Biometric technology in banking institutions: ‘The customers’ perspectives’

Abstract

Background: Over the years, attention has been focused on digital banking and financial technology with little or no attention being paid to biometric banking technology.

Objective: The study aimed to investigate the need for security and simplicity in the authentication of retail payments, digital banking and financial technology through the application of biometric systems.

Method: The study employed quantitative research methodology and a response rate of 52% was achieved. A set of questionnaires was distributed for data collection.

Results: The study’s findings indicated it is imperative for all businesses that participate in financial businesses to fully implement the best possible security measures or systems to ensure or enhance security for financial business activities.

Conclusion: Based on the findings of the study, it is recommended that businesses must adopt the new innovative and secured mechanisms of financial dealings to enhance innovation, security and flexibility.

Full text

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South African Journal of Informaon Management
ISSN: (Online) 1560-683X, (Print) 2078-1865
Page 1 of 12 Original Research
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Authors:
Abraham Morake1
Lucas T. Khoza1
Tebogo Bokaba1
Aliaons:
1Department of Applied
Informaon Systems, College
of Business and Economics,
University of Johannesburg,
Auckland Park, South Africa
Corresponding author:
Lucas Khoza,
lucask@uj.ac.za
Dates:
Received: 13 May 2021
Accepted: 28 Sept. 2021
Published: 10 Dec. 2021
How to cite this arcle:
Morake, A., Khoza, L.T. &
Bokaba, T., 2021, ‘Biometric
technology in banking
instuons: “The customers’
perspecves”’, South African
Journal of Informaon
Management 23(1), a1407.
hps://doi.org/10.4102/
sajim.v23i1.1407
Copyright:
© 2021. The Authors.
Licensee: AOSIS. This work
is licensed under the
Creave Commons
Aribuon License.
Introducon
There are increasing concerns related to the security in authenticity and integrity of banking
systems (De Souza Faria & Kim 2013; Petrlic & Sorge 2013). The weakness of the current verification
or authentication methods such as pin numbers and passwords contributes significantly towards
information leakage stored in Automated Teller Machine (ATM) smartcard which results in loss
of money in bank account (Jaiswal & Bartere 2014). The word biometrics originates from ancient
Greek and implies measures – bios mean life, whilst metrics mean measuring, therefore in full it
means measuring life (Prabhakar, Pankanti & Jain 2003). It can be described as the process of
identifying human uniqueness employing physical traits that include the face, fingerprint, iris
and behavioural traits (Jain, Flynn & Ross 2007). There are various biometrics classifications:
fingerprint scrutiny, face examination, hand geometry, iris observation, voice recognition, and
signature acknowledgement (Clodfelter 2010).
The birth of biometrics can be traced back to the 19th century where it mainly focused on gaining
knowledge of people’s physical traits to secure their identity (Maguire 2009). Earlier biometrics
was mainly applied within high-security applications. However, it is currently applied within a
wider variety of public-facing applications, for example, in prisons, by police for drivers’ license
verification, canteen administration, payment systems, in the borders for verification control,
including electoral system (Ashbourn 1999). Since the late 1990s, there have been changes in
biometrics as a primary security replacement technology from an older form of identification
such as passwords and security pin-codes (Maguire 2009). Biometrics initially was used to
measure the physical and behavioural features of a person (Galton 1901). Upcoming biometric
verification applications comprise ATM use, workplace authentication, network access, travel
and tourism, world wide web connections, and mobile connections (Ashbourn 1999).
Over the last few years, more studies have been done on digital banking, financial technology and
other areas rather than the impact of biometrics within banking and retailing in South Africa.
Digital is the separation of information from physical data storage to the technical potential or
digital (Legner et al. 2017). There are various characteristics of digitisation, namely collaboration,
sharing, co-creation, connectivity, communication, mobility, and flexibility (Syler & Baker 2016).
Digital networks started to join retailers together with traders, clients and customers to develop the
identified needs for the first online connected catalogues and inventory software systems (Kelman
Background: Over the years, attention has been focused on digital banking and financial
technology with little or no attention being paid to biometric banking technology.
Objective: The study aimed to investigate the need for security and simplicity in the
authentication of retail payments, digital banking and financial technology through the
application of biometric systems.
Method: The study employed quantitative research methodology and a response rate of 52%
was achieved. A set of questionnaires was distributed for data collection.
Results: The study’s findings indicated it is imperative for all businesses that participate in
financial businesses to fully implement the best possible security measures or systems to
ensure or enhance security for financial business activities.
Conclusion: Based on the findings of the study, it is recommended that businesses must adopt
the new innovative and secured mechanisms of financial dealings to enhance innovation,
security and flexibility.
Keywords: biometrics; financial technology; security; authentication methods; digital banking.
Biometric technology in banking instuons:
‘The customers’ perspecves’
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Page 2 of 12 Original Research
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2016). Digital banking refers to the process of shifting into
online banking and the digitisation of the entire outdated
banking activities, including plans that were historically
offered to the bank customers and required customers to
physically visit the bank to do specific activities such as
money deposit, withdrawals, money transfers and account
management (Coetzee 2018). Digital banking has facilitated
customers to overcome controlled time banking and local area
operations (Das 2018). Digital banks use advanced banking
systems that can swiftly implement new services allowing
for seamless mobility for bank users (Varga & David 2017).
The main problem with this research is that there is a demand
for more innovative and secured banking systems that will
enable customers to access their money at any given time and
location. In this fourth industrial revolution (4IR) era and the
need for transformation within the banking sectors,
technological advancement has provided better opportunities
for financial institutes to tap. In contrast, many financial
institutions have conformed to the traditional digital banking
platforms as a mode of operation. This digital banking
platform enables customers to make money deposits,
withdrawals, transfers and account management without
physically visiting the bank. However, none of these banking
sectors have been able to take full advantage of the capacity
and possibilities of the 4IR for a more innovative and
simplified banking platform.
There are only few studies which have studies and covered
biometric banking and payment systems. To bridge the gap,
this study seeks to evaluate the innovative and secured
methods of paying for items at retail stores and accessing
money without physically having a bank card and hard cash
through the application of biometrics. The study focuses on
biometrics digital banking financial technology as an
alternative means of authentication for mobile baking
transactions such as payments and bank transfers. Current
authentication methods still use traditional password
authentication. In addition, this article seeks to create
awareness in banks and retailers on the significant role of
biometrics as an essential mechanism in providing speedy,
secured, flexible and innovative authentication process to
protect the funds/money of customers and the organisation,
which can result in crime being lowered or prevented.
This article is structured as follows: section literature review,
discusses the current knowledge and findings around
biometric technology in the banking system. Section Challenges
of biometrics covers the research problem that this research
study attempts to address. Section Research method and
design discusses the research methodology. Lastly, sections
Results and analysis through 11 is the data analysis.
Literature review: Biometric
technology in the banking system
As submitted by Ateba et al. (2013), for banks to remain
relevant, successful and competitive in today’s competitive
world, they must provide innovative and best-secured services
to their customers.
Customer and organisaonal perspecves
A customer can be described as a stakeholder of an
organisation who provides payment in exchange for products
or services (Ateba et al. 2013). In addition, a customer cannot
only be described as a person but also an organisation (e.g.
university, bank, construction company, school, legal firm
and hospital) that buys goods and services from other
retailers (Rahman & Safeena 2016). Organisations (banks and
retail) need to understand that customers come from various
occupations (Rahman & Safeena 2016). More banking and
other financial transactions are being done online by
customers and fraudsters have followed suit, initiating ever-
more sophisticated attacks. With the risk of digital fraud and
theft increasing many organisations have searched for
solutions to stop fraudsters from launching ever-more
sophisticated attacks. Banks cannot stop or limit the high rate
of transaction scams and security breaks by using traditional
security systems such as password/pin and identification
cards; therefore, digital banking solutions appear to be a
perfect mechanism to defeat these threats (Hosseini &
Mohammadi 2012). Pin code verification alone cannot be
regarded as a strong defence mechanism against security
breaches. Using digital banking solutions, the operator’s data
or information is securely kept in an encrypted container or
sandbox (Johnson 2019).
Digital perspecve
Digital banking solutions have proven to be more innovative
for end-users, who appreciate replacing a complicated
password with a fingerprint or face scan, which features
biometric technologies (Agidi 2018). By applying biometrics,
traditional passwords are becoming a thing of the past;
biometrics is taking over banking security. To achieve
safeguarding of operations and customer transactions, one
solution is to secure banking using a consistent authentication
method such as biometric (Hosseini & Mohammadi 2012).
Biometrics characteristics include fingerprints, veins, palm
veins, iris, retina, face, voice, and handwritten signature. The
patterns of blood vessels in the palm finger are so different
that no two or more individuals possess the same, and this
can serve as a trusted security system (Ahmad, Ali & Adnan
2012). Biometrics is still in its early stages in developing
countries, but it has been developed and adopted by
businesses to increase the security and efficiency of the
adopter’s operations (Agidi 2018).
Usage of biometrics in banking institutions is popular in
developed countries thus, the adoption rate of biometrics is
growing significantly (Venkatraman & Delpachitra 2008).
There is no hesitation that biometrics are escalating for
banking security, to an extant identifying authentication
through biometric application is highly secured compared to
password authentication (Liang, Samtani, Guo & Yu 2020).
Biometric authentication is also coming to physical payments

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cards; biometrics are progressively being used for account
access, even replacing debit cards at the ATMs (Lee 2016).
Biometrics provides a much more reliable and efficient
method of verification than relying only on human agents.
The security and efficiency principles of biometrics make the
adoption of biometrics an attractive prospect to banking
institutions across the world (Agidi 2018). With the average
banking customer managing a broader range of financial
transactions online through desktop and mobile devices, the
need for simple and secured access to their banking data is
becoming a top priority for banking service providers
intending to differentiate themselves from the direct rivals.
As the digital age expands, banks need to balance security
and accessibility (Varga 2018).
Major South African banks include: ABSA, FNB, Nedbank,
Standard Bank, and Capitec (Coetzee 2018). This is based on
their revenue generation, large base of customers, services
and products they offer and marketing strategies they
deliver. Without the successful implementation and adaption
of e-banking by the South African banking industry, most
banks will struggle to perform optimally through the
adaption of the 4IR and FinTech (Abukhzam & Lee 2010).
Businesses have realised the increasing value digitisation
provides towards the growth of businesses (Neumeier et al.
2017).
It is important that digital payment service providers (banks)
have a comprehensive cybersecurity strategy aided by a
robust framework to assist all stakeholders participating in
the ecosystem (Kristensen & Solvoll 2019). There is a demand
for managing service interface and customisation of products
and services influenced by the input of technology offered in
business settings (De Farias et al. 2014). Advanced biometric
payment methods enhance the convenience, choice of
payments and alternative payment methods for customers.
Payment methods allow customers to conduct business and
commercial activities with ease and flexibility at any given
time (Kristensen & Solvoll 2019). Payment experts concur
that electronic payment techniques are efficient, convenient
and fast (Crowe, Schuh & Stavins 2006).
Biometrics applicaon in automated teller
machines
Biometrics in banking for ATM authentication provides both
the banks and the customers with an outstanding benefit
through providing customers with the flexibility to do
transactions without physically having their bank cards; thus,
banks can avoid the costs and liabilities of customer problems
because of lost and/or stolen bank cards (Vernkatraman &
Delpachitra 2008). Using biometrics in banking, ATMs are
popular in developed countries; thus, the adoption rate of
biometrics is growing significantly (Venkatraman &
Delpachitra 2008). There is no doubt that biometrics is
escalating in banking security, but it might be a while before
identifying authentication without passwords is completely
secure (Furnell & Evangelatos 2007). Biometric authentication
is also coming to physical payment cards; thus, biometrics is
progressively being used for account access, even replacing
debit cards at the ATMs (Choi et al. 2007).
Challenges of biometrics
Biometric challenges can negatively impact people and
businesses or customers and organisations. Bank crises and
failures can be attributed to the growing extent to which
scammers and fraudsters operate (Bhasin 2015). Fraud is
considered a global phenomenon that negatively challenges
all sectors of the economy (Bhasin 2015). A rapid increase in
security cracks and transactional breaches within traditional
security systems such as pin codes and passwords is speedily
influencing the evolution of a strong biometric authentication
method (Hosseini & Mohammadi 2012).
In addition, a factor that can contribute towards the
challenges of adopting biometrics is too much time and
money spent to educate people who are technologically and
biometrically illiterate (Ahmad et al. 2012). New deployments
or the premature phase of biometric technology are quite
similar to the introduction of any other system, since it might
take a while for general users to accept it, depending on the
system’s impact on them (Wayman et al. 2005).
Any form of change in the customers finger (a user cuts
him-/herself by mistake) may lead to the users being denied
access to their respective systems that has been created by the
users with their normal fingerprint (Ahmad et al. 2012).
Another significant challenge of biometrics includes a scenario
whereby, should the user be involved in an accident and lose
an eye, finger or facial changes occur because of scratches or
cuts, the biometric system will not recognise the user and will
reject the user as a result of the physical changes or damages
(Aly et al. 2008; Buddharaju, Pavlidis & Manohar 2008).
Biometrics has difficult challenges that may impact the
human rights of a person negatively, for example, when a
thief decides to cut off a victim’s finger to gain access to their
information and the system (Choi et al. 2007; Chetty &
Wagner 2009; Jin, Kim & Elliott 2007; Pacut & Czaika 2006;
Tan et al. 2010; Toth 2005). Dust and grime on the fingerprint
scanner may impact the quality of the system negatively,
which may result in a situation where the system does not
recognise the user (Ahmad et al. 2012).
There are various issues that characterise the challenges of
biometrics in problems such as signature authentication
forgery, the high cost of implementing liveness detection,
dust dropped on scanners, poor quality of the scanner to
recognise the user, a time-consuming system, poor human
machine interaction, lack of guidance for interacting with the
system and a lack of proper information security policies and
procedures (Brooks 2010; Jain & Kumar 2010; Koppenhaver
2007; Park 2008).
The main contributing factors to the challenges of biometric
information usage is the misuses, negative interpretation,

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and failure to complying to the Protection of Personal
Information Act (POPIA). The purpose of the Act is to protect
people from harm by protecting their personal information,
to stop their money being stolen, to stop their identity being
stolen, and generally to protect their privacy, which is a
fundamental human right (POPIA Act 2021). In South Africa,
a person’s fingerprints and blood type are considered
personal information under the Electronic Communications
and Transactions Act (ECTA 2002).
Since early 2020, the COVID-19 pandemic has impacted on
and disrupted many aspects of peoples daily life. Touch-
based technologies such as fingerprint and facial recognition
scanners can be considered as indirect contributing factors
for COVID-19, because they are used by many people for
authentication and verification purposes at ATMs, stores and
banks (Gomez-Barrero et al. 2021). Hygiene related fears
have increased the societal resistance towards the use of
touch-based biometrics sensors (Priesnitz et al. 2021). In
addition, it is important to note that such fears have in turn
fuelled research efforts in 2D or 3D touchless fingerprint
recognition systems (Gomez-Barrero et al. 2021).
Benets of biometrics
Biometric benefits can impact both people and businesses or
customers and organisations. Moreover, biometrics can be
considered a quicker information tracer and recovery method
than manual or traditional verification methods carried out
at the counter (Ahmad et al. 2012; Jain & Kumar 2010; Jain,
Ross & Pankanti 2006).
Biometric security can be considered a method that
contributes significantly towards ensuring the integrity,
confidentiality and availability of information (Ahmad et al.
2012). Biometrics protects both logical and physical access
controls. Logical access controls include the protection of
network facilities, computers and information systems
against unauthorised admission (Jain et al. 2006), whilst
physical access controls ensure that only authorised people
have access to IT infrastructures and document filing (Jain
et al. 2006).
Forensic accounting is a requirement for banks to decrease
the speedy growth of financial frauds (Bhasin 2015). In
addition, biometric authentication methods offer a natural,
unforgettable and rarely breached verification (Hosseini &
Mohammadi 2012). Password, pin and code word
authentication can be forgotten, cracked and guessed by
hackers or scammers (Jain et al. 2006). In addition, fingerprint
authentication is more secure, as it provides users with
quicker verification and is impossible to forget compared to a
password (Johnson 2019). Smartcards are also at risk of being
lost, stolen and duplicated (Jain et al. 2006). Therefore,
biometrics can be considered a solution for enhanced security,
as the authentication relies on a person’s physical traits (Jain
et al. 2006). Physiological biometric features include retina,
fingerprint, hand vein, iris, hand geometry facial recognition,
and ear shape. These features are unique, and no one in the
world shares them (Ahmad et al. 2012). Behavioural biometric
features include voice recognition and signature verification
(Ahmad et al. 2012; Jain & Kumar 2010).
Biometric security systems can assist banks with various
benefits such as forensic application, criminal identification,
border control and surveillance (Rhodes 2003). Various
impacts may characterise the benefits of biometrics, for
example, human signature authentication, being user-
friendly, convenient and flexible, maintaining accuracy, faster
information retrieval, strong matching algorithm and speaker
recognition (Koppenhaver 2007; Park 2008; Wang et al. 2011).
Mul-factor authencaon methods
The use of a password (or a PIN) to confirm the ownership
of the user ID could be considered as a single-factor
authentication (SFA) method (Ometov et al. 2018). Evidently,
this is the weakest level of authentication (Dasgupta, Roy &
Nag 2016; Bonneau et al. 2015).
Authentication with just a single-factor method is not reliable
to provide enough protection because of several security
threats such as rainbow table and dictionary attacks (Gunson
et al. 2011). Two-factor authentication (2FA) methods consist
of something the user has, such as cards, smartphones, or
other tokens (Sun et al. 2014; Bruun, Jensen & Kristensen
2014). Multi-factor authentication (MFA) methods consist of
something the user/customer is, specifically, biometric data
or behaviour patterns such as fingerprint, face recognition,
behaviour recognition and others (Ometov et al. 2018).
The need for reliable user authentication method has
increased in the wake of intensified concerns about security
and rapid advancements in communication, mobility, and
networking (Yadav & Gothwal 2011). Frequently, MFA is
based on biometrics, which is automated recognition of
individuals based on their behavioural and biological
characteristics (Frank, Biedert, Ma, Martinovic & Song 2012).
Biometrics challenges and benefits will be further discussed
in detail, because the term can be considered as a key
technique of MFA. Figure 1 shows the evolution from SFA
factor to MFA.
Research method and design
The research design that was used in this study was
quantitative. Quantitative research refers to a numerical
Knowledge factor:
PIN, password,
security questions
Ownership factor:
Smartphone, key-card,
one-time password
Biometric factor:
Fingerprint,
face recognition,
behavior recognition
Single-factor
authentication
Two-factor
authentication
Multi-factor
authentication
Source: Ometov, A., Bezzateev, S., Mäkitalo, N., Andreev, S., Mikkonen, T. & Koucheryavy, Y.,
2018, ‘Mul-factor authencaon: A survey’, Cryptography 2(1), 1.
FIGURE 1: Evoluon of authencaon methods from single-factor authencaon
to mul-factor authencaon.

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illustration of explanations of the phenomena (Sukamolson
2000). Throughout the study, data have been collected by
means of:
• A literature review and comparing a list of similar work
done over the years.
• Conducting an online survey to evaluate the use of
biometrics to authenticate payment and day-to-day
personal banking transactions.
• Consulting with shoppers, bank users, financial
institutions such as banks and general societies (students,
employed and unemployed community members) with
bank accounts.
A questionnaire survey was conducted on a sample population
of respondents who have knowledge on biometrics, digital
banking, financial technology, retail and customers. The
overall number of the questionnaires shared received 336
responses. Out of the 336 responses, only 173 respondents
submitted fully completed questionnaire, the remaining 162
respondents did not complete the survey. This process gave
the questionnaire a successful completion response rate of
52%. The questionnaire was designed into four sections which
are: A, B, C and D. Section A gathered the background
information of the respondents, Section B collected the
challenges of biometric, Section C collected benefits of
biometric, and the final Section D gathered biometric solutions
to enhance secured and innovative means of accessing,
transferring and sharing money. The survey was distributed
electronically via different social media platforms. The
selected sample technique for this study is the probability
sampling technique which facilitates study of a large
population, and therefore was relevant for this study as its
targeted sample size was 300 responses. Furthermore,
quantitative research is commonly aligned with the probability
sampling technique to enhance generalisability (Saunders et
al. 2019). The reason for the study to employ students is
because financial decision-making is very important for the
success of students in their lives and careers; therefore, it is
critical for students to understand funds management
(Sachitra, Wijesinghe & Gunasena 2019). Another contributing
reason for the study to use bank members such as managers is
because they value financial information and have key
financial knowledge (Akhtar & Liu 2018).
The study employed the random sampling technique in
preference of the systematic, stratified and cluster random
sampling techniques. The inclusion criterion for the study
was shoppers with one or more bank accounts. The study
mainly focused on the city of Johannesburg in Gauteng
province. Johannesburg has an estimated population of
5 782 747. Out of this population, about 30% are below the
standard age of owning a bank account (Department of
Statistics South Africa 2019), totalling 1 734 824. From the
remaining 4 047 923 shoppers with bank accounts, the sample
size of the research was limited to 300 respondents because of
issues such as time and resource constraints. The study only
targeted the age group of 18–60. The study also targeted
the population using payment mechanisms such as:
• eWallet
• Electronic Fund Transfers (EFTs)
• Credit and cheque cards
• Internet banking transfers
• Card-based payments
• Debit cards
• PayPal
• Visa Checkout
• Google Pay
• Samsung Pay/ Mobile Pay
Validity of the data collecon tool
used
The validity of the collected data was demonstrated through
questionnaires and surveys. Content validity will be
determined based on the reliably collected data provided by
respondents (bank managers, retail managers and customers).
Thus, constructive validity will be determined through
evaluating the views of customers, bank managers and retail
managers using biometrics authentication for payments and
other activities. Both Cronbach’s alpha and Statistical Package
for the Social Sciences (SPSS Version 26) were used to ensure
that the collected data is accurate, logical and factual
(Scherbaum & Shockley 2015).
Using the Cronbach’s alpha analytical tool on SPSS, it was
found that the validity of the response regarding ‘usage of
biometrics in terms of financial sector’ is 0.857. Table 1 shows
the Cronbach’s alpha values.
Ethical consideraons
Approval to conduct the study was obtained from the College
of Business and Economics, the University of Johannesburg.
During data collection, personal information was not
requested and participation in this research work was
voluntary, and participants were allowed to withdraw upon
completing the questionnaires.
Results and analysis
This section of the study presents findings of the study
obtained during questionnaire distribution.
Descripve stascs
Figure 2 describes the sector or occupation in which
respondents are involved. In a practical example, the people
belonging to academic and education sectors visit retail
stores to purchase books, laptops and other academic or
education-related merchandise. In construction sector, there
must be a purchase of building or construction materials;
TABLE 1: Cronbach’s alpha values.
Secon Cronbach’s alpha value
E-Banking 0.852
Financial Technology 0.857
Biometrics 0.858

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the same applies to other occupations. This shows that data
supplied by these categories of respondents are reliable.
The education sector has the highest percentage of 19.7%,
followed by Other with 19.1%. Academia is the third-ranked
sector with 16.2%, followed by the unemployed respondents
with 14.5%. The information technology sector is ranked
fifth with 9.8%, followed by the banking sector with 5.8%.
Both the health and consulting sectors have a percentage of
4.6%. The manufacturing and insurance sectors both have a
percentage of 2.3%. The construction sector has the lowest
percentage of 1.1%. Table 2 shows the sector or occupation
values to clarify values in Figure 2.
The results show that the study has covered various
organisational sectors. Academia, construction, consulting,
education, health, information technology, insurance,
manufacturing and other sectors, including the unemployed
individuals, are all represented. This concludes that most
organisational sectors have been represented in the study.
Highest qualicaon
Figure 3 describes the highest qualification of respondents to
evaluate their potential level of understanding new topics that
impact their daily lives and activities in this modern era of the
4IR. The results are arranged from the highest to the lowest
percentage. Results reveal that 31.8% of the respondents have
obtained a bachelor’s degree, 16.8% matric or Grade 12, 15.0%
an honours degree, another 13.3% a university diploma, 11.0% a
Master’s degree, 6.4% college diplomas, 4.0% other qualifications,
1.2% with a Ph.D. degree and 0.5% without matric.
The results indicate that a large percentage of the respondents
have obtained a bachelor’s degree. This indicates that most
respondents have a good education and are more
knowledgeable (Bosupeng 2017). A question on rating the
educational level of the respondents has been included to
evaluate their level of understanding new topics impacting
their daily lives and activities in this modern era of the fourth
industrial revolution.
It is important to be educated, well-informed and technologically
exposed because education contributes significantly to
developing a person’s opinions, character, trading with others
and preparing one for life experiences (Al-Shuaibi 2014).
Additional literature aligned with the study provides that
promising stages to prevent fraud activities are educating
customers with various processes of avoiding being a victim of
fraudsters (Bhasin 2015).
Correlaon stascs
The purpose of this section is to describe the relationship
between variables. Thus, extensive literature was used to
analyse other sections of the article; the Pearson’s correlation
was conducted to explore statistical relationships amongst
variables. Moreover, data analysis was conducted through
matching and comparing the benefits variables together with
the challenge variables. The Pearson’s correlation was used
because it works with the raw data values of the variables,
whereas Spearman works with rank-ordered variables.
Moreover, the Pearson’s correlation evaluates the linear
relationship between two continuous variables, whilst the
Spearman correlation coefficient is based on the ranked
values for each variable rather than the raw data (De Winter,
Gosling & Potter 2016).
The data analysis technique used to analyse the data was
correlation to predict the strength and direction between two
variables. The strength of correlation between the variables is
shown under the Pearson’s correlation, whilst Sig. (2-tailed)
represents the significance of the influence amongst the
variables. Sig. (2-tailed) below 0.05 indicates that the
1. Banking (5.8%)
2. Construcon (1.1%)
3. Consulng (4.6%)
4. Academia (16.2%)
5. Informaon technology (9.8%)
6. Insurance (2.3%)
7. Manufacturing (2.3%)
8. Health (4.6%)
9. Educaon (19.7%)
10. Unemployed (14.5%)
11. Other (19.3%)
123
4
5
6
7
8
9
10
11
FIGURE 2: Sector or occupaon analysis.
TABLE 2: Sector or occupaon analysis.
Sector Values (%)
Educaon 19.7
Other 19.1
Academia 16.2
Unemployed 14.5
Informaon Technology 9.8
Banking 5.8
Health 4.6
Consulng 4.6
Insurance 2.3
Manufacturing 2.3
Construcon 1.1
0.51.2 4.0 6.4
11.0
13.3 15.0 16.8
31.8
0
5
10
15
20
25
30
35
No matric
Ph.D. Degree
Other
College diploma
Master's degree
University diploma
Bachelors with honours
degree
Matric
Bacherlor's degree
Percentage (%)
Qualificaon
FIGURE 3: Highest qualicaon of respondents.

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relationship between the variables is significant, whilst Sig.
(2-tailed) above 0.05 indicates that there is no significant
relationship between the variables (Pallant 2020). Table 3
illustrates the Sig. (2-tailed) declaration, whilst Table 4
demonstrates the Pearson’s correlation declaration.
Biometric challenges and benets
analysis
The Pearson’s correlation was conducted to examine the
relationship between biometric challenges and biometric
benefits.
Appendix 1 shows the Pearson’s correlation for enhanced
surveillance with involvement in an accident, sensitivity of
sensor performance and biometric characteristics. There is a
weak positive significant relationship between enhanced
surveillance and involvement in an accident (r = 0.004;
p = 0.216), enhanced surveillance and sensitivity of sensor
performance (r = 0.008; p = 0.202), and enhanced surveillance
and biometric characteristics (r = 0.004; p = 0.220).
These findings indicate that should the user be involved in an
accident and have cuts on their biometric features such as
fingers, face or iris, it will be difficult for a biometric reader to
fully recognise the authorised user to gain access to a system.
The biometric security system can assist banks and retailers
with a wide range of benefits such as surveillance, as reported
by Rhodes (2003).
Appendix 1 also shows the Pearson’s correlation for enhanced
border control with involvement in an accident, sensitivity of
sensor performance and biometric characteristics. Statistical
results indicate that there is a weak positive significant
relationship between enhanced border control and
involvement in accident (r = 0.001; p = 0.253), enhanced
border control and sensitivity of sensor performance
(r = 0.007; p = 0.205), and enhanced border control and
biometric characteristics (r = 0.048; p = 0.150).
These findings indicate that enhanced border control can
be challenged by the fact that biometric characteristics
such as face, fingerprint and voice recognition can be
copied. The biometric security system can assist banks and
retailers with a wide range of benefits such as forensic
application, criminal identification, border control and
surveillance (Rhodes 2003).
Appendix 1 next shows the Pearson’s correlation for criminal
identification with sensitivity of sensor performance and
biometric characteristics. Statistical results indicate that there
is a weak positive significant relationship between criminal
identification and sensitivity of sensor performance (r = 0.017;
p = 0.181) and criminal identification and biometric
characteristics (r = 0.032; p = 0.163).
These findings imply that the process of effortlessly providing
information about the criminal record of the individual can
be challenged by a sensitivity of sensor performance. The
biometric security system can assist banks and retailers with
a wide range of benefits such as forensic applications,
criminal identification, border control and surveillance
(Rhodes 2003).
From Appendix 1, the Pearson’s correlation for ease of
information retrieval with involvement in an accident and
biometric characteristics can be observed. Statistical
results show that there is a weak positive significant
relationship between ease of information retrieval and
being involved in an accident (r = 0.014; p = 0.166) and ease
of information retrieval and biometric characteristics (r =
0.003; p = 0.224).
These findings indicate that the process of providing users
with quicker verification can be hindered by damages or
changes to the users’ physical biometric features such as face,
eyes and fingers caused by accidents. The biometric security
system can assist banks with features maintaining accuracy,
convenience, faster information retrieval, strong matching
algorithm and speaker recognition (Koppenhaver 2007; Park
2008; Wang et al. 2011).
Appendix 1 shows the Pearson’s correlation for strong
matching algorithm with involvement in an accident,
sensitivity of sensor performance and non-technologically
inclined individuals. Moreover, statistical results indicate
that there is a weak positive significant relationship between
strong matching algorithm and being involved in an accident
(r = 0.017; p = 0.161), strong matching algorithm and
sensitivity of sensor performance (r = 0.007; p = 0.204), and
strong matching algorithm and non-technologically inclined
individuals (r = 0.008; p = 0.200).
These findings indicate that biometric systems, which can
easily differentiate between two or more biometric traits
such as hands, eyes and face, can also be hindered by
damages or changes to the user’s physical biometric features
such as the face, eyes and fingers caused by accidents.
Pin code verification alone cannot be regarded as a strong
defence mechanism against security breaches. However,
by using biometric verification, the operator is secured to
their data or information which is securely kept in an
encrypted container or sandbox (Johnson 2019).
TABLE 4: Pearson’s correlaon declaraon.
Correlaon declaraon 1 Correlaon declaraon 2 Level of Signicance
0.00 0.29 Weak impact
0.30 0.49 Medium impact
0.50 1.00 Strong impact
Source: Pallant, J., 2020, SPSS survival manual: A step by step guide to data analysis using
IBM SPSS, Routledge, London
TABLE 3: Sig. (2-tailed) declaraon.
Correlaon Value Level of Signicance
Sig. (2-tailed) Below 0.05 Signicant relaonship
Sig. (2-tailed) Above 0.05 No signicant relaonship
Source: Pallant, J., 2020, SPSS survival manual: A step by step guide to data analysis using
IBM SPSS, Routledge, London

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Appendix 1 shows the Pearson’s correlation for lost or stolen
smartcards and mobile devices with scammers, fraudsters
and non-technologically inclined individuals. There is a
weak positive significant relationship between lost or stolen
smartcards and mobile devices and scammers (r = 0.013;
p = 0.188), lost or stolen smartcards and mobile devices and
fraudsters (r = 0.003; p = 0.224), and lost or stolen smartcards
and mobile devices and non-technologically inclined
individuals (r = 0.005; p = 0.214).
These findings indicate that individuals, such as scammers,
who participate in dishonest schemes by committing fraudulent
activities may intend to exploit lost or stolen smartcards and
mobile devices and steal funds of individuals. Biometrics in
banking for ATM authentication provides both banks and
customers with an outstanding benefit through providing
customers with the flexibility to do transactions without
physically having their bank cards; thus, banks can avoid the
costs and liabilities of customer problems because of lost and/
or stolen bank cards (Vernkatraman & Delpachitra 2008).
Appendix 1 also shows the Pearson’s correlation for impossible
to forget fingerprint authentication, non-technologically
inclined individuals and biometric characteristics. There is a
weak positive significant relationship between impossible to
forget fingerprint authentication and non-technologically
inclined individuals (r = 0.014; p = 0.187) and impossible to
forget fingerprint authentication and biometric characteristics
(r = 0.041; p = 0.155).
These findings indicate that fingerprint authentication is
impossible to forget compared to a password. Moreover,
non-technologically inclined individuals still trust that the
pin code or password authentication method is the best
technique for security authorisation (Bhasin 2015).
Appendix 1 further shows the Pearson’s correlation for
uniqueness, involvement in accident, sensitivity of sensor
performance, non-technologically inclined individuals and
biometric characteristics. Statistical results reveal that there is
a weak positive significant relationship between uniqueness
and being involved in an accident (r = 0.011; p = 0.194),
uniqueness and sensitivity of sensor performance (r = 0.042;
p = 0.155), uniqueness and non-technologically inclined
individuals (r = 0.001; p = 0.247), and uniqueness and
biometric characteristics (r = 0.002; p = 0.253).
These findings indicate that the uniqueness and benefits of
the biometric authentication systems are supported by
variables such as being involved in an accident, sensitivity of
sensor performance, non-technologically inclined individuals
and biometric characteristics such as the face, fingerprint
and voice recognition (Hosseini & Mohammadi 2012).
Physiological biometric features include retina, fingerprint,
hand vein, iris, hand geometry, facial recognition and ear
shape. These features are unique and no one in the world
shares them (Ahmad et al. 2012).
Appendix 1 shows the Pearson’s correlation for forensic
application and dust dropped on the fingerprint scanner,
involvement in an accident, sensitivity of sensor performance,
non-technologically inclined individuals, and fake fingerprint
forgery. There is a weak positive significant relationship
between forensic application and dust dropped on the
fingerprint scanner (r = 0.015; p = 0.185), forensic application
and involvement in an accident (r = 0.002; p = 0.236), forensic
application and sensitivity of sensor performance (r = 0.000;
p = 0.278), forensic application and non-technologically
inclined individuals (r = 0.000; p = 0.273), and lastly forensic
application and fake fingerprint forgery (r = 0.045; p = 0.153).
These findings indicate that because of physical biometric
changes acquired by the users through an accident, it will be
difficult for a biometric scanner system to easily recognise the
user in a system. Forensic accounting is a requirement for
banks to decrease the speedy growth of financial fraud
(Bhasin 2015). In addition, the biometric authentication
method offers natural, unforgettable, and hardly breached
verification (Hosseini & Mohammadi 2012).
Biometric connecons as soluons
to deliver secured and innovave
means of accessing, transferring
and sharing money
Table 5 represents biometrics connections, including the level
of agreeing and disagreeing by the respondents that the
above-mentioned biometrics connections can be labelled as
solutions that can assist banks and retailers in delivering
secured and more innovative means of accessing, transferring
and sharing money. From the 173 surveyed respondents,
93.1% of the respondents agreed that advanced authentications
systems/single authentication that a user shares with no one
TABLE 5: Biometric connecons as soluons.
Variables Frequency %
Advanced authencaons systems/single authencaon
a user shares with no one (such as ngerprint compared
to the old tradional authencaon such as pins and
passwords that can be guessed or traced)
No 12 6.9
Yes 161 93.1
Total 173 100.0
Simple and secured access (ability to manage a broader
range of nancial transacons online)
No 10 5.8
Yes 163 94.2
Total 173 100.0
Enhanced convenience
No 5 2.9
Yes 168 97.1
Total 173 100.0
Increased security
No 14 8.1
Yes 159 91.9
Total 173 100.0
Reliable and ecient vericaon relying only on human
agents
No 13 7.5
Yes 160 92.5
Total 173 100.0

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(such as fingerprint compared to the old traditional
authentication such as pins and passwords that can be guessed
or traced) could be labelled as one of the solutions that can
assist banks and retailers in delivering secured and
innovative means of accessing, transferring and sharing
money, whilst 6.9% disagreed on the statement. A majority
(94.2%) of the respondents agreed that simple and secured
access (ability to manage a broader range of financial
transactions online) can be labelled as one of the solutions
that can assist banks and retailers to deliver secured and
innovative means of accessing, transferring and sharing
money. In comparison, 5.8% disagreed with the statement.
Whilst, 97.1% of the respondents agreed that enhanced
convenience could be labelled as one solution that can assist
banks and retailers in delivering secured and innovative
means of accessing, transferring and sharing money, 2.9%
disagreed with the statement. A higher percentage (91.9%)
of the respondents agreed that increased security could be
labelled as one of the solutions that can assist banks and
retailers in delivering secured and innovative means of
accessing, transferring and sharing money, whilst 8.1%
disagreed with the statement. Regarding the final
connection, 92.5% of the respondents agreed that reliable
and efficient verification relying only on human agents
could be labelled as one of the solutions that can assist
banks and retailers in delivering secured and innovative
means of accessing, transferring and sharing money, whilst
7.5% disagreed on the statement.
Literature postulates that banks must provide customers with
more innovative and secured banking services (Hosseini &
Mohammadi 2012). Biometric authentication or verification
method that includes face and fingerprint recognition is
considered a precise security solution for accessing, transferring
and sharing money (Hosseini & Mohammadi 2012).
Discussion
Pearson’s correlation for enhanced surveillance indicates that
should the user be involved in an accident and have cuts on
biometric features such as fingerprint, face, and iris, it will be
difficult for a biometric reader to fully recognise the authorised
user to gain access into a system. Biometric security systems
can assist banks and retailers with a wide range of benefits
such as surveillance, as reported by Rhodes (2003).
Pearson’s correlation for enhanced border control indicates
that enhanced border control can be challenged by the fact
that biometric characteristics such as face recognition,
fingerprint and voice can be copied none are 100%. Biometric
security systems can assist banks and retailers with a wide
range of benefits such as forensic application, criminal
identification, border control and surveillance (Rhodes 2003).
Pearson’s correlation for criminal identification shows that
the process of effortlessly providing information about the
criminal record of the individual can also be challenged by
the sensitivity of sensor performance. A biometric security
system can assist banks and retailers with a wide range of
benefits such as forensic application, criminal identification,
border control and surveillance (Rhodes 2003).
Pearson’s correlation for ease of information retrieval
indicates that the process of providing users with quicker
verification can be hindered by damages or changes to the
user’s physical biometric features such as face, eyes and
fingers caused by accidents. Biometric security systems can
assist banks with the following features maintaining accuracy,
convenience, faster information retrieval, strong matching
algorithm and speaker recognition (Koppenhaver 2007; Park
2008; Wang et al. 2011).
Pearson’s correlation for strong matching algorithm findings
indicate that biometric systems that can easily differentiate
between two or more biometric traits such as hands, eyes and
iris, can also be hindered by damages or changes to the user’s
physical biometric features such as face, eyes and fingers
caused by accidents. Pin code verification alone cannot be
regarded as a strong defence mechanism against security
breaches, using biometric verification, the operator is secured
to their data or information which is securely kept in an
encrypted container or sandbox (Johnson 2019).
Pearson’s correlation for lost or stolen smartcards and mobile
devices findings indicate that individuals who participate in
dishonest schemes through committing fraudulent activities
such as scammers may intend to exploit lost or stolen
smartcards and mobile devices of other users and steal funds
of other individuals. Biometrics in banking for ATMs
authentication provides both banks and customers with an
outstanding benefit through providing customers with the
flexibility to make transactions without physically having
their bank cards. Thus, banks can avoid the costs and
liabilities of customer’s problems because of lost and stolen
bank cards (Vernkatraman & Delpachitra 2008).
Pearson’s correlation for impossible to forget fingerprint
authentication indicates that fingerprint authentication is
impossible to forget as compared to a password. Moreover,
non-technologically inclined individuals still trust that pin
code or password authentication methods are the best
security authorisation techniques (Bhasin 2015).
Findings for the Pearson’s correlation for uniqueness indicate
that the uniqueness and benefits of the biometric authentication
systems can be astounded by matters such as, involved
in an accident, sensitivity of sensor performance,
non-technologically inclined individuals and biometric
characteristics such as face recognition, fingerprint and voice
can be copied none are 100% (Hosseini & Mohammadi 2012).
Physiological biometric features include retina, fingerprint,
hand vein, iris, hand geometry, facial recognition and ear shape,
these features are unique and no one in the world shares them
or have the same (Ahmad et al. 2012).
Finally, Pearson’s correlation for forensic application findings
indicate that, because of physical biometric changes acquired
by the users through an accident, it will be difficult for a

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biometric scanner system to recognise the user in a system
easily. Forensic accounting is a requirement for banks to
decrease financial fraud’s speedy growth (Bhasin 2015). In
addition, the biometric authentication method offers a
natural, unforgettable and hardly breached verification
(Hosseini & Mohammadi 2012).
Conclusion
This study was carried out to investigate the need for security
and simplicity in the authentication of retail payments, digital
banking and financial technology through the application of
the biometric system. Furthermore, the study assessed the
possible challenges, benefits and solutions to the biometrics
authentication payment system. From the findings, the study
further elaborated and discussed the biometric solutions that
can assist banks and retailers in enhancing secured and
innovative means of accessing, transferring, and sharing
money. It is concluded that biometric technology is the
innovative technology that different banking institutions can
use to enhance security and innovation, protect the funds of
their customers against scammers, fraudsters, hackers, and
other constraints. Therefore, further studies can focus on the
combined relationship amongst biometrics, digital banking
and financial technology.
Acknowledgements
My genuine gratitude to Alpha and Omega, Creator of
heaven and earth. Thank you to my supervisor, Mr Lucas
Khoza and co-supervisor Mrs Tebogo Bokaba, for their
patience, guidance, and continuous support towards
completing this Journal.
Compeng interests
The authors have declared that no competing interest exist.
Authors’ contribuons
All authors contributed equally to this work.
Funding informaon
This study received no specific funding from any agency in
public, commercial or non-profit sectors. Because of the
budget and time constraints, the study sampled only 173
respondents. The authors acknowledge that this could have
impacted the ability to generalise the results of the study. It
is therefore recommended that future studies should look at
larger sample size. In addition, because of the limited
number of individuals who are technologically inclined in
the South African society, it was difficult to find respondents
who fitted the criteria used to select respondents for the
study. The study has not been extended to other provinces,
as it is limited to the Gauteng province of South Africa.
Data availability
Data that support the findings of the study can be obtained
from the corresponding author L.T.K.
Disclaimer
The views and opinions expressed in this article are those of
the authors and do not necessarily reflect the official policy or
position of any affiliated agency of the authors.
References
Adewole, K.S., Abdulsalam, S.O., Babatunde, R.S., Shiu, T.M. & Oloyede, M.O., 2014,
‘Development of ngerprint biometric aendance system for non-academic sta
in a terary instuon’, Development 5(2), 62–70.
Agidi, R.C., 2018, ‘Using biometric in solving terrorism and crime acvies in Nigeria’,
Techsplend Journal of Technology 1(12), 91–105.
Ahmad, S.M.S., Ali, B.M. & Adnan, W.A.W., 2012, ‘Technical issues and challenges of
biometric applicaons as access control tools of informaon security’, Internaonal
Journal of Innovave Compung, Informaon and Control 8(11), 7983–7999.
Akhtar, S. & Liu, Y., 2018, ‘SMEs’ use of nancial statements for decision making: Evidence
from Pakistan’, Journal of Applied Business Research (JABR) 34(2), 381–392.
Aly, S., Sagheer, A., Tsuruta, N., & R.I. Taniguchi, 2008, ‘Face recognion across
illuminaon’, Arcial Life and Robocs 12(1–2), 33–37.
Ashbourn, J., 1999, The biometric white paper, viewed 30 July 2021, from hp://
homepage.ntlworld.com/avan/whitepaper.htm.
Ateba, B.B., Maredza, A., Ohei, K., Deka, P. & Schue, D., 2015, ‘Markeng mix: it’s
role in customer sasfacon in the South African banking retailing’, Banks and
Bank Systems (open-access) 10(1), 82–91.
Bhasin, M.L., 2015, ‘An empirical study of frauds in the banks’, European Journal of
Business and Social Sciences 4(7), 1–12.
Brooks, D.J., 2010, ‘Assessing vulnerabilies of biometric readers using an applied
defeat evaluaon methodology’, Paper presented at the Proceedings of the 3rd
Australian Security and Intelligence Conference, Perth, November 30, 2010.
Board of Governors of the Federal Reserve System, Consumers and Mobile Financial
Services, 2016, Board of Governors of the Federal Reserve System Washington,
DC, pp. 1–86.
Bonneau, J., Herley, C., Van Oorschot, P.C. & Stajano, F., 2015, ‘Passwords and the
evoluon of imperfect authencaon’, Communicaons of the ACM 58(7), 78–87.
Bosupeng, M., 2017, ‘How Relevant Are Academic Degrees In The Workplace?’,
Munich Personal RePEc Archive, MPRA Paper No. 77914. pp. 2–7.
Bruun, A., Jensen, K. and Kristensen, D., 2014, ‘Usability of single-and mul-factor
authencaon methods on tabletops: a comparave study’, in Internaonal
Conference on Human-Centred Soware Engineering, Springer, Berlin, Heidelberg,
pp. 299–306.
Buddharaju, P., Pavlidis, I. & Manohar, C., 2008, ‘Face recognion beyond the visible
spectrum’, in Advances in Biometrics, pp. 157–180, Springer, London.
Chandran, R., 2014, ‘Pros and cons of mobile banking’, Internaonal Journal of
Scienc and Research Publicaons 4(10), 1–5.
Chey, G. & Wagner, M., 2009, ‘Biometric person authencaon with liveness
detecon based on audio-visual fusion’, Internaonal Journal of Biometrics 1(4),
463–478.
Choi, H., Kang, R., Choi, K. & Kim, J., 2007, ‘Aliveness detecon of ngerprints using
mulple stac features’, in Proc. of World Academy of Science, Internaonal
Journal of Biological and Medical Sciences, vol. 2, pp. 200–205, Engineering and
Technology.
Clodfelter, R., 2010, ‘Biometric technologies in retailing: Will consumers accept
ngerprint authencaon?’, Journal of Retailing and Consumer Services 17(2010),
181–188. hps://doi.org/10.1016/j.jretconser.2010.03.007
Coetzee, J., 2018, ‘Strategic implicaons of Fintech on South African retail banks’,
South African Journal of Economic and Management Science 21(1), 2455. hps://
doi.org/10.4102/sajems.v21i1.2455
Crowe, M.D., Schuh, S.D. & Stavins, J., 2006, ‘Consumer behavior and payment choice: A
conference summary’, FRB of Boston Public Policy Discussion Paper, (06-1).
Das, S.S., 2018, ‘A study of digital banking facilies: With reference to Guwaha in
kamrup (metro) district of assam’, Journal of Management 5(1), 6–13.
Dasgupta, D., Roy, A. & Nag, A., 2016, ‘Toward the design of adapve selecon
strategies for mul-factor authencaon’, Computers & Security 63, 85–116.
De Farias, S.A., Aguiar, E.C. & Melo, F.V.S., 2014, ‘Store atmospherics and experienal
markeng: A conceptual framework and research proposions for an
extraordinary customer experience’, Internaonal Business Research 7(2), 87–99.
De Souza Faria, G. & Kim, H.Y., 2013, ‘Idencaon of pressed keys from mechanical
vibraons’, IEEE Transacons on Informaon Forensics and Security 8(7),
1221–1229. hps://doi.org/10.1109/TIFS.2013.2266775
De Winter, J.C., Gosling, S.D. & Poer, J., 2016, ‘Comparing the Pearson and Spearman
correlaon coecients across distribuons and sample sizes: A tutorial using
simulaons and empirical data’, Psychological Methods 21(3), 273–290.
Electronic Communicaons and Transacons Act (ECTA), 2002, South Africa
Government Gazee 446(23708), 1–41.
Eze, S.G. & Chijioke, E.O., 2016, ‘Public enlightenment educaon on the acceptance of
ngerprint biometric technologies for administraon in academic instuons and
other organisaons’, Journal of Educaon and Pracce 7(28), 158–163.

Page 11 of 12 Original Research
hp://www.sajim.co.za Open Access
Frank, M., Biedert, R., Ma, E., Marnovic, I. & Song , D., 2012, ‘Touchalycs: On the
applicability of touchscreen input as a behavioral biometric for connuous
authencaon’, IEEE transacons on informaon forensics and security 8(1),
136–148.
Furnell, S. & Evangelatos, K., 2007, ‘Public awareness and percepons of biometrics’,
Computer Fraud & Security 2007(1), 8–13. hps://doi.org/10.1016/S1361-
3723(07)70006-4
Galton, F., 1901, ‘Biometry’, Biometrika 1, 7–10. hps://doi.org/10.1093/biomet/1.1.7
Gomez-Barrero, M., Drozdowski, P., Rathgeb, C., Pano, J., Todisco, M., Nautsch, A.
et al., 2021, ‘Biometrics in the era of COVID-19: Challenges and opportunies’,
arXiv preprint arXiv:2102.09258, 1–14.
Gunson, N., Marshall, D., Morton, H. & Jack, M., 2011, ‘User percepons of security
and usability of single-factor and two-factor authencaon in automated
telephone banking’, Computers & Security 30(4), 208–220.
Hosseini, S.S. & Mohammadi, S., 2012, ‘Review banking on biometric in the world’s
banks and introducing a biometric model for Iran’s banking system’, Journal of
Basic and Applied Scienc Research 2(9), 9152–9160.
Jain, A., Hong, L. & Pankan, S., 2000, ‘Biometric idencaon’, Communicaons of
the ACM 43(2), 90–98. hps://doi.org/10.1145/328236.328110
Jain, A.K., Flynn, P. & Ross, A., 2007, Handbook of biometrics, pp. 1–556, Springer
Science & Business Media, New York.
Jain, A.K. & Kumar, A., 2010, ‘Biometrics of next generaon: An overview’, Second
Generaon Biometrics 12(1), 2–3.
Jain, A.K., Ross, A. & Pankan, S., 2006, ‘Biometrics: A tool for informaon security’,
IEEE Transacons on Informaon Forensics and Security 1(2), 125–143.
Jaiswal, A.M. & Bartere, M., 2014, ‘Enhancing ATM security using ngerprint and GSM
technology’, Internaonal Journal of Computer Science and Mobile Compung
(IJCSMC) 3(4), 28–32.
Jin, C., Kim, H. & Ellio, S., 2007, ‘Liveness detecon of ngerprint based on band-
selecve Fourier spectrum’, in Internaonal Conference on Informaon Security
and Cryptology, Springer, Berlin, Heidelberg, pp. 168–179.
Johnson, A., 2019, ‘How biometrics (and blockchain) could save bricks-and-mortar
retail’, Biometric Technologies Today 3, 8–10. hps://doi.org/10.1016/S0969-
4765(19)30040-2
Kelman, J., 2016, The history of banking: A comprehensive reference source & guide,
pp. 1–384, CreateSpace Independent Publishing Plaorm, California,
Kim, S., 2007, ‘Governance of informaon security: New paradigm of security
management’, in Computaonal intelligence in informaon assurance and
security, pp. 235–254, Springer, Berlin.
Koppenhaver, K.M., 2007, Forensic document examinaon: Principles and pracce,
pp. 1–389, Springer Science & Business Media, New Jersey.
Kristensen, L.B.K. & Solvoll, M., 2019, ‘Digital payments for a digital generaon’,
Nordic Journal of Media Studies 1(1), 125–136.
Lee, T., 2016, Biometrics and disability rights: legal compliance in biometric
idencaon programs, U. Ill. JL Tech. & Pol’y, p. 209.
Legner, C.T., Eymann, T., Hess, C., Ma, T., Böhmann, P., Drews, A. et al., 2017,
‘Digitalizaon: Opportunity and challenge for the business and informaon
systems engineering community’, Business & Informaon Systems Engineering
59(4), 301–308. hps://doi.org/10.1007/s12599-017-0484-2
Liang, Y., Samtani, S., Guo, B. & Yu, Z., 2020, ‘Behavioral biometrics for connuous
authencaon in the internet-of-things era: An arcial intelligence perspecve’,
IEEE Internet of Things Journal 7(9), 9128–9143. hps://doi.org/10.1109/
JIOT.2020.3004077
Lowrence, D., 2014, ‘Biometrics and retail: Moving towards the future’, Biometric
Technologies Today 2014(2), 7–9. hps://doi.org/10.1016/S0969-4765(14)
70032-3
Maguire, M., 2009, ‘The birth of biometric security’, Anthropology Today 25(2), 9–14.
hps://doi.org/10.1111/j.1467-8322.2009.00654.x
Mahfouz, A., Mahmoud, T.M. & Eldin, A.S., 2017, ‘A survey on behavioral biometric
authencaon on smartphones’, Journal of Informaon Security and Applicaons
37, 28–37. hps://doi.org/10.1016/j.jisa.2017.10.002
Mallat, N., Rossi, M. & Tuunainen, V.K., 2004, ‘Mobile banking services’,
Communicaons of the ACM 47(5), 42–46. hps://doi.org/10.1145/986213.
986236
Manseld-Devine, S., 2013, ‘Biometrics in retail’, Biometric Technologies Today
2013(9), 5–8. hps://doi.org/10.1016/S0969-4765(13)70161-9
Mir, G.M., Balkhi, A.A., Lala, N.A., So, N.A., Kirmani, M.M. & Mir, I.A., 2018, ‘The
benets of implementaon of biometric aendance system’, Oriental Journal of
Computer Science and Technology 11(1), 50–54. hps://doi.org/10.13005/
ojcst11.01.09
Neumeier, A., Wolf, T., & Oesterle, S. 2017, ‘The Manifold Fruits of Digitalizaon -
Determining the Literal Value Behind’, in St. Gallen, J.M. Leimeister & W. Brenner
(eds.), Proceedings der 13, Internaonalen Tagung Wirtschasinformak (WI
2017), St. Gallen, pp. 484–498.
Pacut, A. & Czajka, A., 2006, ‘Aliveness detecon for iris biometrics’, in Proceedings
40th annual 2006 internaonal carnahan conference on security technology, IEEE,
pp. 122–129.
Pallant, J., 2020, SPSS survival manual: A step by step guide to data analysis using IBM
SPSS, Routledge, London.
Park, R.C., 2008, ‘Signature idencaon in the light of science and experience’,
Hasngs LJ 59, 1101.
Petrlic, R. & Sorge, C., 2013, ‘Establishing user trust in automated teller machine
integrity’, IET Informaon Security 8(2), 132–139. hps://doi.org/10.1049/iet-
ifs.2012.0220
Prabhakar, S., Pankan, S. & Jain, A.K., 2003, ‘Biometric recognion: Security and
privacy concerns’, IEEE Security & Privacy 2, 33–42. hps://doi.org /10.1109/
MSECP.2003.1193209
Priesnitz, J., Rathgeb, C., Buchmann, N., Busch, C. & Margraf, M., 2021, ‘An overview
of touchless 2D ngerprint recognion’, EURASIP Journal on Image and Video
Processing 2021(1), 1–28. hps://doi.org/10.1186/s13640-021-00548-4
Rahman, M.R. and Safeena, P.K., 2016, ‘Customer Needs and Customer Sasfacon’,
In Ramees Rahman (eds.), book: Theeranaipunya - A Capacity Building Training
Programme Equipping the Fisher women Youth for the Future, Central Marine
Fisheries Research Instute, pp. 259–262, Kochi, India.
Raani, A. & Derakhshani, R., 2018, ‘A survey of mobile face biometrics’, Computers &
Electrical Engineering 72, 39–52.
Rhodes, K.A., 2003, Informaon security: Challenges in using biometrics, General
Accounng OceTechnical Report # GAO-03-1137T.
Saunders, M., Lewis, P. & Thornhill, A. 2019, Research methods for business students,
8th edn., Pearson Educaon Limited, Harlow.
Sukamolson, S., 2000, Conducng and developing a mulmedia computer-assisted
instrucon program for teaching Foundaon English IIII, Chulalongkorn University,
Language Instute.
Sun, Y., Zhang, M., Sun, Z. & Tan, T., 2017, ‘Demographic analysis from biometric data:
Achievements, challenges, and new froners’, IEEE transacons on paern
analysis and machine intelligence 40(2), 332–351.
Syler, R. & Baker, E., 2016, Building a framework for the inuence of digital content on
student course engagement.
Tan, X., Li, Y., Liu, J. & Jiang, L., 2010, ‘Face liveness detecon from a single image with
sparse low rank bilinear discriminave model’, in European Conference on
Computer Vision, Springer, Berlin, Heidelberg, pp. 504–517.
Ten Have, H. & Gordijn, B. (eds.), 2014, Handbook of global bioethics, vol. 4, Springer,
New York, NY.
Toth, B., 2005, ‘Biometrics. Biometric Liveness Detecon’, Informaon Security
Bullen 10, 291–297.
Varga, D., 2017, ‘Fintech, the new era of nancial services’, Vezetéstudomány/Budapest
Management Review 48, 22–32. hps://doi.org/10.14267/VEZTUD.2017.11.03
Venkatraman, S. & Delpachitra, I., 2008, ‘Biometrics in banking security: A case study’,
Informaon Management & Computer Security 16(4), 415–430. hps://doi.
org/10.1108/09685220810908813
Wang, N., Ching, P.C., Zheng, N. & Lee, T., 2011, ‘Robust speaker recognion using
denoised vocal source and vocal tract features’, IEEE Transacons on Audio,
Speech, and Language Processing 19(1), 196–205. hps://doi.org/10.1109/
TASL.2010.2045800
World Health Organizaon, n.d., Coronavirus disease (COVID-19) pandemic, viewed
16 September 2021, from hps://www.who.int/emergencies/diseases/novel-
coronavirus-2019.
Wayman, J.L., Jain, A.K., Maltoni, D. & Maio, D. (eds.), 2005, Biometric systems:
Technology, design and performance evaluaon, Springer Science & Business
Media, London, United Kingdom.
Zhu, Y., Tan, T. & Wang, Y., 2000, ‘Biometric personal idencaon based on iris
paerns’, in Proceedings 15th Internaonal conference on paern recognion,
ICPR-2000, vol. 2, pp. 801–804, IEEE.
Appendix 1 starts on the next page→

Page 12 of 12 Original Research
hp://www.sajim.co.za Open Access
TABLE 1-A1: Pearson correlaons results of biometric challenges and benets.
Variables Hackers Scammers Fraudsters Signature
authencaon
forgery
Lack of
guidance for
interacng
with the
system
Dust
dropped on
the ngerprint
scanner
Poor human
machine
interacons
Any form of
change in the
user’s nger
Involvement
in accident
Sensivity
of sensor
performance
Non-
technologically
inclined
individuals
Fake
ngerprint
forgery
Biometric
characteriscs
Biometric
informaon
leakage
Enhanced surveillance
Pearson’s correlaon -0.004 -0.019 0.007 0.038 0.094 -0.003 0.013 0.141 0.216** 0.202** 0.094 0.109 0.220** 0.024
Sig. (2-tailed) -0.963 0.803 0.929 0.620 0.220 0.973 0.867 0.064 0.004 0.008 0.220 0.153 0.004 0.752
N173 173 173 173 173 173 173 173 173 173 173 173 173 173
Enhanced Border control
Pearson’s correlaon -0.033 -0.053 -0.020 -0.094 -0.014 0.026 0.032 0.129 0.253** 0.205** 0.142 0.147 0.150*0.094
Sig. (2-tailed) 0.669 0.491 0.792 0.220 0.850 0.732 0.680 0.091 0.001 0.007 0.063 0.053 0.048 0.217
N173 173 173 173 173 173 173 173 173 173 173 173 173 173
Criminal idencaon
Pearson’s correlaon 0.009 -0.008 -0.008 0.048 0.008 0.012 -0.063 0.019 0.140 0.181*0.061 0.063 0.163*0.054
Sig. (2-tailed) 0.903 0.921 0.917 0.533 0.915 0.876 0.414 0.806 0.067 0.017 0.427 0.407 0.032 0.482
N173 173 173 173 173 173 173 173 173 173 173 173 173 173
Ease of informaon retrieval
Pearson’s correlaon 0.040 0.054 0.090 0.050 0.046 0.115 0.019 0.076 0.186*0.106 0.127 0.121 0.224** 0.110
Sig. (2-tailed) 0.604 0.484 0.237 0.514 0.545 0.132 0.806 0.321 0.014 0.165 0.096 0.114 0.003 0.151
N173 173 173 173 173 173 173 173 173 173 173 173 173 173
Strong matching algorithm
Pearson’s correlaon -0.036 -0.076 0.015 0.027 0.056 0.104 0.042 0.080 0.181*0.204** 0.200** 0.055 0.146 0.089
Sig. (2-tailed) 0.636 0.322 0.841 0.720 0.465 0.175 0.582 0.295 0.017 0.007 0.008 0.472 0.055 0.242
N173 173 173 173 173 173 173 173 173 173 173 173 173 173
Lost or stolen Smartcards
and mobile devices
Pearson’s correlaon 0.134 0.188*0.224** 0.076 0.145 0.094 0.076 0.046 0.102 0.076 0.214** 0.026 0.094 0.055
Sig. (2-tailed) 0.080 0.013 0.003 0.323 0.057 0.217 0.322 0.544 0.180 0.318 0.005 0.733 0.219 0.473
N173 173 173 173 173 173 173 173 173 173 173 173 173 173
Impossible to forget
Fingerprint authencaon
Pearson’s correlaon -0.032 0.093 0.118 0.101 0.144 0.042 0.087 0.011 0.085 0.062 0.187*0.022 0.155*−0.020
Sig. (2-tailed) 0.679 0.225 0.123 0.186 0.058 0.587 0.255 0.890 0.268 0.420 0.014 0.776 0.041 0.790
N173 173 173 173 173 173 173 173 173 173 173 173 173 173
Uniqueness
Pearson’s correlaon 0.030 0.090 0.097 0.117 0.133 0.138 0.007 0.028 0.194*0.155*0.247** 0.099 0.235** 0.039
Sig. (2-tailed) 0.698 0.240 0.205 0.124 0.082 0.071 0.928 0.716 0.011 0.042 0.001 0.194 0.002 0.614
N173 173 173 173 173 173 173 173 173 173 173 173 173 173
Forensic applicaon
Pearson’s correlaon -0.020 0.052 0.073 0.096 0.146 0.185*-0.010 0.081 0.236** 0.278** 0.273** 0.153*0.142 0.037
Sig. (2-tailed) 0.793 0.497 0.341 0.209 0.055 0.015 0.892 0.288 0.002 0.000 0.000 0.045 0.063 0.626
N173 173 173 173 173 173 173 173 173 173 173 173 173 173
*, Correlaon is signicant at the 0.05 level (2-tailed).
**, Correlaon is signicant at the 0.01 level (2-tailed).
Appendix 1