A Survey on Digital Payments Security: Recent Trends and Future Opportunities

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International Journal of Computer Trends and Technology Volume 69 Issue 8, 26-34, August 2021
ISSN: 2231 – 2803 / https://doi.org/10.14445/22312803/IJCTT-V69I8P107 © 2021 Seventh Sense Research Group®
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Review Article
A Survey on Digital Payments Security: Recent
Trends and Future Opportunities
Neha Priya1, Jawed Ahmed2
1M.Tech.CSE with specialization in Cyber Forensics and Information Security
2Assistant Professor, Department of Computer Science & Engineering, SEST, Jamia Hamdard
New Delhi, India.
Received Date: 15 July 2021
Revised Date: 17 August 2021
Accepted Date: 29 August 2021
Abstract - Digital payment technologies are growing very
fast in the sector of e-commerce and mobile banking. This
phenomenon has brought vast population to the cyber
space for online payments. However, the users are often
not aware of security aspects of online transactions. The
banking regulations mandate technology led security
interventions by intermediaries to protect customers from
cyber fraud in digital payments ecosystem. Our literature
survey shows the research trend in digital payments
security for the past one decade. We use a literature
classification framework for systematic literature review
on the theme of this work. IS security can impact digital
payments across three sectors- its growth motivation,
growth challenges and growth assurance respectively. We
discuss the recent trends to highlight the research gaps
and potential security application areas. We review the
literature across several prominent IT techniques used for
digital payments security and suggest future opportunities.
Keywords - Cyber fraud, Digital payment, IS security,
Mobile banking, Online payment, Systematic literature
review. I. INTRODUCTION
Digital technology gives fast and convenient lives, but
it comes at a cost- the threat of digital crimes. Even though
there is considerable awareness about digital crimes, they
continue to happen [1]. Further, digital services utilize
cyberspace to replace traditional methods of work and
business. The cyberspace is vulnerable to the risk of cyber
crimes infiltrating online economic and social activities
[2]. Banking industry has largely leveraged digital
technology to devise diversified financial products and
services [3]. A recent example is digital payment services
in money and banking, which has given ‘convenience' as
the key benefit and ‘security' as the key cost [4].
The digital payments ecosystem is a prudent example
of Information Technology (IT) adoption in the domain of
Information Systems (IS). Recent research trend on IT
adoption shows that mobile technology (which includes
mobile payments), e-commerce and internet banking are
among the top five IT adoption contexts in research and
mostly follow the Technology Acceptance Model (TAM)
[5]. The technology acceptance of digital payments is
dependent on factors affecting user attitude, intention and
behaviour [6]. Besides, ease of use and perceived
usefulness, trust and perceived risk are considered to be
prominent behavioural factors affecting digital payments
use [7]. Cyber frauds are identified as one of the risks in
digital payments technology adopted by banks [8]. The
incidence of cyber crime in online banking transactions on
smart phones, shows that there are mostly victims of bank
frauds cases, other cases being phishing and injection of
malware [9]. Bank fraud here refers to an unauthorized
online payment transaction. It is not only a cyber crime,
but also a financial crime [10]. Therefore, we would
alternatively refer to ‘cyber fraud in the digital payments
ecosystem’ as ‘digital payment fraud’.
For securing digital payment services, we understand
that IT adoption can bring forth three broad scopes in
security research, namely secured operations, security
analysis, and security controls which form the basis of our
literature classification framework. The objective of our
work is to highlight the recent research trends for securing
the digital payments ecosystem and find research gaps to
enable IT researchers to get future research directions.
In the rest of this article, follows section 2 with related
work to explain motivation for this work. Section 3
presents our literature classification framework and section
4 describes the research methodology. In section 5 we
discuss the results to highlight recent trends. Section 6
presents reviews of selected literature to find research
gaps. We conclude in section 7 with potential research
opportunities.
II. RELATED WORK
There exists several literature reviews which discuss
about security in any one of the digital payments
technology areas, such as, mobile payments [11, 9, 12, 13]
or payment cards [14, 15, 16]. We hold this observation
from the reviews published in two research databases, the
Springer Link and ScienceDirect, where these articles
mentioned associated keywords in their title or abstract. In
[11], authors explore the state of mobile payment
technology access for elderly with major concerns
including security, trust and privacy. In [9], a review on

Neha Priya & Jawed Ahmed / IJCTT, 69(8), 26-34, 2021
27
the vulnerabilities in android mobile phones for banking
transactions is presented based on the incidence of attacks.
In [12], a review on region specific adoption of mobile
payments showed that the focus and grey areas include
security technology and implementation. Another review
on mobile payments adoption highlights initial trust and
perceived risk as one of the driving factors for user
acceptance [13]. For payment card fraud detection, AI and
machine learning (ML) methods which are effective for
use by industry, are reviewed in [14]. In [15], the author
has discussed the scope of data mining techniques in
preventing frauds in online card payments. In [16], authors
compare ML techniques for predicting credit card fraud
and their performance metrics.
The status of digital payments sector, as a whole, has
not been reviewed comprehensively in the past decade
from the perspective of security research. This research
addresses the need for reviewing the emerging research
trend in IT for securing digital payments ecosystem.
Security is identified as one of the independent variables in
IT adoption [5]. A distribution of independent variables
over research trend, shows that ‘security’ has
comparatively less research contributions, while another
independent variable ‘trust’ has the fourth most prominent
position in research [5]. However, in the context of digital
payments, security cannot be looked independently of trust
as shown by [17]. Banks hold relation of trust with their
customers for ensuring protection of customers’ money
through security solutions [18]. Security builds trust of
customers in online banking and trust leads to positive
growth [5, 19]. Therefore, we focus on the recent trends in
IS security research for digital payments growth.
III. LITERATURE CLASSIFICATION
FRAMEWORK
In this section, we define a three factors based
literature classification framework which assess the impact
of IS security research on digital payments growth. For a
systematic review of literature from the past decade, this
framework comprises following growth factors:
A. Growth Motivation
In this category, we classify those literature whose
focus is on adoption of IT techniques in new product
designs or updates, in order to ensure deployment of
secured products.
B. Growth Challenges
This category refers to the need of technology to
discover vulnerabilities and other growth challenges.
Literature which address these challenges using IT
techniques in research, are classified here.
C. Growth Assurance
The research focus area of literature classified into this
category, includes IT techniques adopted for deployment
of security controls which assure trust led growth in digital
payments ecosystem.
IV. METHODOLOGY
We perform a systematic review based on the
guidelines for literature review methodologies discussed in
[20]. Our research method comprises five steps as shown
in Fig. 1. It is drawn from the approaches suggested by
[11] and [21]. First, the scope of review is defined to
enable convergence of search results. The next step is
searching in the literature databases, with suitable search
string containing keywords. This is followed by screening
of preliminary search results to narrow down the content
for review. After screening, the selected research articles
are represented in the form of illustrations to answer the
review questions. The results are analysed and discussed.
A. Defining Scope
The goal of this literature review is to discover the
research contributions in IT which have the potential to
improve security solutions for digital payments. We aim
to search and analyse research articles published in the last
ten years since 2010, which are in scope of our literature
classification framework.
B. Searching
The search process can be divided into these tasks:
defining sources, developing search string and
implementing search. The associated search areas are
digital payment systems, Information Technology and
security applications. The well known academic database
ScienceDirect is selected as the literature source, due to its
extensive coverage of the topic and its content search
feasibility. In our literature search, we have included only
those research articles which are published in journals or in
conference proceedings. Both full-text as well as preview-
only articles are retained in search results. We limit the
search results to the discipline of IS or Computer Science.
C. Screening
The screening step is a filtering stage. It includes both
exclusion and inclusion criteria for reducing the number of
studies in our review, as shown in Fig. 2. First, we go
through the title and keywords of 441 articles obtained
from the search procedure and apply the exclusion criteria
to 121 articles which are unrelated to ‘digital payment OR
security research'. For the remaining 320 articles, we read
their abstract and apply another exclusion criteria of
research based on empirical findings. We also exclude,
from our review, articles related to ‘digital money or
virtual currency’ and articles related to ‘offline e-cash or
conditional e-payment'. At this stage, we apply the
inclusion criteria of articles related to ‘digital payments
AND security research’. Finally, we obtained 64 articles
after screening and read these literature for further
investigation.
We construct our search string from keywords, as
suggested in [21]. For efficient search, one word as well
as two words based keywords are used. Literature reviews,
discussed in this research so far, do not give exhaustive list
of keywords to address our scope of review. So, we
selectively combine keywords from [11], [9] and [14]. The
resultant search string is:

Neha Priya & Jawed Ahmed / IJCTT, 69(8), 26-34, 2021
28
(Mobile banking OR m-banking OR mobile payment
OR m-payment OR online banking OR internet banking
OR online payment OR electronic payment OR digital
payment OR online transaction OR digital transaction OR
payment card OR credit card)
AND
(Security OR security control OR security measure
OR intelligence OR payment fraud OR information
technology)
Our preliminary search results gave 858 research
articles, where 186 articles were available as full-text. We
have to manually exclude articles not related to IS or
Computer Science discipline by going through the title of
their journal or proceedings. Consequently, we got 441
articles for further screening.
D. Research Questions
To begin with the review of literatures, two research
questions are proposed for literature analysis. Our aim is to
find the yearly distribution of selected
articles over the literature classification framework by
answering the following research questions:
• Which Security application areas are in focus of
research?
• Which IT techniques are emerging areas of security
research?
E. Analysis of Results
The goal of literature analysis is to identify prominent
research gaps and potential research contributions. We
analyse the results containing literature distribution and
obtain an integrated outlook to the situation of security
research for digital payments growth.
Fig. 1 Research methodology for systematic literature review
Fig. 2 Literature search and screening process chart
V. RESULTS AND DISCUSSION
Research articles are classified under three digital
payment growth factors, namely growth motivation, growth
challenges and growth assurance, according to our literature
classification framework. For each growth factor, we further
identify research contributions into various security
application areas and corresponding IT techniques are also
listed. Thus, we get the distribution of research articles to
answer our research questions. Table 1 reveals that number
of researches published is apparently high in five security
application areas, in the order as Authentication mechanism;
Fraud detection; Policy and law; Secure payment protocols;
and Payment privacy.
We observe from Table 2 and Fig. 3 that over the closing
decade (2010-2020), there is general expansion in use of IT
techniques for securing digital payments ecosystem. It is
notable that security led growth motivation is in rise over the
second half of the decade. For addressing growth challenges,
the research community appears to be making low but regular

Neha Priya & Jawed Ahmed / IJCTT, 69(8), 26-34, 2021
29
contribution over the decade. A more interesting observation
has emerged from the third part of literature classification
framework, the Growth assurance. Due to breakthrough IT
techniques such as Machine learning and big data in the
recent times, research work is concentrated in second half of
this decade. Thus, the distribution of research articles
highlight the trending security applications areas and their
emerging IT techniques.
Table 1. Distribution of research articles as per literature classification framework
Framework
Security Application areas
IT Techniques
No. of
articles
Growth
Motivation
(32 articles)
Authentication mechanism
Biometrics [23, 27, 40, 45, 68, 69];
Elliptic curve cryptography (ECC) [30, 82];
Key management [33, 64]; Graphical password[36];
Cryptosystem [46, 47, 52, 62, 72]; Steganography [65];
Signcryption [83]
18
Payment privacy
Digital Signature [24, 28, 77, 84];
Secure Element (SE) in mobile device [81];
2-paymrnt gateway based on cryptography [85];
6
Secure Payment Protocol
Encryption protocol [26, 51, 80]; Tokenset optimisation
[32]
Application layer message format [66]; ECC [67, 78];
Subliminal channels [76]
8
Growth
Challenges
(14 articles)
Cyber attack analysis
Digital forensics [25]; cryptanalysis [70]
2
Skimming attack analysis
Computer forensics [44]
1
Mobile app vulnerability
Mobile forensics [55]
1
Phishing threat avoidance
Security awareness [43, 63]
2
Policy and Law
Digital signature law [34];
Payment Service Directives (PSD) [38, 49];
Liability rules [48, 56]; Data protection [53];
Service agreement [58]; Electronic evidence [60]
8
Growth
Assurance
(18 articles)
Malware detection
Machine learning [22, 29, 74]
3
Spoof attack detection
Biometric anti-spoofing [73]
1
Detecting replicated 2-D
codes
Watermark extraction [75]
1
Phishing detection
Machine learning [35]; Genetic algorithm [61];
Fuzzy system [71]
3
Fraud detection
Big data analytics [31, 59]; Wavelet transform [37];
Data intelligence [39]; Machine learning [41, 50];
Deep learning [42]; Sequence classification [54];
Network analysis [57]; Semi-supervised clustering [79]
10
Table 2. Distribution of research articles based on security application areas
Growth Motivation
Growth Challenges
Growth Assurance
Year of
Publication
No. of articles
Authentication
mechanism
Payment
privacy
Secure
payment
Protocol
No. of articles
Cyber attack
analysis
Skimming
attack analysis
Mobile app
vulnerability
Phishing
threat
avoidance
Policy and
Law
No. of articles
Malware
detection
Spoof attack
detection
Detecting
replicated 2-D
codes
Phishing
detection
Fraud
detection
2010
1
27
2011
3
82
66,76
1
34
2012
4
52,83
26,80
1
70
2013
2
36,72
3
63
48,6
0
2014
2
33,65
2
44
43
2015
2
25
58
4
73
71
57,

Neha Priya & Jawed Ahmed / IJCTT, 69(8), 26-34, 2021
30
Fig. 3 Security research trend based on literature classification framework
VI. LITERATURE REVIEW
To find the research gaps, we have reviewed selected
articles corresponding to each digital payment growth
factor as defined in our literature classification framework.
The emerging IT techniques for security research are
emphasized for each contribution area.
A. Increasing Growth Motivation by security features
Security research on authentication, privacy and non-
repudiation and payment protocols increase motivation for
deployment of new products. Secured operation of digital
payment transactions enable technology acceptance.
Emerging research techniques in this area are:
a) Biometric Authentication
Biometric data such as fingerprints and face
recognition are used for authentication of user accounts. It
is more secure than traditional authentication mechanism
based on alphanumeric passwords. However, biometric
information need to be secured themselves. There is
current research focus to improve reliability of biometric
information used for authorizing online payments. In [68],
(2016), authors propose finger, iris and palm print for
physical biometrics and encrypt this data to prevent leak of
sensitive data. In [40], (2020), authors emphasize on the
future scope of voice or video selfie as biometrics, other
than fingerprints, for authorization of online banking
transactions. In a latest research topic on behavioural
biometrics, keystroke dynamics is characterized and
adjusted for aging effect to provide accuracy in
authentication ([23], 2019). In [69], (2019), authors have
proposed a continuous authentication mechanism which
captures behavioural biometric data from sensors of
android phones for in mobile banking application.
b) Cryptographic Security
Cryptography is commonly used in cryptosystems,
key management tasks, encryption protocols and digital
signatures. Cryptosystem provides security functions such
as authentication, confidentiality and non-repudiation.
When two different keys, the public and private key, are
used in a cryptosystem, the technique is called asymmetric
key cryptography. Security research trend shows that
asymmetric key or public key algorithms have been a
popular research area over the last decade. The use of
elliptic curve cryptography is trending in security research
for authentication of transactions and secured payment
protocols [78, 30, 67]. Recent research topics based on the
79
2016
8
64,68
24,77
, 81
51,32,
78
2
38,5
6
2
75
35
2017
4
30,47
28,85
1
55
2018
3
45,62
84
1
49
5
29
31,
59,
41,
54
2019
3
23,69
67
6
22,
74
37,
39,
50,
42
2020
2
40,46
1
53
1
61
Tota
l
18
6
8
2
1
1
2
8
3
1
1
3
10

Neha Priya & Jawed Ahmed / IJCTT, 69(8), 26-34, 2021
31
application of asymmetric key cryptography are: multi-
layer encryption algorithms for improving security ([46],
2020); a certificate less encrypted signature for user
authentication ([47], 2017); one-time username to replace
predefined credentials for access control ([62], 2018); use
of cryptography over 2 payment gateways for anonymity
of mobile payments ([85], 2017). Key management is
another important application area of public key
cryptography. It depends on Public Key Infrastructure
(PKI) for key generation and certification. The key
research topics in the last decade were as: improving
security of certificates distribution ([64], 2016); and use of
partner key to eliminate the need for certificate validation
([33], 2014). Besides authentication, research for payment
privacy and non-repudiation services are also in recent
trend. Computational tasks, such as security verifications,
are outsourced to a cloud server to save the limited
resources of mobile devices. Since the outsourced cloud
environment is unsecured, digital signature based payment
protocols are one of the recent research topics, to improve
anonymity and security of mobile wallet transactions [77,
28, 84].
B. Addressing Growth Challenges by Security
Countermeasures
Growth challenges include vulnerabilities and threats
in digital payments environment, past experiences of
security attacks and existing security policy paralysis.
Security analysis measures focus on identification of
vulnerabilities, analysis of threats and attacks and review
of critical policies or laws by security experts. Literature
review results in Fig. 3 show consistent research efforts
over the last decade to manage the growth challenges.
However, the focus here is less than growth motivation or
assurance contribution areas. Two emerging research
techniques, namely digital forensics and security policy
review are highlighted for future research directions.
a) Digital Forensics
Digital forensics research includes other sub-domains
like computer forensics, mobile forensics and cyber
forensics. With the growth of mobile payments, mobile
apps are widely used. Mobile forensics research can
enable analysis of any security attacks on the mobile
devices. Digital forensic techniques may not only be used
for post-incident analysis but also for validation of
potential threats before an actual attack could take place
[55].
b) Policy Evaluation
The area of security policy includes formulation and
alteration of rules, regulations, guidelines, directives, laws
issued by competent authorities with validity over a certain
jurisdiction and timeframe. The policy research trend for
digital payments domain relates to the current issues and
concerns of the stakeholders, such as: liability ownership
and the burden of proof [56], security authentication
services [38], consumer protection through service
agreement [58]. When the European Union mandated all
banks to provide application programming interface or API
so that third party payment system operators (PSO) could
manage online payments by customers, this raised the
issue of cybersecurity for customer’s financial data [49]. A
most recent policy research topic is evaluation of personal
data protection regulations in digital payments ecosystem
[53]. Existing policy and laws are growth challenges,
given the evolving Financial technology (FinTech) sector.
Future research directions include analysis of global and
regional security conditions to suggest policy reforms.
C. Building Growth Assurance by Security Controls
The elements of growth assurance include additional
security controls over normal operations of digital payment
services. The primary goal of security applications is
detection of cyberattacks on online payments, which are
also financial crimes. Various classification algorithms are
used in research for prediction of attacks and performance
evaluation. Emerging IT techniques are machine learning,
soft computing and big data analytics.
a) Machine Learning
Recent trend shows wide use of ML algorithms for
detection of credit card frauds, phishing websites in
internet banking and malwares in mobile applications. In
[50], credit card frauds are detected in streaming
transactions, by analysing each transaction and sending
feedback for learning past transactional patterns. With the
evolution of mobile payments technology, malware
detection in android applications is a potential research
area [22, 29, 74]. Research method for selection of a ML
algorithm for the model, uses performance comparisons of
different ML algorithms to determine the best model [22,
50].
b) Soft Computing
Even though, machine learning techniques are widely
in binary classification problems, certain data limitations
exist such as insufficient data and uncertainty. Soft
computing techniques are utilized in ML problems with
uncertainty and approximation of data. In [41], the fraud
classifier is customized by a hyper-heuristic evolutionary
algorithm which automatically selects components of the
classifier model to suit the nature of input datasets.
Another fraud classification problem is training data
imbalance since the class of frauds is of much smaller size
than the class of genuine transactions. In [42], authors have
proposed a generative deep learning model to augment the
data in fraud class for supervised machine learning. Soft
computing techniques increase the effectiveness of fraud
predictions. For detection of phishing attacks, features
extraction and feature selection algorithms are important
research areas in soft computing. In [71], phishing
indicators are identified from Iranian e-banking websites
and a fuzzy expert system is used for phishing detection. In
[61] A genetic algorithm based feature selection method is
proposed for classification of phishing websites.
c) Big Data Analytics
The volume of online payment transactions is
increasingly large streaming data input for fraud
prediction. Big data tools are integrated with machine
learning algorithm, for scalability of fraud prediction

Neha Priya & Jawed Ahmed / IJCTT, 69(8), 26-34, 2021
32
models [59]. A big data interface, Hadoop, is used for
storage and processing of the transactions data from the
source to an analytical server, for prediction modelling.
This approach increases the prediction performance of the
ML algorithm used for classification [31]. To overcome
data imbalance in big data problem, in [39] the authors
have proposed a data intelligence based multiple consensus
model, where probabilistic and majority voting of
classification algorithms is combined for in large real
dataset.
The digital payments’ growth challenges continue to
exist with the evolving information and communications
technology (ICT). Hence, there remains the scope of
persistent research directions arising from this sector.
Financial and cyber crimes in digital payments, one of the
growth challenges, are addressed by crime analysis, an
important research topic in security. At this point, we make
a note that security contributions, under Growth assurance,
are making constant effort to detect and prevent crime
events in real time. This lowers the incidence of crime and
consequently, the activity burden of crime analysis is also
reduced. Taking inspiration from this, we suggest a
research direction where the crime analysis efforts can be
further eased by using Growth assurance techniques such
as data intelligence. Recent trend shows that data
intelligence is an emerging but less researched technique
for fraud detection. We suggest that, data intelligence can
be utilized for fraud data analytics during investigation of
crime cases. The volume of crime data is not big data.
Therefore, crime data intelligence is a potential future
research direction for disposal of crime cases in digital
payments and also, in general cyber crimes, reported for
investigation. VII. CONCLUSION
The purpose of our literature review was to find
security research trend over the past decade in the field of
digital payments. The proposed systematic literature
review methodology was used to define the research scope
and important research questions. It provided an effective
literature search and screening process. The literature
classification framework represented research articles from
different security perspectives. Our review results help us
to discover past decadal research trend, current research
gaps and future research directions. Since, research under
Growth assurance has been at high rising trend recently,
security applications under this head are promising
research themes, especially fraud detection. The digital
payments’ growth challenges combined with growth
assurance factors have scope of future IS security research.
Areas where research contribution is low, such as cyber
forensics and data intelligence, are potential research
opportunities.
This work has used only selective databases and
therefore, the trend outcome may be limited by the types of
documents and databases used for literature survey. There
is scope to combine the research trends with real life
digital payments statistics, like volume of transactions or
cyber fraud incidence, for better understanding of research
problems.
ACKNOWLEDGMENT
We acknowledge the use of online databases at
SpringerLink and ScienceDirect for browsing scientific
documents. The line chart in this work was constructed
using online drawing tool and workspace of Visual
Paradigm Online.
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