position at any point in time. There should be an appropriate
balance between the short-term risk of liquidity and the long-term risk of profitability. The depositors who
invest their money for the short term in the bank will make frequent withdrawals, making the bank pay
more attention to liquidity. However, there is an unwanted situation on both extremes of liquidity: high
liquidity indicates reduced utilization of available resources, and low liquidity describes a wrong impression
of the banks, which leads to low deposits and a drop in market share. In both cases, the bank is moving
towards an unpleasant situation, such as bankruptcy.
Banks have a comprehensive database from which a liquidity risk alert system can be modeled with
an artificial neural network, generic algorithm for evaluating the liquidity risk, and a Bayesian network for
making predictions of the distribution liquidity risk. The measurement of a company’s liquidity risk can be
done by bifurcating into two elements: market-related features that consider recession, inference in the
transaction system, and disarrangement in the markets (capital) and bank-related features that contemplate
many factors such as credit risk and others. A system risk adjustment model for liquidity is a kind of
probability measurement of liquidity risk by combining data from the market and balance sheet and the
pricing method. The probability distribution is also used to find liquidity risk, but this requires big data to
International Management Review Vol. 17 No. 1 2021
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get proper output (Tavana et al., 2018). Tavana et al. (2018) used an artificial neural network applied along
with the genetic and Levenberg-Marquardt algorithm for identifying important feature vectors; the
Bayesian network is preferred to assess the probability of occurrence of liquidity risk from the feature, as
mentioned earlier vectors. This combination gives a better result, which is consistent after training the
model correctly.
Intelligence and Augmented Reality
Various industries have started using the latest technology to enhance their business, which is an outstanding
advantage for business operations. Augmented reality, which assists individuals by extending perception
and easing communications, delivers virtual aid to alleviate the complex real-world problems by disclosing
more details. From healthcare to games and media, many fields have already deployed augmented reality
to optimize existing processes. Industries with high-cost operations and high-risk involvement can
implement this application for business development (Heng, 2015). Augmented reality is also used by
banking and finance institutions to understand and demonstrate customer performance and make the best
recommendation to enhance customers’ expenditure patterns (Dubey, 2019). On the other hand, intelligent
systems are also installed for improving operations and reducing manual work. For example, JP Morgan
has executed a contract intelligence system that reduces labor activity by 0.36 million hours (Dubey, 2019).
Chatbot
If there is an issue or inquiry related to banking institutions’ products or services, customers have to contact
the officers to get the problem solved. However, this process is kind of tedious, repetitive, and time-
consuming. Due to advancements in AI-based banking technology, many industries have benefitted, and it
is working well in businesses. Moreover, Watson, developed by IBM, is designed to answer the queries;
this is done by applying machine learning algorithms and natural language processing (NLP), which helps
to retrieve the information and represent the inbuilt domain knowledge. Implementing these bots is useful
for serving customers better and is already done by most big growing banks (Singh et al., 2018).
Conclusion
Millions of customers undergo multiple transactions in a day as a routine. The data has been generated by
the customers, which is stored and maintained as a big database. Moreover, there is a lot of manual work to
perform to carry out most of the banking industry processes. Now, AI has made it easy for them to reduce
manual labor from the employee and customer sides. This kind of sophisticated work has become a simple
task, which has never been seen before due to the machine learning technique.
The banking sector has been improving its service quality by providing various practical tools to ensure
safety and comfort. The technology keeps improving daily; it is better to incorporate those technologies
into the business’ different fields. State-of-the-art technology is mandatory in maintaining and enhancing
security in the banking system, and other sections of the banking industry are ready to implement the latest
technology. In this digital era, customers are also expecting their bank to be up to date. The technology
upgradeability will uplift the service and security and improve the reputation of the bank. Nowadays,
internet banking and mobile banking are attractive to customers due to their effectiveness and user-
friendliness.
Many studies show that different models are launched to maximize the accuracy of the process, further
improving the customer-banking relationship and creating a win-win situation for both. Due to the
competition from the non-banking sectors, banks have to adapt the latest trending technologies used in the
digital era to improve their service quality. Technology has a more positive effect on the banking industry.
Artificial intelligence techniques should be utilized in the banking industry to make customer banking
transactions smooth and spontaneous in the business. Fortunately, AI has been providing a plethora of
applications to make the banks reach their fullest efficiency, paving the way for a new dimension in banking
services.
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