Available via license: CC BY 4.0
Content may be subject to copyright.
Citation: Che Mohd Salleh, M.; Nor,
R.M.; Yusof, F.; Amiruzzaman, M.
Constructing a Shariah Document
Screening Prototype Based on
Serverless Architecture. Computers
2023,12, 50. https://doi.org/
10.3390/computers12030050
Academic Editor: Fernando Bobillo
Received: 4 January 2023
Revised: 13 February 2023
Accepted: 14 February 2023
Published: 24 February 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
computers
Article
Constructing a Shariah Document Screening Prototype Based
on Serverless Architecture
Marhanum Che Mohd Salleh 1, *, Rizal Mohd Nor 2, Faizal Yusof 3and Md Amiruzzaman 4
1Department of Finance, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia
2
Department of Computer Science, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia
3Faculty of Resilience, Rabdan Academy, 65 Al Inshirah Street, Al Sa’adah,
Abu Dhabi 22401, United Arab Emirates
4Department of Computer Science, West Chester University, West Chester, PA 19383, USA
*Correspondence: marhanum@iium.edu.my
Abstract:
The aim of this research is to discuss the groundwork of building an Islamic Banking
Document Screening Prototype based on a serverless architecture framework. This research first
forms an algorithm for document matching based Vector Space Model (VCM) and adopts Levenshtein
Distance for similarity setting. Product proposals will become a query, and policy documents by
the central bank will be a corpus or database for document matching. Both the query and corpus
went through preprocessing stage prior to similarity analysis. One set of queries with two sets
of corpora is tested in this research to compare similarity values. Finally, a prototype of Shariah
Document Screening is built based on a serverless architecture framework and ReactJS interface.
This research is the first attempt to introduce a Shariah document screening prototype based on a
serverless architecture technology that would be useful to the Islamic financial industry towards
achieving a Shariah-compliant business. Given the development of Fintech, the output of this research
study would be a complement to the existing Fintech applications, which focus on ensuring the
Islamic nature of the businesses.
Keywords: Islamic banking; document screening; Levenshtein distance; serverless architecture
1. Background of Research
Digitalization is transforming how people interact and conduct daily business. The
advancements in banking technology have influenced the future of financial services
around the world. The establishment of digital banking is now in its growing stage where
the way financial services are offered to all is positively accepted, especially by our new
generation of digitally savvy millennials and Gen Zers. Through technology, the business
is leaning more towards business-to-customer (BTC), where the financial services are
directly reached to the customer at a fingertip and within seconds. The usage of artificial
intelligence (AI) has complimented the roles of humans as financial advisors, and through
blockchain technology, the whole financial system becomes efficient to tie all segments
of banking businesses. Apart from the finance industry, Machine learning (ML), being a
vast area within artificial intelligence (AI), has revolutionized many industries and fields.
Meta-heuristic optimization algorithm based on a hybrid of the Sine Cosine Algorithm
(SCA) and the Grey Wolf Optimizer (GWO) can be used to train the Multilayer Perceptron
(MLP) neural network [1].
Despite all the advantages that technology has brought to the financial market, there
are a few segments that still need attention to be digitalized, especially the Islamic banking
business, which requires attention on Shariah compliancy in all aspects. It is observed that
there is a limitation of technology usage in product structuring, mainly on documentation,
which is conducted manually by bank officers. Compared to conventional products, Islamic
banking products require more documents based on the nature of the contracts adopted.
Computers 2023,12, 50. https://doi.org/10.3390/computers12030050 https://www.mdpi.com/journal/computers
Computers 2023,12, 50 2 of 15
It is the duty of Shariah officers to ensure that all documents comply with the Shariah
and the central bank policy in the process of structuring new products. This duty would
require much effort, time, and energy to verify that the product will not have any element of
Shariah non-compliance. Hence, the advancement of Natural Language Processing (NLP)
technology would be necessary and significant to assist this process and would reduce the
burden of the Shariah officers. The existence of serverless technology would ensure the
process of software development for Shariah screening is shortened. The development
team can release applications, analyze user feedback, and iterate improvements faster.
Obviously, there are a limited number of Robo Advisors offered in the financial
market, and the focus is merely on digital investment management services. Based on
observation, there are five Robo Advisors currently in the Malaysian financial market,
which are MyTheo, StashAway, BEST, Wahed Invest, and Raiz. Unfortunately, none of these
Robo Advisors have aided the Islamic banks regarding the Shariah-compliant status of the
products during the structuring process. Based on the suggestion by [
2
], the involvement
of Robo advisory will ease the product structuring process by evaluating the sources of
Shariah to provide necessary information for the physical Shariah advisor to accomplish the
ruling accordingly. This effort may be started with the development of an NLP algorithm
for document matching or similarity screening.
Moreover, there are several Shariah non-compliant cases among Islamic banks in
Malaysia where the root cause is documentation issues [
3
]. As reported, most of the
cases are related to the contract documentation, calculation of Ta’widh/Ibra’, and term
of conditions which contradict the nature of the contract (sample of cases: CIMB Islamic
Bank Bhd v LCL Corp Bhd & Anor [2012] 3 MLJ 869 and one Court of Appeal case of Pan
Northern Air Services Sdn Bhd v Maybank Islamic Bhd and another appeal [2021] 3 MLJ
408). The existence of an efficient platform (a mechanism) to retrieve relevant documents
efficiently would assist the Shariah officer as well as the Shariah Committee in conducting
earlier screening of the Shariah status of the documents/products proposed.
This paper is structured in the following manner: it starts with a research background
and is followed by a discussion of the past literature, mainly on financial technology
adoption in the banking industry, updates on the NLP literature, and serverless architecture.
This is followed by research methodology and the suggestion of a Shariah document
screening prototype. It ends with a discussion and suggestions for future research.
2. Past Literature
2.1. Adoption of Financial Technology (Fintech) in the Financial Industry
The emergence of the Islamic financial industry since the 1960s has brought the
practice of a dual banking system to the world. The system has been recognized not only
in Muslim countries but also by non-Muslim countries. This recognition is something that
Muslims must be proud of as the world has respected Islamic law (Shariah) as the main
law for Islamic financial businesses. This is because the aim of Shariah is to safeguard
all aspects of human beings, and this is the missing part of the conventional financial
system. The objective of the Shariah (Maqasid Shariah), which is comprised of five elements
(religion/belief, lineage, wealth, intellect, and life), became the main aspect whenever
Islamic financial products were offered in the market.
Accordingly, with the strong support from the authority and Shariah advisory commit-
tees both at the local and international level, such as the Shariah Advisory Council of the
Central Bank of Malaysia and the International Islamic Fiqh Academy, which is comprised
of 57 member states of the Organization of Islamic Cooperation (OIC), any issues which
arise pertaining to the industry is discussed and solved at various levels. Now, the industry
has been developed, remains competitive with its conventional counterparts, and is evolv-
ing with financial technology. As an update on the Malaysian Islamic financial industry,
the central bank granted five digital banks licenses recently on April 2022, which are;
Licensed under the Financial Services Act 2013 (FSA):
Computers 2023,12, 50 3 of 15
i. a consortium of Boost Holdings Sdn. Bhd. (Kuala Lumpur, Malaysia) and RHB Bank
Berhad;
ii.
a consortium led by GXS Bank Pte. Ltd. (Singapore) and Kuok Brothers Sdn. Bhd
(Kuala Lumpur, Malaysia); and
iii.
a consortium led by Sea Limited (Singapore) and YTL Digital Capital Sdn Bhd (Kuala
Lumpur, Malaysia).
Licensed under the Islamic Financial Services Act 2013 (IFSA):
i.
a consortium of AEON Financial Service Co., Ltd. (Tokyo, Japan), AEON Credit Service
(Hong Kong, China) (M) Berhad and MoneyLion Inc. (New York, NY, USA); and
ii. a consortium led by KAF Investment Bank Sdn. Bhd.
Three of the five consortiums are majority-owned by Malaysians, namely Boost Hold-
ings and RHB Bank Berhad, Sea Limited, and YTL Digital Capital Sdn. Bhd. and KAF
Investment Bank Sdn. Bhd.
2.2. Fintech and Banking Business
The current fintech environment has opened unprecedented opportunities for banking
businesses and their customers. It is undeniable that financial services have become faster
and easy to use where money can be transferred within a few seconds, and financing
products can be approved within an hour. Technology has replaced human roles in provid-
ing financial services. Scholars have agreed that fintech somehow has indirectly brought
negative effects to the traditional banking system [
4
,
5
]. Fintech has brought positive effects
to the banking system, as reported by researchers, such as a digital calculator for bank
charges using information asymmetry [
6
], detection of anomalies in data streaming [
7
],
digital signature for bank customers using a functional symmetry approach [
8
], and others.
In Malaysia, the government, through the central bank, has supported the emergence
of technology in the Malaysian financial system. Among the initiative of the bank is to
capitalize on digitalization which is stated in Malaysia Financial Sector Blueprint 2022–
2026. The bank has provided a few technology infrastructures as a backbone of the digital
economy, which includes real-time payment systems and a few guidelines on fintech. After
the successful establishment of digital banks, the central bank issued a discussion paper
on the Licensing Framework for Digital Insurers and Takaful operators in February 2022.
Other standards or guidelines that were set by the bank are the policy on Risk Management
in Technology (2020), the Financial Technology Regulatory Sandbox Framework (2016),
and the Minimum Guideline on the Provision of Internet Banking Services by Licenced
Baking Institutions (2014).
Overall, the technology has also opened the door to new businesses related to financial
services (fintech business), which has become a challenge to the existing financial institu-
tions. However, it is observed that the Malaysian financial market is healthy enough to
welcome newcomers where they have complimented each other and even become strategic
partners. In this context, the Malaysian government has been supportive, and among the
initiatives given to the small and medium enterprises fintech companies are tax angel incen-
tives (granted to angel investors in fintech start-ups), income tax exemption by Malaysian
Industrial Development Authority (MIDA), partial corporate tax exemption for entities
in the Malaysian Digital Hub under MDEC, Malaysia Tech Entrepreneur Program under
MDEC, Multimedia Super Corridor (MSC) Malaysia status recognition for ICT, and others.
In the Islamic finance industry [
9
], we have suggested the usage of AI in NLP based
on the Islamic FinTech Model that combines Zakat and Qardh-Al-Hasan (benevolent loan)
to minimize the negative impact of COVID-19 on individuals and SMEs. The authors
believed that Islamic finance has big potential to face any kind of situation/pandemic,
especially the combination of Zakat and Qardh-Al-Hasan. In addition, research conducted
by [
10
] and others [
11
,
12
] have explored the potential of Fintech, which includes AI, smart
contract, blockchain, and others in the Islamic banking and finance industry in various
Asian countries and their findings indicate that Fintech would benefit the industry greatly to
be at par with the conventional counterparts. Hence, a lot of effort is needed to implement
Computers 2023,12, 50 4 of 15
Fintech in the Islamic financial industry so that it would have a significant impact on society
and be practiced with the Shariah spirit.
Figure 1presents the increasing trend in the literature on NLP based on the Scopus
database in all areas of research, including social science, arts, engineering, computer
sciences, business, management, economics, and others. Since 2016, the number of literature
studies continued to increase until 2021 (2045 articles) and is expected to increase further
until the end of this year. Since 1976, a total of ten thousand articles have been published
in the Scopus database, and the author that has contributed a lot in this domain is Liu, H,
with 77 articles. It is followed by Xu, H and Friedman, C. Please refer to Figure 2.
Computers 2023, 12, x FOR PEER REVIEW 4 of 17
In the Islamic finance industry [9], we have suggested the usage of AI in NLP based
on the Islamic FinTech Model that combines Zakat and Qardh-Al-Hasan (benevolent loan)
to minimize the negative impact of COVID-19 on individuals and SMEs. The authors be-
lieved that Islamic finance has big potential to face any kind of situation/pandemic, espe-
cially the combination of Zakat and Qardh-Al-Hasan. In addition, research conducted by
[10] and others [11,12] have explored the potential of Fintech, which includes AI, smart
contract, blockchain, and others in the Islamic banking and finance industry in various
Asian countries and their findings indicate that Fintech would benefit the industry greatly
to be at par with the conventional counterparts. Hence, a lot of effort is needed to imple-
ment Fintech in the Islamic financial industry so that it would have a significant impact
on society and be practiced with the Shariah spirit.
Figure 1 presents the increasing trend in the literature on NLP based on the Scopus
database in all areas of research, including social science, arts, engineering, computer sci-
ences, business, management, economics, and others. Since 2016, the number of literature
studies continued to increase until 2021 (2045 articles) and is expected to increase further
until the end of this year. Since 1976, a total of ten thousand articles have been published
in the Scopus database, and the author that has contributed a lot in this domain is Liu, H,
with 77 articles. It is followed by Xu, H and Friedman, C. Please refer to Figure 2.
Figure 1. Number of literature studies on NLP by year.
Figure 1. Number of literature studies on NLP by year.
Computers 2023, 12, x FOR PEER REVIEW 4 of 17
In the Islamic finance industry [9], we have suggested the usage of AI in NLP based
on the Islamic FinTech Model that combines Zakat and Qardh-Al-Hasan (benevolent loan)
to minimize the negative impact of COVID-19 on individuals and SMEs. The authors be-
lieved that Islamic finance has big potential to face any kind of situation/pandemic, espe-
cially the combination of Zakat and Qardh-Al-Hasan. In addition, research conducted by
[10] and others [11,12] have explored the potential of Fintech, which includes AI, smart
contract, blockchain, and others in the Islamic banking and finance industry in various
Asian countries and their findings indicate that Fintech would benefit the industry greatly
to be at par with the conventional counterparts. Hence, a lot of effort is needed to imple-
ment Fintech in the Islamic financial industry so that it would have a significant impact
on society and be practiced with the Shariah spirit.
Figure 1 presents the increasing trend in the literature on NLP based on the Scopus
database in all areas of research, including social science, arts, engineering, computer sci-
ences, business, management, economics, and others. Since 2016, the number of literature
studies continued to increase until 2021 (2045 articles) and is expected to increase further
until the end of this year. Since 1976, a total of ten thousand articles have been published
in the Scopus database, and the author that has contributed a lot in this domain is Liu, H,
with 77 articles. It is followed by Xu, H and Friedman, C. Please refer to Figure 2.
Figure 1. Number of literature studies on NLP by year.
Figure 2. Total number of NLP literature studies by author.
Accordingly, as presented in Figure 3below, the United States has been dominant in
discussing NLP (2642 articles), followed by China (1542 articles), India (1356 articles), and
the United Kingdom (573 articles). It is undeniable that these countries have been at the
forefront of technology adoption. It is also not a surprise that NLP is mainly conducted
by researchers from computer science (37.3%) and engineering (14.3%). Other areas that
have adopted NLP are medicine, social science, arts, physics, business, management, and
accounting. Please refer to Figure 4
Computers 2023,12, 50 5 of 15
Computers 2023, 12, x FOR PEER REVIEW 5 of 17
Figure 2. Total number of NLP literature studies by author.
Accordingly, as presented in Figure 3 below, the United States has been dominant in
discussing NLP (2642 articles), followed by China (1542 articles), India (1356 articles), and
the United Kingdom (573 articles). It is undeniable that these countries have been at the
forefront of technology adoption. It is also not a surprise that NLP is mainly conducted
by researchers from computer science (37.3%) and engineering (14.3%). Other areas that
have adopted NLP are medicine, social science, arts, physics, business, management, and
accounting. Please refer to Figure 4
Figure 3. Countries that have contributed to the NLP literature.
Figure 4. The literature on NLP by subject area.
In addition, out of ten thousand literature studies on Natural Language Processing
(NLP) from various areas, this research has limited the search of the subject area to only
social sciences, business, management, and accounting. As in Figure 4, there are 1712 ar-
ticles found in these areas. Bhattacharyya, P., Ekbal, A., and Korhonen, A. have contrib-
uted equally to the literature.
This research further defined the search for the economics, finance, business, man-
agement, and accounting subject areas (please refer to Figure 5). The results indicate that
there are 178 articles with an increasing trend, which is expected to increase until 2022.
Please refer to Figure 6. The main contributors are Coffas, Delcea, and Melumad, and each
of them wrote three articles on NLP between the year 2019–2020 (please refer to Figure 7).
Most authors have adopted NLP in social media research, either in terms of sentiment
analysis based on image evaluation, customers’ product opinions, social media users’
emotions, or on consumers' vocabularies. The most cited article was by Melumad (2019)
Figure 3. Countries that have contributed to the NLP literature.
Computers 2023, 12, x FOR PEER REVIEW 5 of 17
Figure 2. Total number of NLP literature studies by author.
Accordingly, as presented in Figure 3 below, the United States has been dominant in
discussing NLP (2642 articles), followed by China (1542 articles), India (1356 articles), and
the United Kingdom (573 articles). It is undeniable that these countries have been at the
forefront of technology adoption. It is also not a surprise that NLP is mainly conducted
by researchers from computer science (37.3%) and engineering (14.3%). Other areas that
have adopted NLP are medicine, social science, arts, physics, business, management, and
accounting. Please refer to Figure 4
Figure 3. Countries that have contributed to the NLP literature.
Figure 4. The literature on NLP by subject area.
In addition, out of ten thousand literature studies on Natural Language Processing
(NLP) from various areas, this research has limited the search of the subject area to only
social sciences, business, management, and accounting. As in Figure 4, there are 1712 ar-
ticles found in these areas. Bhattacharyya, P., Ekbal, A., and Korhonen, A. have contrib-
uted equally to the literature.
This research further defined the search for the economics, finance, business, man-
agement, and accounting subject areas (please refer to Figure 5). The results indicate that
there are 178 articles with an increasing trend, which is expected to increase until 2022.
Please refer to Figure 6. The main contributors are Coffas, Delcea, and Melumad, and each
of them wrote three articles on NLP between the year 2019–2020 (please refer to Figure 7).
Most authors have adopted NLP in social media research, either in terms of sentiment
analysis based on image evaluation, customers’ product opinions, social media users’
emotions, or on consumers' vocabularies. The most cited article was by Melumad (2019)
Figure 4. The literature on NLP by subject area.
In addition, out of ten thousand literature studies on Natural Language Processing
(NLP) from various areas, this research has limited the search of the subject area to only
social sciences, business, management, and accounting. As in Figure 4, there are 1712 arti-
cles found in these areas. Bhattacharyya, P., Ekbal, A., and Korhonen, A. have contributed
equally to the literature.
This research further defined the search for the economics, finance, business, manage-
ment, and accounting subject areas (please refer to Figure 5). The results indicate that there
are 178 articles with an increasing trend, which is expected to increase until 2022. Please
refer to Figure 6. The main contributors are Coffas, Delcea, and Melumad, and each of them
wrote three articles on NLP between the year 2019–2020 (please refer to Figure 7). Most
authors have adopted NLP in social media research, either in terms of sentiment analysis
based on image evaluation, customers’ product opinions, social media users’ emotions, or
on consumers’ vocabularies. The most cited article was by Melumad (2019) on the effect of
social media content on the usage of smartphones. Based on the literature findings, there
are still limited literature studies that have utilized NLP for banking and finance.
Computers 2023,12, 50 6 of 15
Computers 2023, 12, x FOR PEER REVIEW 6 of 17
on the effect of social media content on the usage of smartphones. Based on the literature
findings, there are still limited literature studies that have utilized NLP for banking and
finance.
Figure 5. NLP literature studies in social sciences, business, management, and accounting area.
Figure 6. NLP literature studies in the business, economics, and finance areas.
Figure 5. NLP literature studies in social sciences, business, management, and accounting area.
Computers 2023, 12, x FOR PEER REVIEW 6 of 17
on the effect of social media content on the usage of smartphones. Based on the literature
findings, there are still limited literature studies that have utilized NLP for banking and
finance.
Figure 5. NLP literature studies in social sciences, business, management, and accounting area.
Figure 6. NLP literature studies in the business, economics, and finance areas.
Figure 6. NLP literature studies in the business, economics, and finance areas.
Computers 2023, 12, x FOR PEER REVIEW 7 of 17
Figure 7. Main contributors to the NLP literature.
3. Methodology
The proposed model in this research is based on Vector Space Model (VSM) for doc-
ument matching and similarity measures. Product proposals became a query, and policy
documents by the central bank would be a corpus or database for document matching.
Both the query and corpus went through a preprocessing stage prior to similarity analysis.
One set of queries with two sets of corpora is tested in this research to compare similarity
values. For document similarity checking, this research adopts the Levenshtein Distance
method in Python programming language. It is a technique of finding strings that match
with a given string partially and not exactly. When a user misspells a word or enters a
word partially, fuzzy string matching helps in finding the right word. According to [13],
among the advantages of this method are it may improve data quality and accuracy and
is used for fraud detection within an organization. The algorithm behind fuzzy string
matching does not simply look at the equivalency of two strings but rather quantifies how
close two strings are to one another. This is usually performed using a distance metric
known as ‘edit distance.’ This determines the closeness of two strings by identifying the
minimum alterations needed to convert one string into another. Figure 8 below presents
the proposed algorithm for Shariah document screening.
Figure 7. Main contributors to the NLP literature.
3. Methodology
The proposed model in this research is based on Vector Space Model (VSM) for
document matching and similarity measures. Product proposals became a query, and policy
documents by the central bank would be a corpus or database for document matching.
Both the query and corpus went through a preprocessing stage prior to similarity analysis.
One set of queries with two sets of corpora is tested in this research to compare similarity
values. For document similarity checking, this research adopts the Levenshtein Distance
Computers 2023,12, 50 7 of 15
method in Python programming language. It is a technique of finding strings that match
with a given string partially and not exactly. When a user misspells a word or enters a word
partially, fuzzy string matching helps in finding the right word. According to [
13
], among
the advantages of this method are it may improve data quality and accuracy and is used
for fraud detection within an organization. The algorithm behind fuzzy string matching
does not simply look at the equivalency of two strings but rather quantifies how close two
strings are to one another. This is usually performed using a distance metric known as
‘edit distance.’ This determines the closeness of two strings by identifying the minimum
alterations needed to convert one string into another. Figure 8below presents the proposed
algorithm for Shariah document screening.
Computers 2023, 12, x FOR PEER REVIEW 8 of 17
Figure 8. Proposed algorithm for Shariah document screening.
3.1. Data Preprocessing
Two sets of documents (product proposal and Central Bank of Malaysia (CNM)
guidelines) were selected in this research and went through a preprocessing stage to avoid
any irrelevant information or accuracy of the results later. Steps in data preprocessing
include tokenization, stop-word removal, and stemming [14]. The documents first went
through tokenization which the whole documents were transferred into words using
white spaces. It was followed by punctuation and stop word removal such as commas,
semicolons, ‘and,’ ‘is,’ ‘or,’ and others. Next, any compound words were split, and stem-
ming was accomplished using Porter stemming program. In this stage, words were con-
verted into their stems, such as ‘structuring’ or ‘structures’ into ‘structure,’ to determine
domain vocabularies and to reduce redundancy, as most of the time, the word stem and
their derived words mean the same. Once this preprocessing stage was completed, the
similarity analysis was performed.
This is an important and essential step prior to the next level of model development
in NLP. A set of text corpus (data) collected from one or many sources may have incon-
sistencies and ambiguity that requires preprocessing to clean it up. If text preprocessing
is not performed properly, it may affect the output of the NLP model later. Using the
NLTK library, text preprocessing procedures were conducted, which included lowercas-
ing, removing extra whitespaces, punctuations, stopwords, tokenization, spelling correc-
tion, stemming, and lemmatization.
3.2. Measure of Similarity
Once the queries and corpus have been screened and are ready for further analysis,
the vital process is checking for document similarities. Similarity analysis is a necessary
stage in most Information Retrieval (IR) and Natural Language Processing (NLP) tasks,
including document clustering, plagiarism detection, text categorization [15], and docu-
ment screening. The success of IR models mostly depended on their similarity measures
[16]. There were various measures of similarity, as discussed in the past literature,
whereby the differences among the measures were on their functionality; a similarity
Figure 8. Proposed algorithm for Shariah document screening.
3.1. Data Preprocessing
Two sets of documents (product proposal and Central Bank of Malaysia (CNM) guide-
lines) were selected in this research and went through a preprocessing stage to avoid any
irrelevant information or accuracy of the results later. Steps in data preprocessing include
tokenization, stop-word removal, and stemming [
14
]. The documents first went through
tokenization which the whole documents were transferred into words using white spaces.
It was followed by punctuation and stop word removal such as commas, semicolons,
‘and’, ‘is’, ‘or’, and others. Next, any compound words were split, and stemming was
accomplished using Porter stemming program. In this stage, words were converted into
their stems, such as ‘structuring’ or ‘structures’ into ‘structure’, to determine domain vo-
cabularies and to reduce redundancy, as most of the time, the word stem and their derived
words mean the same. Once this preprocessing stage was completed, the similarity analysis
was performed.
This is an important and essential step prior to the next level of model development
in NLP. A set of text corpus (data) collected from one or many sources may have incon-
sistencies and ambiguity that requires preprocessing to clean it up. If text preprocessing
is not performed properly, it may affect the output of the NLP model later. Using the
NLTK library, text preprocessing procedures were conducted, which included lowercasing,
removing extra whitespaces, punctuations, stopwords, tokenization, spelling correction,
stemming, and lemmatization.
Computers 2023,12, 50 8 of 15
3.2. Measure of Similarity
Once the queries and corpus have been screened and are ready for further analysis,
the vital process is checking for document similarities. Similarity analysis is a necessary
stage in most Information Retrieval (IR) and Natural Language Processing (NLP) tasks, in-
cluding document clustering, plagiarism detection, text categorization [
15
], and document
screening. The success of IR models mostly depended on their similarity measures [
16
].
There were various measures of similarity, as discussed in the past literature, whereby the
differences among the measures were on their functionality; a similarity measure effective
in addressing one measurement problem may not be effective in another [
17
]. This research
adopted Levenshtein Distance for similarity checking.
Levenshtein Distance
Levenshtein Distance, also known as Minimum Edit distance, is a popular method
used to measure the distance between two strings. It is computed by counting the num-
ber of edits required to transform one string into another. The edits could be either the
addition of a new letter, removal of a letter, and substitution. For example, the Leven-
shtein distance between “house” and “mouse” is 1 as only 1 edit is required to change
‘h’ into ‘m’. There could be multiple ways of transitioning from one word to another, but
Levenshtein distance chooses the smallest possible path. The more similarity between
two strings, the less distance between them, and vice versa. This method is commonly
used in autocompletion or autocorrection text applications such as Google search or online
dictionaries. Four processes are involved in generating distance values, including creating
the distances matrix, initializing the distances matrix, printing the distances matrix, and
finally calculating distances between all prefixes [18].
Accordingly, this method is introduced by Vladimir Levenshtein in the year 1965. This
is a mathematical formula created to measure the similarity of distance. The Levenshtein
distance between two strings a,b(of length |a| and |b|, respectively) is given by leva, b
(|a|,|b|) where: 1(ai
6=
bi) is the indicator function equal to 0 when ai
6=
bi and equal to 1
otherwise, and lev a,b(i,j) is the distance between the first icharacters of aand the first j
characters of b[19]. Please refer to Equation (1).
Note that the first element in the minimum corresponds to deletion (from ato b),
the second to insertion, and the third to match or mismatch, depending on whether the
respective symbols are the same.
leva,b(i,j)=
max(i,j),i f min(i,j)=0
min
leva,b(i−1, j)+1
leva,b(i,j−1)+1
leva,b(i−1, j−1)+1(ai6=bj)
,otherwise (1)
a= string #1
b= string #2
i= the terminal character position of string #1
j= the terminal character position of string #2
The conditional (ai6=bj)
airefers to the character of string aat position i
bjrefers to the character of string bat position j
Equation (1): Levenshtein Mathematical Formula.
Below is a sample of python codes conducted in this research to measure the Leven-
shtein Distance.
min_distance = 1
max_ratio = 0
max_ratio_label = 0
max_ratio_label_content = ““
for nums in StrOptions.keys():
Computers 2023,12, 50 9 of 15
Distance = lev.distance(String1.lower(),StrOptions[nums].lower())
Ratio = lev.ratio(String1.lower(),StrOptions[nums].lower())
print(“Distance:”, Distance, “Ratio:”, Ratio, ““, f’”{nums}”‘, StrOptions[nums])
if max_ratio < Ratio:
max_ratio = Ratio
max_ratio_label = nums
max_ratio_label_content = StrOptions[nums]
print(‘\n’)
print(“The least distance is:”, min_distance, “The greatest ratio is:”, max_ratio, “
\
nTawarruq
“, max_ratio_label, max_ratio_label_content)
Given the algorithm written in Python, this research tests two different sets of text
data where the first one is a sample of the Tawarruq product proposal (query) with the
Tawarruq policy document (corpus), and the second set is the same Tawarruq proposal
(query) with Shariah Governance Policy Document (SGPD) as corpus. The second text
set is intentionally conducted to test the validity of the Levenshtein Distance algorithm in
checking the similarity of two unrelated texts/documents. Figures 9and 10 below present
the results of Levenshtein Distance for two sets of text.
Computers 2023, 12, x FOR PEER REVIEW 10 of 17
max_ratio_label_content = StrOptions[nums]
print(‘\n’)
print(“The least distance is:”, min_distance, “The greatest ratio is:”, max_ratio,
“\nTawarruq “, max_ratio_label, max_ratio_label_content)
Given the algorithm written in Python, this research tests two different sets of text
data where the first one is a sample of the Tawarruq product proposal (query) with the
Tawarruq policy document (corpus), and the second set is the same Tawarruq proposal
(query) with Shariah Governance Policy Document (SGPD) as corpus. The second text set
is intentionally conducted to test the validity of the Levenshtein Distance algorithm in
checking the similarity of two unrelated texts/documents. Figures 9 and 10 below present
the results of Levenshtein Distance for two sets of text.
Figure 9. Results of Leveshtein Distance for related texts.
Figure 9. Results of Leveshtein Distance for related texts.
Computers 2023,12, 50 10 of 15
Based on the above similarity results, for the first set of related documents, the value
of similarity is 0.957, which is near 1.0 and can be interpreted as having minimum distance
or high similarity level. Meanwhile, for the second set of unrelated texts, the value of
similarity is 0.451, which is far from 1.0 and is an indicator of a low level of similarity or an
unrelated text document.
Computers 2023, 12, x FOR PEER REVIEW 11 of 17
Figure 10. Results of Leveshtein Distance for unrelated texts.
Based on the above similarity results, for the first set of related documents, the value
of similarity is 0.957, which is near 1.0 and can be interpreted as having minimum distance
or high similarity level. Meanwhile, for the second set of unrelated texts, the value of sim-
ilarity is 0.451, which is far from 1.0 and is an indicator of a low level of similarity or an
unrelated text document.
4. Serverless Architecture for Shariah Document Screening Prototype
The Shariah document screening prototype is developed using a serverless architec-
ture. A serverless architecture enables the development and operation of applications and
services without the need to manage infrastructure. The application still runs on servers,
but the cloud provider handles all server management. Developers are no longer required
to spend time provisioning, scaling, and maintaining servers. The developers can concen-
trate on the core product rather than managing and operating servers or runtimes in the
cloud or on-premises. This reduced overhead and allowed developers to reclaim time and
resources that could be spent creating top-notch products.
4.1. Serverless Design Principles
Serverless architecture applications share the following design principles:
• Simplicity and speed. Concise functions should be written that are intended to carry
out transactional operations or finish computing tasks applied to one or more
Figure 10. Results of Leveshtein Distance for unrelated texts.
4. Serverless Architecture for Shariah Document Screening Prototype
The Shariah document screening prototype is developed using a serverless architecture.
A serverless architecture enables the development and operation of applications and
services without the need to manage infrastructure. The application still runs on servers,
but the cloud provider handles all server management. Developers are no longer required to
spend time provisioning, scaling, and maintaining servers. The developers can concentrate
on the core product rather than managing and operating servers or runtimes in the cloud or
on-premises. This reduced overhead and allowed developers to reclaim time and resources
that could be spent creating top-notch products.
4.1. Serverless Design Principles
Serverless architecture applications share the following design principles:
•
Simplicity and speed. Concise functions should be written that are intended to carry
out transactional operations or finish computing tasks applied to one or more en-
Computers 2023,12, 50 11 of 15
tities. These transactions must be completed quickly because they have time and
capacity restrictions.
•
Hardware is agnostic. It is essential, when developing serverless applications, to
remove any dependencies that are hardware related. This is because the resources are
only provisioned for the duration of the function’s runtime.
•
Optimized for concurrency. Functions should be designed considering the concur-
rency requests limitation of the serverless architecture. For serverless applications,
optimizing for total requests may not be the best design strategy because the total
request count may peak less frequently than concurrent request capacity limits.
•
Temporary storage. When a function is invoked, the underlying resources are pro-
visioned or accessed for a limited period. The state of the environment, including
storage capacity, may change during the execution of a function; therefore, it may be
preferable to use persistence to satisfy durable storage requirements.
•
Redundancy. Failure must be handled properly by design. A single failure can
propagate to subsequent requests and impede the application’s operational workflow.
4.2. Advantages of Serverless Architecture
Serverless architecture provides many advantages because of the design principles.
The advantages of serverless architecture include the following:
Deploy and run. The cloud vendor is responsible for managing infrastructure re-
sources. Critical areas in software development can be focused on by the internal IT team.
This optimizes resource usage and delivery time.
Optimized usage-based billing. The pay-as-you-go model reaches out to a bigger
spectrum of industry players, including small and midsize companies.
Fault-tolerance. Hardware failures have minimal impact on the software development
lifecycle due to the serverless application being logically decoupled from the underlying in-
frastructure.
Built-in integrations. The cloud vendors offer specialized services, including in-
tegrations and configuration work allowing software companies to focus on building
high-quality applications.
Low operational overhead. Cloud vendors manage infrastructure and tasks related
to operations management. The overall process of the software development lifecycle is
shortened. The development team can release applications, analyze user feedback, and
iterate improvements faster.
4.3. Disadvantages of Serverless Architecture
Security. A significant portion of data will be given to another business, which may or
may not protect it. Security and apprehension about the unknown are the main reasons
given by the 60% of businesses that do not use serverless systems for their operations.
Privacy. The resources are shared in cloud environments where other resources may
also reside.
Complexity. When something is not working as it should, it might be difficult to
pinpoint the issue. The various components involved may require significant time to trou-
bleshoot.
Contracts. Due to the nature of the services, vendors require customers to sign lengthy
contracts. These contracts are complex and may have many loopholes if not properly vetted.
5. Prototype Components
In this research study, the prototype is separated into two main modules. The back-end
similarity analytical service is configured as a back-end service that could be scaled up
automatically depending on the requests. The other module is the user interface module
developed using ReactJs. Overall, the front-end is developed using ReactJS with mongodb,
while the back end is based on Python language. Figure 11 below presents a sample of
the Shariah document screening prototype developed in this research. Guidelines from
Computers 2023,12, 50 12 of 15
relevant authorities can be uploaded to the proposed back-end service component. The
back-end service will be initialized with the target clauses for similarity analysis.
The current research is limited to two guideline documents. This can be extended to
multiple documents from relevant authorities.
Computers 2023, 12, x FOR PEER REVIEW 13 of 17
mongodb, while the back end is based on Python language. Figure 11 below presents a
sample of the Shariah document screening prototype developed in this research. Guide-
lines from relevant authorities can be uploaded to the proposed back-end service compo-
nent. The back-end service will be initialized with the target clauses for similarity analysis.
The current research is limited to two guideline documents. This can be extended to
multiple documents from relevant authorities.
Figure 11. User Interface of Shariah Screening Prototype.
Process flow of Shariah Document Screening Prototype
The prototype can be used by uploading a single file with the text to be analyzed for
compliance against guidelines produced by the authorized agency. Once uploaded, the
file will have the status “New” by default. The analysis can be started by clicking the Pro-
cess button, in which the content will be extracted and passed to the back-end function
for processing. The processing is performed using the Levenshtein algorithm to check for
text similarity between the supplied text and the stored guidelines. Due to the nature of
serverless architecture, it is possible to provide a back-end service for similarity checking
using other algorithms in the future. The same front-end can be used to connect to the
other service using different algorithms. It is also possible to use several algorithms and
enable to assess and compare results from various algorithms used. Please refer to Figure
12.
Figure 11. User Interface of Shariah Screening Prototype.
Process Flow of Shariah Document Screening Prototype
The prototype can be used by uploading a single file with the text to be analyzed
for compliance against guidelines produced by the authorized agency. Once uploaded,
the file will have the status “New” by default. The analysis can be started by clicking the
Process button, in which the content will be extracted and passed to the back-end function
for processing. The processing is performed using the Levenshtein algorithm to check for
text similarity between the supplied text and the stored guidelines. Due to the nature of
serverless architecture, it is possible to provide a back-end service for similarity checking
using other algorithms in the future. The same front-end can be used to connect to the other
service using different algorithms. It is also possible to use several algorithms and enable
to assess and compare results from various algorithms used. Please refer to Figure 12.
Computers 2023, 12, x FOR PEER REVIEW 14 of 17
Figure 12. Shariah Document Screening Prototype Process Flow.
Once completed, the status of the file will be changed to Processed. The respective
report in PDF format can be downloaded by clicking the View Report button. Figure 13
below is excerpted from a sample report:
Figure 13. Snapshot of Generated Report.
6. Conclusions and Discussion
The Islamic banking industry, similar to other industries, is, at present, experiencing
a shift of technological advancement. On top of all-digital transactions, as financial ser-
vices providers, the banks are expected to upgrade their services to customers when
needed continuously. Hence, to grow with the technology and to remain compliant with
industry regulations, Islamic banks must have a proper IT infrastructure and processes in
Figure 12. Shariah Document Screening Prototype Process Flow.
Computers 2023,12, 50 13 of 15
Once completed, the status of the file will be changed to Processed. The respective
report in PDF format can be downloaded by clicking the View Report button. Figure 13
below is excerpted from a sample report:
Computers 2023, 12, x FOR PEER REVIEW 14 of 17
Figure 12. Shariah Document Screening Prototype Process Flow.
Once completed, the status of the file will be changed to Processed. The respective
report in PDF format can be downloaded by clicking the View Report button. Figure 13
below is excerpted from a sample report:
Figure 13. Snapshot of Generated Report.
6. Conclusions and Discussion
The Islamic banking industry, similar to other industries, is, at present, experiencing
a shift of technological advancement. On top of all-digital transactions, as financial ser-
vices providers, the banks are expected to upgrade their services to customers when
needed continuously. Hence, to grow with the technology and to remain compliant with
industry regulations, Islamic banks must have a proper IT infrastructure and processes in
Figure 13. Snapshot of Generated Report.
6. Conclusions and Discussion
The Islamic banking industry, similar to other industries, is, at present, experiencing a
shift of technological advancement. On top of all-digital transactions, as financial services
providers, the banks are expected to upgrade their services to customers when needed
continuously. Hence, to grow with the technology and to remain compliant with industry
regulations, Islamic banks must have a proper IT infrastructure and processes in place.
Accordingly, applying a serverless architecture makes sense for Islamic banks as they are
currently working on enhancing customer services as the demands keep growing. The
adoption of serverless architecture in banking and financial services would be an added
advantage to the institutions in controlling operational costs, enhancing the ecosystem, and
adhering to regulatory standards, which require automation and secure distributed systems.
As the Islamic banking industry has received a special focus from the regulator mainly
on the Shariah compliancy of the business, a smart, more secure, and distributed system
based on a Shariah ledger of transactions must be in place. A Shariah-based automated
system is needed to reduce human error and mitigate the possibility of fraud. In addition, a
transparent system supported by open sources would assist the Islamic banking ecosystem.
Hence, serverless architecture, which is built on a cloud computing mechanism in the
context of this research study, will act as a deployment platform for the document screening
process. It also mitigates the need for vast architecture knowledge and responsibilities
such as management, provisioning, and maintenance. These will lower the cost of IT
infrastructure for the banking ecosystem.
The Shariah document screening proposed in this research is tested using a Python
programming language based on Levenshtein Distance similarity analysis. According
to [
13
], among the advantages of this method are it may improve data quality and accuracy
and is used for fraud detection within an organization. Recent research studies that
have utilized this method include [
20
], where his research has estimated the consistency,
Computers 2023,12, 50 14 of 15
proficiency, and accuracy of fuzzy string matching as automated metrics of participants’
accuracy in speech intelligibility. This method is also useful for recording linkages [
21
],
spelling inspectors, spam detectors [
22
], and speech detection [
23
]. However, it has yet
to be explored in the banking industry, especially for document assessment. Levenshtein
Distance is basically quantifying the match between a given string and a target string based
on the number of shared characters. It is useful to quantify the accuracy of a particular
document given the standard document or guideline by authority.
The final step is developing an interface or prototype of this Shariah document screen-
ing platform, which is performed by adopting ReactJS with mongodb. React is undoubtedly
becoming the best tool to develop front-end applications in the financial industry nowa-
days, where skills in this area are highly demanded by the industry [
24
]. There has been
a lot of research conducted by adopting this method in various areas, including research
conducted by [
25
] for automated text translation, [
26
] for blockchain platform development,
and [
27
] to develop an electronic prescription system using NLP and blockchain technology.
Overall, the proposed prototype is the first attempt in the Islamic banking literature as well
as in the industry to focus on the Shariah document screening process. It has the potential
to be commercialized in the future.
7. Limitations and Suggestions for Future Research
A few limitations of using serverless architecture for the proposed prototype include
rising costs recently due to high demand. Further, testing and debugging might also
become a limitation in a serverless environment due to a lack of back-end process visibility.
In terms of security, because the involved data are generally from open-source such as the
central bank website and industry standards that are publicly accessible, there would be
not many issues with security, except at the user side when they enter their query. In this
context, developers need to be sure the data shared by the user/customer is kept secure.
Future research may extend the proposed prototype to include sources of the database from
various authorities (local and international levels). It is also suggested to investigate the
viability of the prototype among Islamic banking practitioners and to attain more valuable
input from them for enhancement. Future researchers may also apply the algorithms built
in this research to other areas and contexts.
Author Contributions:
Conceptualization, M.C.M.S. and R.M.N.; Methodology, M.C.M.S., F.Y., M.A.
Software, M.C.M.S., F.Y.; Validation, R.M.N., F.Y., M.A. Formal analysis, M.C.M.S. Writing—review
and editing, M.C.M.S., R.M.N., F.Y., M.A. Project administration, M.C.M.S., R.M.N. All authors have
read and agreed to the published version of the manuscript.
Funding:
The APC and prototype development were funded by research funds from Rabdan Academy.
Data Availability Statement:
Part of data has been presented in main text which is dummy product
proposal and another part of data (policy document by central bank) can can be downloaded from
https://www.bnm.gov.my/banking-islamic-banking, accessed on 13 February 2023.
Acknowledgments: This work was supported by Research Fund from Rabdan Academy.
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
El-kenawy, E.M.; Abutarboush, H.F.; Mohamed, A.W.; Ibrahim, A. Advance Artificial Intelligence Technique For Designing
Double T-Shaped Monopole Antenna. Comput. Mater. Contin. 2021,69, 2983–2995. [CrossRef]
2.
Saad, A.; Alhabshi, S.; Noor, A.; Hassan, R. Robo-Advisory for Islamic Financial Institutions: Shari’ah And Regulatory Issues.
Eur. J. Islam. Financ. 2020. [CrossRef]
3.
Ali, M.M.; Hassan, R. Survey on Shar
¯
ı
Computers 2023, 12, x FOR PEER REVIEW 16 of 17
Author Contributions: Conceptualization, M.C.M.S. and R.M.N.; Methodology, M.C.M.S.; F.Y.;
M.A. Software, M.C.M.S.; F.Y..; Validation, R.M.N.; F.Y.; M.A. Formal analysis, M.C.M.S. Writing–
review and editing, M.C.M.S.; R.M.N.; F.Y.; M.A. Project administration, M.C.M.S.; R.M.N. All au-
thors have read and agreed to the published version of the manuscript.
Funding: The APC and prototype development were funded by research funds from Rabdan Acad-
emy.
Data Availability Statement: Part of data has been presented in main text which is dummy product
proposal and another part of data (policy document by central bank) can can be downloaded from
https://www.bnm.gov.my/banking-islamic-banking accessed on 13 February 2023.
Acknowledgments: This work was supported by Research Fund from Rabdan Academy.
Conflicts of Interest: The authors declare no conflict of interest.
References
1. El-kenawy, E.M.; Abutarboush, H.F.; Mohamed, A.W.; Ibrahim, A. Advance Artificial Intelligence Technique For Designing
Double T-Shaped Monopole Antenna. Comput. Mater. Contin. 2021, 69, 2983–2995.
2. Saad, A.; Alhabshi, S.; Noor, A.; Hassan, R. Robo-Advisory for Islamic Financial Institutions: Shari’ah And Regulatory Issues.
Eur. J. Islam. Financ. 2020. https://doi.org/10.13135/2421-2172/3992.
3. Ali, M.M.; Hassan, R. Survey On Sharīʿah Non-Compliant Events in Islamic Banks in the Practice of Tawarruq Financing in
Malaysia. ISRA Int. J. Islam. Financ. 2020, 12, 151–169. https://doi.org/10.1108/IJIF-07-2018-0075.
4. Tobias, B.; Burg, V.; Gombovic, A.; Puri, M. On the Rise of FinTechs: Credit Scoring Using Digital Footprints. Rev. Financ. Stud.
2020, 33, 2845–2897.
5. Petralia, K.P.; T. Rice, T.; Veron, N. Banking Disrupted? Financial Intermediation in an Era of Transformational Technology; Centre
for Economic Policy Research: London, UK, 2019; ISBN 978-1-912179-26-8.
6. Soukal, I. Novel Interaction Cost Analysis Applied to Bank Charges Calculator. Computers 2019, 8, 64.
https://doi.org/10.3390/computers8030064.
7. Togbe, M.U.; Chabchoub, Y.; Boly, A.; Barry, M.; Chiky, R.; Bahri, M. Anomalies Detection Using Isolation in Concept-Drifting
Data Streams. Computers 2021, 10, 13. https://doi.org/10.3390/computers10010013.
8. Titov, V.; Shust, P.; Dostov, V.; Leonova, A.; Krivoruchko, S.; Lvova, N.; Guzov, I.; Vashchuk, A.; Pokrovskaia, N.; Braginets,
A.; et al. Digital Transformation of Signatures: Suggesting Functional Symmetry Approach for Loan Agreements. Computation
2022, 10, 106. https://doi.org/10.3390/computation10070106.
9. Haider Syed, M.; Khan, S.; Rabbani, M.R.; Thalassinos, Y.E. An Artificial Intelligence and NLP based Islamic FinTech Model
Combining Zakat and Qardh-Al-Hasan for Countering the Adverse Impact of COVID 19 on SMEs and Individuals. Int. J. Econ.
Bus. Adm. 2020, VIII, 351–364. https://doi.org/10.35808/ijeba/466.
10. Ali, H.; Abdullah, R.; Zaini, M.Z. Fintech and Its Potential Impact on Islamic Banking and Finance Industry: A Case Study of
Brunei Darussalam and Malaysia. Int. J. Islam. Econ. Financ. 2019, 2, 73–108.
11. Laldin, M.A.; Furqani, H. FinTech and Islamic Finance. In Fintech in Islamic Finance: Theory and Practice; Routledge: Oxfordshire,
UK, 2019.
12. Zubaidi, I.B.; Abdullah, A. Developing a Digital Currency from an Islamic Perspective: Case of Blockchain Technology. Int. Bus.
Res. 2017, 10, 79.
13. Kuruvilla, V.P. A Comprehensive Guide to Fuzzy Matching/Fuzzy Magic. 2021. Available online: https://nanon-
ets.com/blog/fuzzy-matching-fuzzy-logic/ (accessed on 13 February 2023).
14. Manning, C.D.; Raghavan, P.; Schütze, H. An Introduction to Information Retrieval; Cambridge University Press: Cambridge, UK,
2009.
15. Bigi, B. Using Kullback-Leibler Distance for Text Categorization; Springer: Berlin/Heidelberg, Germany, 2003; pp. 305–319.
16. Polettini, N. The Vector Space Model in Information Retrieval-Term Weighting Problem. Entropy 2004, 34, 1–9.
17. Thompson, V.; Panchev, C.; Oakes, M. Performance Evaluation of Similarity Measures on Similar and Dissimilar Text Retrieval.
In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge
Management (IC3K 2015), Lisbon, Portugal, 12–14 November 2015; Volume 1, pp. 577–584, ISBN 978-989-758-158-8.
18. Gad, A.F. Levenshtein Distance for Word Autocompletion and Autocorrection. 2020.Available online: https://blog.paper-
space.com/implementing-levenshtein-distance-word-autocomplete-autocorrect/ (accessed on 13 February 2023).
19. Binami. The Levenshtein Algorithm. 2019. Available online: https://blog.paperspace.com/implementing-levenshtein-distance-
word-autocomplete-autocorrect/ (accessed on 13 February 2023).
20. Bosker, H.R. Using Fuzzy String Matching for Automated Assessment of Listener Transcripts in Speech Intelligibility Studies.
Behav. Res. Methods 2021, 53, 1945–1953. https://doi.org/10.3758/s13428-021-01542-4.
21. Wang, J.; Li, G.; Fe, J. Fast-Join: An Efficient Method for Fuzzytoken Matching Based String Similarity Join. In Proceedings of
the 27th International Conference on Data Engineering, Hannover, Germany, 11–16 April 2011; pp. 458–469.
ah Non-Compliant Events in Islamic Banks in the Practice of Tawarruq Financing in
Malaysia. ISRA Int. J. Islam. Financ. 2020,12, 151–169. [CrossRef]
4.
Tobias, B.; Burg, V.; Gombovic, A.; Puri, M. On the Rise of FinTechs: Credit Scoring Using Digital Footprints. Rev. Financ. Stud.
2020,33, 2845–2897.
5.
Petralia, K.P.; Rice, T.T.; Veron, N. Banking Disrupted? Financial Intermediation in an Era of Transformational Technology; Centre for
Economic Policy Research: London, UK, 2019; ISBN 978-1-912179-26-8.
Computers 2023,12, 50 15 of 15
6. Soukal, I. Novel Interaction Cost Analysis Applied to Bank Charges Calculator. Computers 2019,8, 64. [CrossRef]
7.
Togbe, M.U.; Chabchoub, Y.; Boly, A.; Barry, M.; Chiky, R.; Bahri, M. Anomalies Detection Using Isolation in Concept-Drifting
Data Streams. Computers 2021,10, 13. [CrossRef]
8.
Titov, V.; Shust, P.; Dostov, V.; Leonova, A.; Krivoruchko, S.; Lvova, N.; Guzov, I.; Vashchuk, A.; Pokrovskaia, N.; Braginets, A.;
et al. Digital Transformation of Signatures: Suggesting Functional Symmetry Approach for Loan Agreements. Computation
2022
,
10, 106. [CrossRef]
9.
Haider Syed, M.; Khan, S.; Rabbani, M.R.; Thalassinos, Y.E. An Artificial Intelligence and NLP based Islamic FinTech Model
Combining Zakat and Qardh-Al-Hasan for Countering the Adverse Impact of COVID 19 on SMEs and Individuals. Int. J. Econ.
Bus. Adm. 2020,VIII, 351–364. [CrossRef]
10.
Ali, H.; Abdullah, R.; Zaini, M.Z. Fintech and Its Potential Impact on Islamic Banking and Finance Industry: A Case Study of
Brunei Darussalam and Malaysia. Int. J. Islam. Econ. Financ. 2019,2, 73–108. [CrossRef]
11.
Laldin, M.A.; Furqani, H. FinTech and Islamic Finance. In Fintech in Islamic Finance: Theory and Practice; Routledge: Oxfordshire,
UK, 2019.
12.
Zubaidi, I.B.; Abdullah, A. Developing a Digital Currency from an Islamic Perspective: Case of Blockchain Technology. Int. Bus.
Res. 2017,10, 79. [CrossRef]
13.
Kuruvilla, V.P. A Comprehensive Guide to Fuzzy Matching/Fuzzy Magic. 2021. Available online: https://nanonets.com/blog/
fuzzy-matching-fuzzy-logic/ (accessed on 13 February 2023).
14.
Manning, C.D.; Raghavan, P.; Schütze, H. An Introduction to Information Retrieval; Cambridge University Press: Cambridge,
UK, 2009.
15. Bigi, B. Using Kullback-Leibler Distance for Text Categorization; Springer: Berlin/Heidelberg, Germany, 2003; pp. 305–319.
16. Polettini, N. The Vector Space Model in Information Retrieval-Term Weighting Problem. Entropy 2004,34, 1–9.
17.
Thompson, V.; Panchev, C.; Oakes, M. Performance Evaluation of Similarity Measures on Similar and Dissimilar Text Retrieval.
In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge
Management (IC3K 2015), Lisbon, Portugal, 12–14 November 2015; Volume 1, pp. 577–584, ISBN 978-989-758-158-8.
18.
Gad, A.F. Levenshtein Distance for Word Autocompletion and Autocorrection. 2020. Available online: https://blog.paperspace.
com/implementing-levenshtein-distance-word-autocomplete-autocorrect/ (accessed on 13 February 2023).
19.
Binami. The Levenshtein Algorithm. 2019. Available online: https://medium.com/@binaniharsj/the-levenshtein-algorithm-21
5567a3ab1f (accessed on 13 February 2023).
20.
Bosker, H.R. Using Fuzzy String Matching for Automated Assessment of Listener Transcripts in Speech Intelligibility Studies.
Behav. Res. Methods 2021,53, 1945–1953. [CrossRef] [PubMed]
21.
Wang, J.; Li, G.; Fe, J. Fast-Join: An Efficient Method for Fuzzytoken Matching Based String Similarity Join. In Proceedings of the
27th International Conference on Data Engineering, Hannover, Germany, 11–16 April 2011; pp. 458–469.
22.
Wei, C.; Sprague, A.; Warner, G. Clustering Malware-Generated Spam Emails With a Novel Fuzzy String Matching Algorithm. In
Proceedings of the 2009 ACM Symposium on Applied Computing, Honolulu, HI, USA, 9–12 March 2009; pp. 889–890.
23.
Schalk, T.B.; Zimmerman, R.S. Knowledge-Based Strategies Applied to N-Best Lists in Automatic Speech Recognition Systems.
U.S. Patent No. US6922669B2, 26 July 2005.
24.
Sharma, G. Best React JS Courses. 2023. Available online: https://www.bankersbyday.com/react-js-courses-certifications/
(accessed on 13 February 2023).
25. Khuzyatov, S.S.; Galiullin, L.A. Automated Text Translations. Int. J. Criminol. Sociol. 2020,9, 2320–2324.
26.
Liu, X.; Jiang, Y.; Wang, Z.; Cheung, H.H.; Huang, G.Q. LimseStudio: Blockchain-Enabled Secure Digital Twin Platform for
Service Manufacturing. Int. J. Prod. Res. 2021. [CrossRef]
27.
Mahatpure, J.; Motwani, M.; Shukla, P.K. An Electronic Prescription System Powered by Speech Recognition, Natural Language
Processing and Blockchain Technology. Int. J. Sci. Technol. Res. 2019,8, 1454–1462.
Disclaimer/Publisher’s Note:
The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.
