140 IJCSNS International Journal of Computer Science and Network Security, VOL.18 No.

10, October 2018

Impact of Artificial Intelligence on Performance of Banking

Industry in Middle East
Amer Awad Alzaidi
1
Department of Information Systems, University of Jeddah, Jeddah-Asfan road, Saudi Arabia

Abstract algorithms that communicate at high speed and designed

Artificial intelligence is a cutting-edge technology that have been with superior decision-making capabilities (Mata et al.,
forefront in technological revolution worldwide. Owing to greater 2018). In the opinion of Mannino et al., (2015) artificial
acceptance of new technological innovations, artificial intelligence in computer system is more efficient and
intelligence sector had been developing at an unprecedented pace superior than human experts. Presently, artificial
and is being applied in many walks of life. Artificial intelligence
systems have potential in transforming all operations of banking
intelligence had been applied in narrow but well-tested
industry is seen as (AI) and is received with enthusiasm due to its application areas such as certain medical diagnostics,
capability of taking human-like decisions and avoiding human- driverless cars are a few amongst them. In these areas of
like errors. Artificial intelligence has been adopted in some applications, it was noted that number of road accidents
sectors more widely than others, banking sector is amongst the decreased as well as medical diagnostics became more
few sectors that had shown moderate level of acceptance and accurate with the use of artificial intelligence system. As
adoption of this technology. This study explores the adaption of artificial intelligence had opened up several global
artificial intelligence in banking sector of Middle East. This opportunities, optimisation of relevant algorithms will
region is known for its mixed pace acceptance of various further aid in developing superhuman intelligence of
technological tools in the local banking industry. The primary
data required to analyse issue at hand was collected using survey
various automatic process in near future.
as a research strategy, the data was collected from 200 bank
employees across a few selected banks in the region. The main 1.2 Artificial Intelligence in banking industry
behind this survey is to gather information related to
understanding implementation of Artificial intelligence in Technological adaptation in the banking sector have been
banking sector and understand its impact. The data collected was gradual as compared to technological adoption in other
also quantitatively analysed using SPSS21.0 software. sectors. One of the crucial region behind it is the high
Key Words: dependence of banking industry on human involvement,
Artificial intelligence, Banking Industry, Impact, Performance, most of the operations were dependent on employed
Middle East, Employee perception manpower. With time technological inventions were
adopted in day-to-day working of banks and it efficiently
improved their performance. Application of artificial
1. Introduction intelligence in banking sector can make the operations
more impactful and hassle free (Manning, 2018). Some of
This section of the report presents a brief overview of the the areas in banking industry, where artificial intelligence
concept of artificial intelligence and its application in
can be successfully applied are enlisted below (Noonan,
banking sector.
2018; Punamaraju, 2018)–
Tailored Financial Services: Artificial intelligence can
1.1 Concept of Artificial Intelligence (AI)
aid in analysing stock markets and provide
Artificial intelligence (AI) is the field of science that deals recommendations as per financial goals of individual
with rivalling the capabilities of modern computer systems customers.
to resolve isues using human-like complex capabilities of Underwriting: Automation of this process is possible with
reasoning, learning and self-correction. Possibility of the use of artificial intelligence services that can use
applying artificial intelligence in diverse fields are far- unstructured information in the decision-making process.
reaching, and with recent development in both hardware Voice Aided Banking: Language processing using
and software technologies had increased the capabilities of artificial intelligence can reduce human error and improve
artificial intelligence systems. Application of artificial efficiency of the process.
intelligence is wide-ranging, it is applied in the field of Data-Driven Loaning Decisions: Artificial intelligence
optical communication, network planning, programming, systems’ assistance in analysing huge chunks of data,
transportation networks, medical science to name a few. performing calculation and prediction capabilities can aid
Artificial intelligence systems use various complex

Manuscript received October 5, 2018

Manuscript revised October 20, 2018
IJCSNS International Journal of Computer Science and Network Security, VOL.18 No.10, October 2018 141

in developing strategies in financial loaning department for In recent years, many of the Arab investors had initiated
better returns. investing a part of their money in the local market instead
Digitalisation of Branches: The lengthy process of of solely investing in American or European markets,
banking can be replaced complete digitisation of which had aided the banking and financial sectors recent
documents by developing a comprehensive platform using growth. The growth trend in the banking sector was also
artificial intelligence. supported by policy changes made by most of the Middle
East governments, that allowed more foreign investment
which were more regulated and independent. Another
2. Aim of the study boost came in the form of investment banking, that led to
middle eastern countries accept instruments of modern
The main aim of this study to analyse application of banking system (Farazi et al., 2011). The developing
Artificial Intelligence in banking industry in Middle East. banking sector of middle east still faces lags behind in
This study presents a comprehensive review of the certain fields such as risk management, better corporate
application of AI techniques in banking sector improving governance, mitigating the negative impact of economic
overall performance of the systems and banking network. shocks and slowdowns and such more. In addition to this,
banking sectors also faces significant challenges in
determining productive projects, managing savings,
3. Central research question investing in sustainable businesses to name a few (Jamall,
2017).
The central research question based on the aim of this study
is as follows: 4.2 Use of technology in banking sector in Middle
What are the opportunities associated with application East with special reference to AI
of artificial intelligence in banking sector of Middle
Eastern region? Use of technological innovation in the banking and
financial sector has been a global trend for quite some time,
been adopted in both developed and developing economies.
4. Literature Review Adaptation of technological innovation in the Middle
eastern banking and financial sector had been rapid in
This section of report presents a comprehensive analysis of
recent years, which had benefited bank’s customers as well
the existing literature on banking performance of Middle
as the finance remitters by reducing the cost of the services.
Eastern countries, application of technology in banking
Moreover, technological adaptation in banking sector had
sector of Middle East as well as advantages and
also increased profitability of the regional banks by
disadvantages associated with the use of artificial
reducing fees of money transfer fees and such (Sophia,
intelligence in banking sector.
2018). Technological inventions working complimentarily
with banking sector can provide more harmonised and
4.1 Performance of Banks in Middle East in past
modernised services to the customers. Banks in middle east
decade are in transformation phase having been adopted new
Financial and banking services sector of the Middle Eastern technologies on various levels, still being gradual on others.
region is amongst the fastest growing banking markets in It is an ongoing process that brings to table challenging
the world. Amidst the colossal overhauling, banking opportunities in the industry (John, 2017).
industry in many of the Gulf Cooperation Council (GCC) In the opinion of Shirish, Jayantilal and Haimari, (2016),
countries is profitable, efficient and well-developed (rncos, strong consumer adaptation is the primary reason behind
2012). As per Eisazadeh, Shaeri and Ali, (2012) in most of greater acceptance of technological advances in banking
the middle eastern countries, the public-sector banks sector of Middle eastern countries. Considering significant
dominate baking industry, due to intervention of local penetration of e-commerce, digital banking is adopted by
governments in losses and liquidity challenges as well as 20% to 25% of the consumers, which is a large number
credit allocation. As per the analysis conducted by the owing to lack of essential digital resources and security
author Middle Eastern banking industry was expected to protocols. As per (Dash, 2017) UAE’s banking sector is
grow over 16% during the year 2011-2014. In the past ten the trendsetter in embracing new technological innovations,
years, Islamic banking in the middle east also have carved banks such Emirates NBD, First Gulf Bank and Emirates
its niche in the region and have shown more growth as Islamic Bank and such have been leader in providing digital
compared to conventional banking sector of the region. In banking services. The entire banking industry is now being
the opinion of Jamall, (2017), banking sector in middle east disrupted by new technologies, and Artificial intelligence
have expanded over the years, which had led to increase in disruptive move by many, as this technology have potential
competitiveness within the sector. to transform all banking operations (Ghurair, 2018).
142 IJCSNS International Journal of Computer Science and Network Security, VOL.18 No.10, October 2018

According to Verma, (2017), artificial intelligence can be nature using both inductive and deductive approaches.
pivotal in changing in customer engagement within the Research approach acts as a guide for conducting the study
banking sector in Middle east. A few of the banks in Middle and improves credibility of the study (Blessing,
East have used chat boxes for providing their customers a Chakrabarti and Blessing, 2009). The mixed approach is
more personalised experience. selected here for enhancing accuracy of the research
findings. The research strategy for this study was based on
4.3 Pros and cons of Artificial Intelligence in banking survey that had been used to collect primary quantitative
sector data from the respondents. Survey method was selected
here to gather data from a wide-geographical area and a
Adaptation of Artificial Intelligence in banking sector have large sample population– 200 bank employees from
certain pros and cons associated with it, some of them are selected banks of Middle Eastern region. The research
enlisted below (Mannino et al., 2015; Verma, 2017; instrument used here for collecting quantitative data was
Ghurair, 2018; Manning, 2018; Noonan, 2018; Punamaraju, structured and close-ended questionnaire. They were
2018): approached and surveyed to understand the areas of
Pros: implementation of AI and its impact on the performance of
It can enable and accelerate automation of all the processes banking sector in Middle East. The quantitative data
in banking. analysis was conducted using SPSS21.0 software.
Less room for human errors.
It can significantly reduce the cost of banking services.
It can aid in systematically analysing behaviour pattern of 6. Data Analysis
customers and offer them more personalised services to
cater their needs. 6.1 Quantitative analysis
With the use of machine learning, artificial intelligence
systems can identify abnormalities in patterns to recognise The quantitative analysis is performed here for the primary
security threats and responds to them in time. data collected from surveying 200 bank employees of some
Cons: selected banks across Middle Eastern region. For this
It is disruptive for all the bank processes to adopt artificial analysis, only respondents who have submitted complete
intelligence in their day-to-day operations. responses were included and partial or incomplete
Complete automation of process will lead to no responses were excluded. For conducting this survey all the
supervision. participants were pre-informed about the aim of study and
It lacks the ability to take decisions under special the survey was conducted via e-mail after obtaining
circumstances. required permission and information from respective banks.
It requires more security protocols for developing a safe
automated environment. 6.1.1 Descriptive Analysis
Firstly, descriptive analysis here is used to present the
5. Methodology demographic profiling of the respondents that were
involved in the survey and the general background for the
Research methodology is defined as a well-planned and current research study being conducted.
methodical academically procedure that is used for
gathering the required data for related to a research study
for accomplishing all of its aims and objectives effectively
(Kothari, 2004). A suitable research method selected for
the study facilitates in resolving research issue at hand
(Newman and Benz, 1998). This study had employed
descriptive and explanatory research methods as it is
primarily a quantitative study and these methods aid in
collection of quantitative data for getting a better insight on
the relationship between various research variables.
Explanatory research method is selected here as it is a
flexible and casual method for understanding background
of the study. Moreover, descriptive research method was
selected for analysing demographic distribution and
general perception of the respondents on the issue at hand.
Research approach selected for this study is a mixed in
IJCSNS International Journal of Computer Science and Network Security, VOL.18 No.10, October 2018 143

Fig. 2 Distribution charts for adaptation of AI in banking sector and

application of AI in specific area of banking sector.

Figure 2 presents frequency distribution for adaptation of

AI in banking sector and application of AI in specific area
of banking sector. All the participant in this survey were
aware about the application of AI in banking and finance
sector. However, 64% of all the 200 participants admitted
that artificial intelligence systems were not adopted in their
Fig. 1 Demographic Distribution banks, 33% of the participants admitted to application of
AI systems in their respective banks and remaining 3%
The demographic description of the participants is depicted were undecisive. In the opinion of majority of the
in figure 1. The first parameter of the demographic participants i.e., 72%, artificial intelligence is mainly
profiling of all the participant in the survey is age-wise applied for automation of the processes and around 17.5%
distribution. It was found that majority number of the and 17% were of the opinion that AI is applied for
participants belonged to 41-50 years age group i.e., 33%. reduction in human error and personalisation of services
Age group of 21-30 and 31-40 both had 24.5% of the respectively. These findings were similar to findings of the
participants each and remaining 18% were above 50 years secondary research conducted, majority of existing studies
in age. Next parameter is gender distribution, as per also suggests that artificial intelligence application in
demographic data collected, 81% of the respondents were banking sector is for automating process to reduce manual
male employees of various banks and rest 18% were female labor as well as reduce human error and provide
employees. Therefore, it can be concluded that male personalized services to customers.
employees were more participative in the survey and it also
is in sync with social situation in middle eastern countries,
where female employment is quite low as compared to
other parts of world. Another demographic profiling
parameter was experience in the banking industry, it was
found that maximum percentage of participants i.e., 39%
had been working in the industry for a period of less than 5
years, 26% had experience of 5-10 years, 27.5% had
experience of 10-15 years and remaining 7.5% had
experience of more than 15 years. Another parameter was
country of residence and the majority of 59% belonged to
UAE, 32% to Saudi Arabia, 15% to Oman ,12% to Qatar
and Bahrain each and 11% to Kuwait.

Fig. 3 Distribution charts for impact of AI in banking sector and driving

factor behind implementing AI in banking sector.

Figure 3 presents, frequency distribution charts for impact

of AI in banking sector and driving factor behind
implementing AI in banking sector. The statistics shows
144 IJCSNS International Journal of Computer Science and Network Security, VOL.18 No.10, October 2018

that 68.5% out of all the 200 participants admitted that dependent variable with Pearson correlation= 0.972,
artificial intelligence systems would significantly closely followed by R3 (Asset Management), R8
(positive) impact on performance of bank, 21.5% of the (Digitisation and automation in back-office processing),
participants were of the opinion that AI systems will not R5 (Ease of services) and R4 (Reduction in cost of
significantly impact the banks and remaining 10% were services) having Pearson correlation value of 0.954, 0.949,
undecisive. When asked about the driving factor behind 0.944 and 0.917 respectively, whereas R1 i.e. “Customer
adaptation of AI in banks, 79% of the participants were of Satisfaction” is least correlated with the dependent variable
the opinion that reduction in human error is the primary among all with Pearson correlation= 0.193. This means that
factor, 21.5% considered reduction in service cost to be the the relationship application of AI’s application in banking
primary factor, 20% considered efficient working of AI sector and wealth management for the customer is strongest
systems as a major driver and 19% opined that efficient as with the use advanced data analytics techniques and
decision-making capability of AI system was the major complex predictive algorithms can provide more probable
driving factor behind the success of adaptation of AI in predictions for investments made. Also, least correlation
banking sector. This finding was also noted to be in sync with customer satisfaction indicates towards hesitation on
with the finding of secondary research, which also reveals customers’ part to depend entirely on artificial intelligence.
that reduction in manual error is major driving force behind Later, regression analysis was conducted for primary data
adaptation of AI in banking and finance sector. collected for this research study, in order to scrutinise the
impact of application of AI on the overall banking
6.1.2 Inferential Analysis performance. Table 2 and table 3 presents model summary
and ANOVA respectively.
Inferential statistical results for the data collected from
survey for this research study is presented in this section. Table 2: Model Summary
Model R R Adjusted R Std. Error of the
Table 1: Correlation Table
Square Square Estimate
1 .980a .961 .958 .294
Impact of AI on bank Pearson Correlation 1 a. Predictors: (Constant), R10, R1, R2, R6, R8, R7, R4, R3,
performance N 200 R5, R9
Pearson Correlation .193**
R1 Sig. (2-tailed) .006
N 200 Table 3: ANOVAa
Pearson Correlation .672** Sum of Mean
R2 Sig. (2-tailed) .000 Model Squares df Squar F Sig.
N 200 e
Pearson Correlation .954** Regressio 398.50 10 39.85 460.05 .000
R3 Sig. (2-tailed) .000 n 4 0 8 b
N 200 1 Residual 16.371 18 .087
Pearson Correlation .917** 9
R4 Sig. (2-tailed) .000 Total 414.87 19
N 200 5 9
Pearson Correlation .944** a. Dependent Variable: Impact of AI on bank performance
R5 Sig. (2-tailed) .000 b. Predictors: (Constant), R10, R1, R2, R6, R8, R7, R4, R3,
R5, R9
N 200
Pearson Correlation .737**
R6 Sig. (2-tailed) .000 As per the above tables 2 and 3, it can be seen that value of
N 200 R square value has been calculated to be 0.961, which
Pearson Correlation .893**
R7 Sig. (2-tailed) .000 indicates that 96.1% of variation in the dependent variable
N 200 can be explained on the basis of the independent variables
Pearson Correlation .949**
R8 Sig. (2-tailed) .000
together. This means that Artificial Intelligence system’s
N 200 application in banking industry can be reason behind
Pearson Correlation .972** 96.1% of improved performance factors of the banking
Sig. (2-tailed) .000
R9
N 200 industry. As per a article published by Nuseibeh (2017)on
Pearson Correlation .464** application AI in banking, it was found that the with the
R10 Sig. (2-tailed) .000 application of AI in banking sector can provide more
N 200
**. Correlation is significant at the 0.01 level (2-tailed) efficient and fast services as compared to any financial
R1- Customer Satisfaction, R2- Risk Management, R3- Asset advisor. It also supports more efficient and smart portfolio
Management, R4- Reduction in cost of services, R5- Ease of management along with cost and service time reduction.
services, R6- Fraud Detection, R7- Security, R8- Digitisation and
automation in back-office processing, R9- Wealth management for The ANOVA analysis presented above facilitates in
masses, R10- Enhanced performance of the ATMs inferring that the null hypothesis can be rejected in the
As per the statistics presented in table 1, correlation table it regression analysis, as the value of F is equal to 460.058,
can be inferred that all the variables are significant at 0.01 mean square value is 39.850 and high significance level
level of significance. It was noted that R3 i.e. “Wealth
management for masses” has highest correlation with
IJCSNS International Journal of Computer Science and Network Security, VOL.18 No.10, October 2018 145

value is 0.000. This indicates that model presented by the 7.1.1 What are the opportunities associated with
research is fit. application of Artificial intelligence in banking sector
of Middle Eastern region?
Table 4: Coefficients for Regression Analysis
Unstandardiz Standardiz
ed Artificial intelligence expected adoption in banking sector
ed Coefficient Sig
Model Coefficients t of Middle East region had opened up many opportunities.
s .
Std. Currently artificial intelligence is used in detecting
B Error Beta mismatching in transactions, providing personalised
(Constant) -.29 .120 - .01 recommendations for the customers and developing
4 2.442 6
solution for eliminating human errors (Pwc, 2018). Further,
R1 -.006 .015 -.006 -.387 .699
reduction of manual task and reduced need for back office
R2 .003 .033 .002 .088 .930 operation can also be achieved by using artificial
R3 .277 .062 .226 4.471 .000
R4 .112 .040 .111 2.781 .006 intelligence in banking sector. Although implementation of
1 R5 .036 .056 .035 .638 .524 artificial intelligence in banking sector is in quite early
R6 -.031 .034 -.021 -.936 .351 phase, but with the use of sophisticated algorithms of
R7 .047 .063 .027 .752 .453 artificial intelligence can enable efficient risk and asset
R8 .179 .051 .178 3.521 .001 management in the banking sector that can further optimise
R9 .445 .066 .445 6.793 .000
R1 .000 .009 financial policies at the Middle Eastern banks. Banks in the
0 .001 .035 .972
region can use quick and efficient artificial intelligence
a. Dependent Variable: Impact of AI on bank performance systems can enable banking organisations to develop
revenue generation models and start using smart financial
Table 4 presents the coefficients for regression analysis; management tools. However, currently available artificial
this table was found to be significant statistically as it aids intelligence tools used by the banks are mostly static that
in stating the existing dependency of the dependent provides mostly requirement and risk profiling. For
variable on the independent variables and establish providing more pragmatic and faster services, dynamic
relationship between them. As per statistics presented in systems are required to sense change patterns of markets
the above table, it can be inferred that out of 10 variables adjust financial strategies accordingly (Nuseibeh, 2017).
only 6 variables are coming out to be significantly having
“Sig.>=0.05”, that indicates, R1- Customer Satisfaction, 7.2 Implications
R2- Risk Management, R5- Ease of services, R6- Fraud
Detection, R7- Security, and R10- Enhanced performance It can be noted form this study that technological adaptation
of the ATMs. This indicates that performance of the banks in banking sector of Middle-eat have moved at a much
is related to application of AI system in banking industry, slower pace as compared to other global markets. With
application of such systems boosts efficiency of overall time there had been shift in attitude towards technological
system by increasing ease of service, increasing tools and now banking professionals aim to work hand in
predictability capabilities of system and reducing manual hand with the technological developments. Artificial
errors and discrepancies. An article published by Ghurair, intelligence adaptation in banking sector is far from
(2018), noted that application of AI will be transformative reaching to complete maturity level within the industry but
for the banks of the region and implementing it broadly will use of artificial intelligence in banking industry had
be both an opportunity and challenge of the local banking become trendsetter. The findings of the quantitative
sector. The author emphasised that use of AI-powered tools analysis conducted for this study was comparable to the
would systematically improve performance of the banks by findings of the secondary literature analysed. Thus, it can
tracking customer behavior patterns, offering personalised be inferred that with application of Artificial Intelligence in
services, decrease errors, reduce banking cost as well as banking sector of middle east, performance of local banks
time to closely match the needs of its customers. Thus, it can be significantly boosted.
can be concluded that use of Artificial intelligence in
banking sector can significantly impact upon the 7.3 Future Scope
performance of bank and have positive impact on overall
productivity of the system. This study had focused on analysing the impact of artificial
intelligence in the banking sector of middle-east region,
further it can be analysed, whether or not it had similar
7. Conclusion impact on banking sector of different regions across the
globe and global market. This study had only focused on
7.1 Answering Central Question advantages, disadvantages and opportunities associated
with use of artificial intelligence in banking sector, but it
The answer to the central research question is as follows:
146 IJCSNS International Journal of Computer Science and Network Security, VOL.18 No.10, October 2018

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IJCSNS International Journal of Computer Science and Network Security, VOL.18 No.10, October 2018 147

APPENDIX I: SURVEY QUESTIONNAIRE  Disruption of operations

Q7. Does the AI has a significant impact on the

performance of the banking sector?
Section 1: Demographic Profile  Yes
Q1. What is your age?  No
 21-30 years  Can’t Say
 31-40 years
Q8. What is the major driving factor behind adaptation of
 41-50 years
AI at your bank?
 Above 50 years
 Error Reduction
 Increased work efficiency
Q2. What is your gender?
 Reduced cost of the operations
 Male
 Efficient decision making
 Female
 Others (Please specify): _________________
Q3. For how long have you been working in banking
sector?
Section C: Impact of AI on the performance
 Less than 5 years
 5 – 10 years of the bank
 10 – 15 years Q10. Is there a significantly positive impact of AI on the
 15 – 20 years performance of your bank?
 More than 20 years  Strongly Agree
 Agree
Q4. Which country do you belong to?
 Neutral
 UAE
 Disagree
 Saudi Arab
 Strongly Agree
 Kuwait
 Oman Rate the following performance aspects of banking
 Qatar industry as per your perception on improvement in
 Bahrain performance of the banks after use of artificial intelligence
 Others (Please Specify) on the scale of 1 to 5 where
 Strongly Agree
 Agree
Section B – General Background  Neutral
Q4. Are you aware about the use of AI in banking sector?  Disagree
 Yes  Strongly Agree
 No
 Can’t Say

Q5. Does your bank uses AI in banking processes

 Yes
 No
 Can’t Say

Q6. What are the areas where AI is being used in your

bank?
 Automation of processes
 Reduction in Human error
 Consumer satisfaction
 Personalises Services
 Mitigating Security Concerns
 Ability to take complex decisions
148 IJCSNS International Journal of Computer Science and Network Security, VOL.18 No.10, October 2018

S. PERFORMANCE FACTORS 1 2 3 4 5
No
1 Customer Satisfaction (Customer
support and help desk)
2 Risk Management
Tailored products being offered to
2.1 clients by looking at historical
data
2.2 Doing risk analysis
2.3 Eliminating human errors from
traditional models
3 Asset Management
3.1 Track sentiments/ Markets
3.2 Enhance portfolio management
3.3 Understand customer behaviour
3.4 Automate compliance
4 Reduction in cost of services
5 Ease of services
6 Fraud Detection
6.1 Increase the accuracy of credit
card fraud detection
6.2 Increase the anti-money
laundering
7 Security
7.1 Suspicious behaviour
7.2 Logs analysis
7.3 Spurious emails can be tracked
down to prevent
7.4 Predict security breaches
8 Digitisation and automation in
back-office processing
Wealth management for masses
(Personalised portfolios being
9 managed for clients by taking into
account lifestyle, appetite for risk,
expected returns on investment,
etc.)
Enhanced performance of the
ATMs by use of Image/face
recognition using real-time
10 camera images and advanced AI
techniques such as deep learning
can be used at ATMs to detect
and prevent frauds/crimes.