Typically, transfer learning is not suitable for machine translation even though the languages have similar grammar. In this experiment, we analyzed the transferability of LAC inter similar languages. First, we used both pretrained discriminator and generator from a relative and higher resource language. Then we used a separate pretrained discriminator and generator. We found that using a separate pretrained discriminator shows better performance. Similarly, in case studies, a separate pretrained discriminator produced more fluent and correct sentences. It manifested that the LAC model has the potential in cross-lingual transfer learning compared with traditional models.

We analyzed the impact of different components in the LAC network by ablation experiments. In conclusion, no matter the ability of discriminator or generator is reduced, the translation results eventually became worse. Furthermore, from the Wasserstein distance curves (i.e., convergence curves) of ablation experiments, we found that reducing the capabilities of the discriminator or generator will eventually make the LAC model non-convergent. It showed that the original LAC model incorporates the useful adversarial features from discriminator and generator. The performance of the LAC model is the result of the interaction of discriminator and generator.

Finally, we tested the translation performance of the model in different iteration steps, and we found that the LAC model was better than the translation system without an adversarial mechanism during iteration. It shows that the adversarial mechanism can improve the model's ability in any step, and the improvement is stable.

In summary, experimental results showed that LAC has good potential in LRL translations. For future works, we will explore how to improve and leverage the discriminator and generator so that the translation performance can be further improved. In addition, we will work on how to reduce the computational costs of adversarial training.

**Author Contributions:** Methodology, M.S.; software, M.S.; validation, M.S.; formal analysis, M.S.; investigation, M.S.; data curation, M.S.; writing—original draft preparation, M.S.; writing—review and editing, H.W., M.P. and I.A.H.; visualization, M.S.; supervision, H.W., M.P. and I.A.H.; project administration, H.W., M.P. and I.A.H.; funding acquisition, H.W., M.P. and I.A.H. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Norwegian University of Science and Technology.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data available in a publicly accessible repository that does not issue DOIs. Publicly available datasets were analyzed in this study.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

## *Article* **Selection of the Right Undergraduate Major by Students Using Supervised Learning Techniques**

**Alhuseen Omar Alsayed 1,2,\* , Mohd Shafry Mohd Rahim 1 , Ibrahim AlBidewi 3 , Mushtaq Hussain 4 , Syeda Huma Jabeen 5 , Nashwan Alromema 6 , Sadiq Hussain 7 and Muhammad Lawan Jibril 8**

**Abstract:** University education has become an integral and basic part of most people preparing for working life. However, placement of students into the appropriate university, college, or discipline is of paramount importance for university education to perform its role. In this study, various explainable machine learning approaches (Decision Tree [DT], Extra tree classifiers [ETC], Random forest [RF] classifiers, Gradient boosting classifiers [GBC], and Support Vector Machine [SVM]) were tested to predict students' right undergraduate major (field of specialization) before admission at the undergraduate level based on the current job markets and experience. The DT classifier predicts the target class based on simple decision rules. ETC is an ensemble learning technique that builds prediction models by using unpruned decision trees. RF is also an ensemble technique that uses many individual DTs to solve complex problems. GBC classifiers and produce strong prediction models. SVM predicts the target class with a high margin, as compared to other classifiers. The imbalanced dataset includes secondary school marks, higher secondary school marks, experience, and salary to select specialization for students in undergraduate programs. The results showed that the performances of RF and GBC predict the student field of specialization (undergraduate major) before admission, as well as the fact that these measures are as good as DT and ETC. Statistical analysis (Spearman correlation) is also applied to evaluate the relationship between a student's major and other input variables. The statistical results show that higher student marks in higher secondary (hsc\_p), university degree (Degree\_p), and entry test (etest\_p) play an important role in the student's area of specialization, and we can recommend study fields according to these features. Based on these results, RF and GBC can easily be integrated into intelligent recommender systems to suggest a good field of specialization to university students, according to the current job market. This study also demonstrates that marks in higher secondary and university and entry tests are useful criteria to suggest the right undergraduate major because these input features most accurately predict the student field of specialization.

**Keywords:** machine learning; learning analytics; student field forecasting; imbalanced datasets; explainable machine learning; intelligent tutoring system

**Citation:** Alsayed, A.O.; Rahim, M.S.M.; AlBidewi, I.; Hussain, M.; Jabeen, S.H.; Alromema, N.; Hussain, S.; Jibril, M.L. Selection of the Right Undergraduate Major by Students Using Supervised Learning Techniques. *Appl. Sci.* **2021**, *11*, 10639. https://doi.org/10.3390/ app112210639

Academic Editors: Suhuai Luo and Kamran Shaukat

Received: 25 September 2021 Accepted: 8 November 2021 Published: 11 November 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 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/).

#### **1. Introduction**

Today, higher education institutions face considerable difficulties, such as the absence of government funding, competitive job markets, admission processes, student strength, and selections of student specializations [1,2]. Student specialization selection is an area of educational research that has received little attention, although it is critical in recognizing students' interests and preparing them for a future career [3]. Student specialization is a worldwide educational problem that needs to be investigated. For example, in the USA, approximately 30% of year-one students do not return for their second year, and more than \$9 billion is spent on these students [4]. Furthermore, the completion rates of 4-year degrees in the US are approximately 50% [5]. These alarming figures require every possible effort to support students and higher education institutions in this critical issue. According to a study conducted by the United States Departments of Education (NCES), of the 98% of students that declared a bachelor's degree major in 2011–2012, 33% changed their major by 2014 during their third year of study [6]. Moreover, approximately 35% of college students who declared their majors to be STEM programs and 29% of students who declared their majors to be STEM-related programs eventually changed their majors after 2 years of study [7].

Student specialization selection can indicate the choice of an appropriate specialization/major that leads to a high level of satisfaction, success in allotment, graduation within a time frame, or other more specific milestones [8]. In an educational institution, the selection of the right undergraduate major by students is a major challenge when progressing to an academic level because students do not know about the job market and the demand for the required skills.

#### *1.1. Student Field Specialization (Undergraduate Major Course)*

Field specialization selection means selecting the right undergraduate major for students, for example, engineering, computer science, and management [9,10]. Universities are required to fulfill students' academic disciplines. One essential goal of universities is to aid student admission into their desired college specialization. What student admission means varies depending on the context of the university requirements, students' academic results, and other related factors [11,12]. Universities provide student admission centers and student counselors or advisors to help students meet their educational needs. Recommending suitable colleges and fields (suitable undergraduate majors) based on students' attributes and preferences is one service that could be provided by admission departments. However, due to the growing numbers of fields, students, and available skills, these advisors sometimes fail to help students with their selections [3,13,14]. Due to the substantial amount of work required by these advisors, who are not able to handle this situation, students have insufficient knowledge about how to select an appropriate field (major) in their undergraduate program that fits their preferences, personality, subjects of interest, and career type that he or she likes [3,13,14].

### *1.2. Significance of Predicting Student Field Specialization*

The undergraduate major (field specialization) is an important research topic because an incorrect undergraduate major selection affects students' academic lives, learning, and careers [15]. Students in every country face challenges in selecting the right undergraduate major. From the time students decide to continue their higher education, they are confronted with decisions concerning their education, many of which can be challenging. When students attend college, they choose a major based on several factors, such as their friends, parents, future opportunities, and, most importantly, the student advisory center. Some students are not fully aware of the importance of their academic abilities and job market demands [16]. They may depend on others' opinions, which may lead to the incorrect and unsuitable major selection. Incorrect academic decisions have a considerable and direct impact on students' success and future lives [17]. If a student chooses an unsuitable

academic major and continues to have low grades and fails to raise his or her CGPA within a year, the student will be dismissed from college.

Thus, the choice of a field (student major) for a new student can be a difficult decision; therefore, universities need to use a student intelligent counseling system because the correct student feedback has been shown to decrease the course dropout rate and increase graduation rate [18]. Long et al. [19] indicated that improving and enhancing the matching of students with their university specialization could substantially assist in decreasing the level of study discontinuation among younger students, which would also contribute to opening up spots for other potential students and in decreasing the inefficient utilization of public resources and funds for higher education. Therefore, this paper addressed the field of specialization suggestion problem by suggesting appropriate study fields for students at early stages, according to the current job market, education history, and career goals for the students. Hence, it is desirable to develop sophisticated forms of intelligent recommendation tools to help students in selecting an appropriate field of specialization

#### *1.3. Machine LearningTechniques Used in an Education Predictive Model*

Artificial intelligence (AI) is an important concept in the field of science and is currently a promising technological revolution. It uses machines to develop a concept of intelligence that is more like the human brain. There have been various fields that have taken advantage of implementing AI in their day-to-day business processes. In the area of computer sciences, the concept of artificial intelligence is widely utilized, and it is considerably related to the concept of machine learning (ML). ML is a sub-field of AI that identifies complex and hidden patterns or knowledge from large amounts of data and then makes smart decisions on unseen data [20]. One of the key features of ML is a training model utilizing different dependent and independent variables, which further depends on different utilized learning algorithms types (supervised, semisupervised, or unsupervised). ML is mostly used for predictions in many field to provide solutions to questions such as global solar radiation [21], weather predictions [22], flight time deviation [23], mortality rates in COVID-19 patients [24], predict bank failures [25], credit default prediction in bank [26], cyber security [27], bankruptcy prediction [28], filter e-learning contents [29] and efficient processes for manufacturing industries [8]. It can also be used for predicting rates in student dropouts in any course [17] and for understanding unique student learning styles [30]. Moreover, ML algorithms can assist the educational sector by constantly evaluating student academic performance. Due to the vast and dynamic implementation of ML, as well as its capability to learn from any dataset and to predict and classify future transactions, we have selected multiple ML algorithms for use in this study.

In recent years, research interest in the application of ML in education has increased, particularly among higher-education institutions. A recent study discovered that educationalrelated decisions are frequently made based on educational management stakeholders'/ students' impressions and experience, rather than based on knowledge-rich data. Unfortunately, it is a challenging task to make a suitable choice of the subject matter at an early stage due to the convoluted interaction of a variety of factors [31–33]. ML approaches are designed to make necessary educational information readily available to knowledge consumers. ML techniques have also been shown to be beneficial in improving outcomes at several educational institutions and student management centers by making necessary educational information readily available to students and other individuals [34,35]. In the past 10 years, investigations on ML and education data mining [EDM] have played a significant role in exploring educational problems [31,36], such as understanding student performance [37,38] and educational institution performance [39]. These techniques have also been used to predict student engagement and difficulty in online education [40,41] and in recommending suitable colleges and courses [9,42–45]. ML is increasingly prevalent and vital in educational contexts, in terms of predicting and identifying quality educationalrelated problems for students and decision-makers, as well as in enhancing other managerial services pertaining to streamlining students' needs. Furthermore, numerous research

studies on education have predicted admission to universities, student allotment, and admission into their desired colleges/majors by using ML techniques [33,46,47].

The current study investigated the best ML classifier that is suitable for building student field specialization intelligent systems, which can predict student study fields based on student academic history and the job market. Predicting student study fields is a classification problem; therefore, we verified the performance of common ML classification algorithms, such as Decision Tree (DT), extra tree classifiers [ETC], random forest (RF) classifiers, and gradient boosting classifiers [GBC], and Support Vector Machine [SVM], on the current study dataset. The performances of these algorithms are good, based on categorical data [48]. Additionally, these algorithms are easily described, understandable, and implemented [48]. The current study also verified the performance of the SVM on the current study dataset because it can examine both linear and nonlinear data [49].

#### *1.4. Innovation of the Current Study*

The current study investigated the student field of specialization by using students' previous histories and job market information utilizing different ML techniques. ML approaches have been employed to accurately forecast college selection and select the best fit student major by means of common classification algorithms with diverse feature sets [1]. To achieve our current study goal, we trained different common ML models (extra tree classifiers [ETC], random forest [RF] classifiers, decision tree [DT] classifiers, gradient boosting classifiers [GBC], and support vector machine [SVM]) based on the current job market and students' previous histories. The results showed that RF and GBC predicted student majors with higher accuracy, as compared to DT, SVM, and ETC. Based on the Spearman correlation method, the study concluded that higher marks in higher secondary levels, entry tests, and universities, are good criteria for suggesting student field specialization. Furthermore, student work experiences and job placements are additional factors that are strongly related to student field specializations. In addition, these ML models and features could be of high value in developing an intelligent system to easily recommend a specialization to potential applicants who are often unsure of their desired fields of specialization. Finally, this paper differs from other research in this area of predicting student field specializations because it is based on the job market and student histories and experiences.

The current study investigated the following research questions.

**Question 1:** Can we model the student undergraduate major path choice according to the job market and student academic history by applying different ML algorithms, and if so, which ML classifier offers optimal performance in predicting student undergraduate major selection?

**Question 2:** How is a student's undergraduate major path choice associated with that student's previous academic performance and the job market?

**Contributions:** This study possesses contributions as enlisted below in the domain of selection of majors by students.

The rest of this paper is organized as follows. The literature review of the student study field is discussed in Section 2. The research materials and methods are discussed in Section 3, which contains all of the details of our proposed framework of the student study field selection system. Section 4 describes the experiment and discusses the results of the current study, where the performances of different ML algorithms are tested on the current study dataset. Finally, Section 5 presents the conclusions and future work of the current study.

#### **2. Literature Review**

Several studies have been conducted to investigate student field specialization (student major) using ML. Past research has used different ML techniques and input features to study the relationship between student data and student majors. Alshaikh et al. [3] built a recommendation system to suggest suitable colleges for KAU students based on the students' grades, college specializations, and enrollment requirements. They applied this system to a dataset of 960 KAU preparatory students in 2017. Two methods were used to evaluate the accuracy of the k-nearest-neighbor algorithm. In the first method, the dataset was split into two datasets, 20% of the dataset for testing and the remaining 80% for training, which generated 70.83% accuracy. The second approach applied k-fold crossvalidation, where the dataset was split into K smaller sets and the test was applied K times. Pupara et al. [50] have proposed an accurate institutional recommender system (RS) that was developed by combining decision tree and association rule methodologies. The RS is intended to assist students in selecting acceptable colleges based on their context and educational institution information using a mobile device. Ezz and Elshenawy [9] presented an adaptive recommendation system for predicting a suitable engineering department for students enrolled in an engineering preparatory year college using classification methods such as SVM, k-nearest neighbor (KNN), linear regression (LR), quadratic discriminant analysis (QDA), and RF. The system recommends a suitable engineering department among seven engineering departments for each student based on his academic performance and the proposed system has an average accuracy of 82.57%. In the study of Salaki et al. [51], 3 ML algorithms, namely, naïve Bayes (NB), RF, and sequential minimal optimization (SMO), were trained on a dataset collected from three different educational colleges in Bangladesh to identify and select the best groups of educational majors to streamline the selection of a suitable direction for new students. The results showed that RF had the best performance, with 84.9% accuracy, 84.9% precision, 84.6% sensitivity, and an F-measure of 84.3%. In a study conducted by Fiarni et al. [52], an academic decision support system was built in the IS department to classify and recommend IS sub-majors for students using a C4.5 decision tree classifier and a rule-based approach. Bautista et al. [10] adopted 8 methods (namely, the J48 tree classifier, logistic function classifier, naïve Bayes (NB), nominal regression, decision tree CHAID, neural network multilayer perceptron, neural network radial basis function, and nearest neighbor) to recommend a suitable specialization for engineering students. The first three methods were tested in Weka and achieved accuracies of 80.5%, 64.30%, and 60.11%, respectively. The last 5 experiments conducted in SPSS, yielded accuracies of 64.00%, 68.20%, 71.30%, 61.00%, and 71.20%. Moreover, the J48 tree classifier performed the best, with the highest accuracy of 80.5%. A study conducted by Kularbphettong and Tongsiri [53] aimed to develop a decision support system for student major selection using two ML methods, J48 and Bayesian network algorithms (BNs). Their results showed that BNs performed the best, with 92.13%, 0.93, and 0.91 accuracy, precision, and F-measure, respectively. Meng and Fu [35] applied 8 classification methods, namely, SVM, decision tree (DT), naïve Bayes (GNB), RF, gradient boosting decision tree (GBDT), convolutional neural network (CNN), collaborative filtering (CF), and recurrent neural network (RNN), to recommend appropriate college majors. RF performed best, with an accuracy of 97.87% and an f-score of 96.60%. Wei et al. [54] proposed an improved SVM-based prediction system model for predicting second major selection. Their experimental results indicated that the proposed method performed best, with an accuracy of 87.36%, AUC of 0.8735, the sensitivity of 85.37%, and specificity of 89.33%, Sethi et al. [55] conducted a study to predict the appropriate study stream for students in higher secondary education. They found that the neural network (NN)

outperformed the other approaches with a classification accuracy of 86.72%, the sensitivity of 0.92, specificity of 0.82, and MCC of 0.72. Abosamra et al. [56] examined various types of ML predictions models on a dataset, which gave the best choice as a (NN) architecture that provides 6.26 an average root mean squared error, and a mean absolute error of 5.74 based on a scale of 0 to 100.

The artificial neural network (ANN) method was adopted by Latifah et al. [57] to predict suitable student specialization in a dataset of 314 students based on student records from the iGracias Integrated Academic Information System at Telkom University in 2016, and they achieved an accuracy of 94.81%. The NB method and analytic hierarchy process (AHP) techniques were adopted by Zubaedah et al. [58] to build a decision support system to predict suitable specialization in technical faculty in Indonesia. A rule-based classification algorithm (PART) was adopted by Tamiza et al. [59] to propose an intelligent model for selecting and predicting suitable university specialization. The model achieved an accuracy of 73.7%. Iyer and Variawa [60] built a system model to guide firstyear undeclared/undecided engineering students to predict suitable engineering majors. They found that the RF approach outperformed the other classification algorithms, with the highest accuracy of 57%. AlAhmar [61] developed a rule-based expert system that suggested majors for students at the undergraduate level. Kamal et al. [62] has used RF classifiers to analyze students' personality and intelligence across various majors and academic programs and predict suitable college majors for students based on academic results, personality, and level of intelligence with an accuracy of level one at 96.1% and 94.72% at second level respectively, moreover, they have investigated that their framework has potential to recommend a student towards future higher degree options.

Although the attractiveness of higher education institutions in many areas of student field of study selection has been extensively researched, there is a paucity of evidence available for modeling the relationship between these factors and intelligent recommendation of student fields based on the job market and student history and experience. To our knowledge, no studies have been conducted on the use of any ML algorithm specifically designed for the purpose of predicting student specialization and identifying the extent to which various parameters contribute to the determination of the specialization of students. As a result of this discovery, we were inspired to conduct our current study. As a result, the current research has implications for higher education, students, and the labor market.

#### **3. Materials and Methods**

In the current study, we developed an intelligent system for the field of specialization selection. We trained and tested various ML models on a student dataset because such techniques are suitable for categorical data. The proposed framework has the following stages: data preprocessing, visualization model selection modules, and model deployment. Figure 1 shows all the steps of the proposed framework. Overall, this section provides all the implementations of the student study field intelligent system in the form of the below sub-section.

#### *3.1. Data Description*

The data collection consists of several steps. Before the implementation process, consistent and appropriate educational data are required to achieve acceptable results. In this experiment, the dataset was published in Kaggle [63]. The details are shown in Table 1. This dataset was collected from MBA students of CMS Business School in January 2020 and was published on the Kaggle website [63]. This dataset contains placement data of students, including secondary school, higher secondary school, and entry test scores. The dataset also includes work experience, degree percentage, and salary offered by the organization. The salary information represents the importance of the field in the job market. The degree percentage shows the student's interest in the field. The current database contains 216 student records and 19 input features. The first experimental dataset is shown in Table 1, and the target variable is specialization.

**Figure 1.** Intelligent recommender system for student field recommendation.

#### *3.2. Proposed Framework*

Undergraduate major selection is a crucial and challenging decision for the university and the student during the process of admission to fulfill their future success. Due to bad counseling by the admission office center in the university, students go into the wrong study field; as a result, student learning will be affected [15]. Every country faces problems with students selecting the right undergraduate major course, and past studies have indicated that institutions have seen a significant increase in the number of students enrolling as undeclared. In reality, it is estimated that over 50% of students enter college undecided and that approximately 75% of students change their majors at least once before they complete their degree [64,65].

This study builds a student major intelligent system that provides feedback to students and universities about study field selection based on the data extracted from the university database. Finding and building an intelligent system model of students' field of specialization could be of high value in developing an intelligent system to easily recommend a specialization to potential applicants who are often unsure of their desired field of specialization. In the current study, we verified the performance of different supervised ML techniques to predict students' fields of specialization based on student history and the job market. High-performance ML algorithms can provide a high degree of support to student major intelligent systems. Based on performance, the student major intelligent system can provide various support to educational institutions on many issues, such as (1) helping the university administration staff in making quick decisions about students' fields of study; (2) recommending personalized study fields according to students preferences and the job market; (3) decreasing the workload for the admission office; (4) enrolling students according to their preferences and job markets; (5) identifying at-risk students and chances of success of students at early stages; and (6) intervening with the student at early stages so that course dropout rates will decrease, as well as to ensure that students go into the right study fields. Figure 1 shows the basic architecture of the intelligent system for student field of specialization recommendation. The proposed student field intelligent system framework has four major phases, which are shown below.

**Step 1 Pre-processing**: In the first phase known as Phase-1 (preprocessing), raw information (216 student records) was collected from a university database, as shown in Table 1. Subsequently, we applied different preprocessing techniques by using the Python module, such as removing missing records, deleting irrelevant student records, normalization, outlier detection, and hot encoding. To increase the proposed system performance, we also created new features by creating different categories at different education levels (ssc\_p\_catg, hsc\_p\_catg, mba\_p\_catg, degree\_p\_catg and etest\_p\_catg). To remove the missing records, we used different missing record techniques. Sometimes, ML techniques do not process the categorical technique; therefore, we applied the hot encoding technique. Additionally, the cleaned data were normalized because the ML model does not work correctly on non-normalized data. Finally, the preprocessing module converted the data into an acceptable form for the ML models, and the data were ready for the next phase. The current study dataset contained 19 input features (previous exam history, salary, and experiences of students), and the explanation of our current study dataset is shown in Table 1. Specialization is the target variable (Y) that finds the class of independent variables. When students belong to Management and Human resource management (HR) study fields (Mk and HR) then target variable (Y) is set "1" and if students belong to Management and Finance study field then target variable (Y) is set to "0".

**Step 2 Data Visualization**: In the second phase (data visualization), the clean data were visualized by using a different Python library, which shows how important the input feature is in predicting student study fields. This visualization is used to better understand the current study data.

**Steps 3 Model Selection**: In the third Phase (model selection modules), different supervised ML techniques were trained and tested using a 10-fold cross-validation method on the clean data by using the Scikit-learn Python library. It is a free ML library of python. In this library, we can easily implement ML algorithms. The current study ML algorithms were trained and tuned on training data and tested on test data using 10-fold cross-validation. After training, the model will find a pattern between input features and out variables or find the best model on the current study dataset. In addition, to select high-performance models, different performance metrics, such as accuracy and a true positive rate, were used. Finally, ML models with high accuracy are selected for intelligent field systems:

**Step 4 Model Deployment**: In Phase four, an intelligent field of specialization system was developed with the help of a high-performance ML model (RF, GBC, and SVM) because RF, GBC, and SVM predict field specialization with high accuracy. Consequently, intelligent detection of the student field of specialization provides decision-makers, academic advisors, students, and other individuals with knowledge and person-specific information, which is intelligently filtered or presented at the appropriate time, to improve education and the student's best-fit specialization field [3,14,35,66,67]. Additionally, the proposed intelligent field of specialization system will help university admission offices in daily activities.

#### **4. Results and Discussion**

Consequently, the selection of an appropriate and suitable field of study is a paramount issue for both students and educational institutes. Therefore, in this section, we investigated the student field (student major) of specialization selection using different ML techniques based on student academic history and the current job market. We also visualized the current dataset to further understand the input variables and performed several experiments using Python to answer the research questions.

We performed data visualization using Python to further understand the experimental dataset. These visualization results show the importance of input attributes in predicting field specialization. Figure 2 shows that higher secondary students who obtained jobs mostly majored in commerce and science. This result further indicates that commerce and science fields are currently in the greatest demand.

**Figure 2.** The proportion of different student fields of specialization in higher secondary school.

Figure 3 shows that commerce and management students in higher secondary schools mostly take science and technology fields as postgraduates.

In Figure 4, the blue portion (1) represents Mkt and finance, and the yellow portion (0) represents the Mkt and HR. Figure 4 indicates that 55.81% of students take the MKT& Finance program in postgraduate education, and others take the MKT & HR field.

**Reference Year of**

**Publication Country No of**

[69] 2006 USA 89

[70] 2017 Korea 816

[71] 2016 Korea 195

**Figure 3.** The proportion of different student fields of specialization in degree.

**Figure 4.** The proportion of different student fields of specialization in postgraduate education. **Figure 4.** The proportion of different student fields of specialization in postgraduate education.

At this step, we have obtained considerable knowledge about the data and can easily build models. Although there are lots of studies related to the selection of majors by students and its influential factors [1,2,3,4,5,6,7,68], studies with a machine learning approach in this domain are limited [7]. In this study, we investigate the student study field based on student job markets, student academic history, and job experience. As the number of factors, size of the datasets, methods applied varied in different studies, it is impractical to compare these studies. Some of these studies were listed in Table 2. To find an appropriate ML model and student factors for our proposed recommendation system, this study conducts two experiments on datasets using the Python Sklearn library. Sklearn is a Python library that is used to train and deploy ML models. In this section, we investigate the following research question: At this step, we have obtained considerable knowledge about the data and can easily build models. Although there are lots of studies related to the selection of majors by students and its influential factors [1–7,68], studies with a machine learning approach in this domain are limited [7]. In this study, we investigate the student study field based on student job markets, student academic history, and job experience. As the number of factors, size of the datasets, methods applied varied in different studies, it is impractical to compare these studies. Some of these studies were listed in Table 2. To find an appropriate ML model and student factors for our proposed recommendation system, this study conducts two experiments on datasets using the Python Sklearn library. Sklearn is a Python library that is used to train and deploy ML models. In this section, we investigate the following research question:

**Some of the Most Influential Factors in**

long-term salary prospects, probability of working in the field after graduation, starting salary, and prestige of the profession

collaborative learning; technology-based learning; self-regulated learning; handson activities; belief in major benefits

Gender, Grade, Acquaintance's recommendation, Daily hours of study, Place of residence

**Selecting Major Methods Used**

Average Importance

Cronbach's Alpha, exploratory factor analysis (EFA), and confirmatory factor analysis (CFA)

Fisher's exact test, *t*-test, one way ANOVA, Mann Whitney test, and ANCOVA

**Table 2.** Related studies are based on major influence factors for the recommendation of the future domain.

**Participants**

**Table 2.** Related studies are based on major influence factors for the recommendation of the future domain.

**Question 1:** Can we model the student undergraduate major path choice according to the job market and student academic history by applying different ML algorithms, and if so, which ML classifier offers optimal performance in predicting student undergraduate major selection?

The first experiment was conducted to explore this question. In the first experiment, we applied several Tree based ML models (DT algorithm, RF algorithm, extra tree classifier, and XGBoost) and SVM to our dataset by using their default parameters. Decision-tree has been widely implemented in various domains, such as in medical fields [75], marketing prediction tasks [76], and education [77,78], due to its various well-known attractive features [79]. Features such as simplicity, comprehensive calculations, no required parameters, and the capability of handling mixed types of data, encouraged us to select DT in this study. Random forests are used in this study due to being easy and stable with many interesting properties. One of these interesting properties is that they provide a powerful computation of variables [80]. The extra tree classifier is one of the learning algorithms that can aggregate the results of multiple de-correlated decision trees collected in a "forest" to output its classification result. It has been applied in this study because it is similar to RF; however, it is faster, and its method in the construction of the decision tree in the forest is optimal. The tree-based algorithm is simple and requires less data; additionally, it is easy

to understand and easily implemented [81,82]. Conversely, deep learning (artificial neural network) is complex, computationally expensive, and requires more data [83]. Additionally, we did not use the naive Bayes algorithm, which is a very commonly used algorithm to solve real-life problems because it overlooked how to calculate probabilities [84]. We used SVM because the performance is good using small datasets [85,86]. Moreover, it does not apply a strict requirement on the number of samples and sample points; additionally, it can process error distributions and can be easily promoted. XGBoost was used in this study because it has higher predictive accuracy than other ML algorithms, such as SVM and DT [87,88]. Our dataset contains both numeric and categorical attributes. Therefore, the selected model must perform well on categorical data. For the first experimental dataset, we used 19 input features, which are shown in Table 1, and the target variable was *specialization*. First, we converted the target variable (specialization) into a binary form (0,1) by using python, wherein "0" denotes marketing and finance (Mk&Fin) and "1" represents the Market and Human resources Field (Mk&HR). As ML algorithms cannot directly work on categorical data, and to convert input features in digital form, we used a hot encoding technique. Hot encoding is a technique that can map categorical data into integers; as a result, the Ml algorithm can produce better results. Hot encoding is useful when there is no relationship between the variables. The 10-fold cross-validation method was used to increase the generalization ability of the models and to ensure that the model behavior was optimal. Furthermore, accuracy, true positive rate (TPR), false-positive rate (FPR), and receiver operating characteristic (ROC) curve were used as evaluation metrics. The accuracy represents the percentage of correct predictions of the model given unseen data. In ML, the TPR is also known as recall or sensitivity and indicates the percentage of actual positive values that are correctly predicted by the model. Finally, the ROC curve plots the TPR of the model [89]. The current study performance metrics are shown below.

> TPR = TP/TP + FN FPR = FP/FP + TN Accuracy = TP + TN/TP + TN + FP + FN

**Notes:** TP (true positive), FN (false negative), TN (true negative), FP (false positive).

DT algorithms belong to the supervised category of ML algorithms. A DT is simple and easily understandable. We selected this technique because it is widely used by researchers due to its simplicity. In addition, a DT has some great advantages, such as representing rules that could be easily understood and interpreted by users [81]. This type of algorithm performs well for categorical and numerical attributes and does not require complex data preparation. In short, ML classifiers and their outputs are easy to understand for individuals with a non-analytical background [90]. The default parameters (ccp\_alpha = 0.0, criterion = 'gini', min\_samples\_split = 2) are used to train the DT model, and an accuracy of 55% was obtained by DT using 10-fold cross-validation. Table 3 shows that the DT correctly classifies student specialization with a TPR of 0.87 and an FPR of 0.71.

**Table 3.** Confusion matrix of the decision tree classifier.

In the second step of the first experiment, RF classifiers were used to predict student specialization given a student placement dataset. The RF classifier is a supervised learning algorithm that applies to both classification and regression problems [80]. RF creates multiple DTs from random sample data and then gives predictions on high-accuracy trees [91,92]. The RF classifier predicts the student field specialization with the following default parameters (bootstrap = True, ccp\_alpha = 0.0, criterion = 'gini', max\_depth = 15, max\_features = 'auto',max\_leaf\_nodes = 10). The TPR of RF is 0.70, and the FPR is 0.20, as shown in Table 4.

**Table 4.** Confusion matrix of the random forest classifier.

Extra tree classifiers (ETC) are used in the third step to predict the student's field of specialization. ETC is an ensemble learning technique that collects the result of multiple trees. The approach is similar to an RF classifier, but the tree construction method differs. The accuracy of the ETC classifier on the student dataset is 0.52. During training, the ETC Classifier used the default parameters (n\_estimators = 100, random\_state = 0) to predict student specialization with high accuracy. The TPR and FPR of the ETC classifier were 0.53 and 0.49, respectively, as shown in Table 5.

**Table 5.** Confusion matrix of the extra tree classifier.

In the fourth step of the first experiment, we used the SVM classifier. SVM is a supervised learning algorithm that is mostly used for classification problems. SVM finds the hyperplane that divides a dataset into two classes [93]. SVM classifiers perform well on clean and small datasets. Furthermore, SVM is faster than other machine learning techniques [93]. The best accuracy (52%) of SVM was achieved with the following default parameters (random\_state = 0, tol = 1 × 10−5 ), as shown in Table 6. The TPR and FPR of SVM are 0.53 and 0.49, respectively, as shown in Table 7.

**Table 6.** Experimental results of the machine learning models on the current study dataset.

**Table 7.** Confusion matrix of the support vector machine (SVM).

Finally, we used the XGBoost classifier to predict student specializations by using default parameters (base\_score = 0.5, booster = 'gbtree', colsample\_bylevel = 1, learning\_rate = 0.1). XGBoost is a popular boosting technique in ensemble ML, and its performance is good on structured and tabular data. XGBoost is also called GBC. XGBoost uses parallel tree boosting to solve real-life data science problems. We used this technique because its impact has been widely recognized in many machine learning and data mining challenges, where

it has become a commonly used and popular tool among Kaggle's competitors and data scientists [87]. XGBoost predicts the student's specialization with an accuracy of 61%, and the TPR and FPR of the XGBoost classifier are 0.57 and 0.35, respectively, as shown in Table 8.

**Table 8.** Confusion matrix of the XGBoost classifier.

In the first experiment, the TPR and accuracy of the RF and GBC classifiers were higher than DT, SVM, and ETC, and the FPR was lower than that of DT, SVM, and ETC, as shown in Figure 5, which indicates that the performance of these classifiers in predicting field specializations is good, compared to that of the alternatives. Sometimes, accuracy is misleading when the dataset is imbalanced [94–96]. In other words, if the ratio of some classes is less than that of others in the dataset, we used the ROC curve and TPR, which is also called recall. We used ROC to further understand the performance of the models. The ROC is an evaluation metric that represents the performance of an ML model in the form graph [97] by plotting the TPR and FPR of the model. Figure 5 shows that the TPR of the RF and GBC classifiers was high, compared to that of the DT, SVM, and ETC. The results also showed that RF and GBC classifiers are appropriate classifiers to build student field recommendation systems because they can handle ordinal, non-ordinal, and categorical data and are also good choices for skewed and multimodal data [98]. Moreover, the RF and GBC classifier ensemble method outperforms simple DT classifiers. The previous study showed that the performance of SVM in small data is good and faster [93]. Furthermore, DT and ETC are unstable and have high sensitivities for overfitting classifiers [15].

**Figure 5.** ROC curves of all the classifiers.

**Question 2:** How is a student's undergraduate major path choice associated with that student's academic performance and the job market?

We performed a second experiment to investigate the second research question. The second experiment investigated how a student's field of specialization was associated with that student's previous academic history, salary, and experience. First, we observed the baseline characteristics of different selected variables, such as secondary education percentage, higher secondary education percentage, degree percentage, MBA percentage, and employability test percentage. Then, we performed statistical analysis (Spearman correlation) to assess the relationship between a student's major and other input variables. The Spearman correlation shows how closely two variables are related. Table 9 shows the Spearman correlation between student field specialization and other input variables of the

current study. The statistical results show that higher student marks in higher secondary (hsc\_p), university degree (Degree\_p), and entry test (etest\_p) play a significant role in student field of specialization, and we can easily suggest study fields according to these features. Furthermore, student work experience (work\_exp) and job placement (status) also impact student field specialization. Several interesting observations are obtained from the above statistical analysis. First, students at the high secondary stage are very excited about their field in university (undergraduate level) or undergraduate major path choice. At this level, every student wants to go into a good study field. In other words, students place high importance on student field specialization decisions. Second, students who get admitted to their favorite field graduate with higher grades. Third, the student field of specialization also affects student work experience and market salary. Fourth, students who applied for their favorite field may receive high marks on their university entry test. Fifth, students who graduate in their favorite field have a high chance of getting a job. In addition, the student marks percentage in a higher secondary, university degree, and entry test is useful criteria for study field suggestion. The result also demonstrates that student marks percentage in higher secondary, university degree, entry test assignment, and other factors are beneficial to the intelligent recommendation system. Using these variables, the proposed recommender system correctly predicts student field specialization according to their marks and preferences.

**Table 9.** Spearman correlation between student specialization and other input variables.

The current study results show that we can design a recommendation system for predicting the field of specialization using RF, GBC, and SVM classifiers. The proposed recommendation system will offer a variety of functions to students and college/university staff, such as recommending appropriate fields of study for students, ranking highly demanding fields in the coming and current years, and predicting the future salary of recommended fields. The results also show that higher secondary education is an appropriate stage to enter a good study field. Moreover, having a suitable specialization might affect students' academic performance and job salary, which could assist in lessening their

anxiety and confusion and could lead to significantly better study program completion and increase graduation rates in the future. Having early awareness of the estimated number of incoming freshmen per study specialization program could also be of high value to the college administration. With this great insight, they would be able to allocate required resources per specialization field and better prepare schedules.

#### **5. Conclusions**

Unsuitable field of specialization selection for new graduate students has serious consequences for students and universities. Choosing an appropriate field of specialization is a critical determinant of a student's future academic and work progression. The current study used a machine learning and statistical approach to investigate the student study field. In this study, we extracted data from the Kaggle repository, which is publicly available for research purposes, and then converted these data into a form that is acceptable for ML models. We then applied several supervised learning techniques (DT, RF, ETC, and GBC) to our dataset and evaluated them using a 10-fold cross-validation method. The findings showed that RF and, GBC predict student study fields with accuracy 0.75% and 0.61 respectively. The results indicate that RF and GBC are the most appropriate, classifiers to integrate into the intelligent field recommender system for predicting suitable fields for students according to the job market because the performance of these classifiers is good on less training data Additionally, the intelligent field recommender system will help educational institutions to suggest study fields according to the current job market and demand. Using this recommendation system, students can select a field that is according to the job market. The study also demonstrated that the student field of specialization selection is mostly dependent on the percentage of marks in higher secondary, university, and entry tests. Student work experience and student job placement also affect the student's field of study. Furthermore, student mark percentage in higher secondary, university, and entry tests are appropriate criteria for all higher education institution admission departments to select the right undergraduate major path choice.

This experiment aims to investigate whether these data could be used to suggest an appropriate study field for students. This study used student academic data and job market data from the Kaggle repository. The student's field of specialization is a complex problem that also depends on other factors, such as country and student family background. Therefore, these factors must be further investigated.

**The Current study limitations:** There are some limitations, for example, the current study has limited specializations, records, and input features. In the future, we will use design surveys to assess other factors or input features related to the student field of specialization. Additionally, the accuracy of RF, GBC, and SVM models will be further improved by increasing the number of observations and hyperparameter tuning. Then, we will build an intelligent field recommendation system using collaborative filtering to recommend suitable fields to students according to their preferences and the job market. This proposed system will help the university admission system make quick decisions about student field recommendations.

**Author Contributions:** Conceptualization, A.O.A. and M.S.M.R.; Methodology, M.H., S.H.J. and S.H.; Software, M.H.; Validation, M.H., M.S.M.R. and M.L.J.; Formal Analysis, A.O.A. and M.H., S.H.J.; Investigation, A.O.A. and S.H.J.; Resources, A.O.A.; Data Curation, M.H.; Writing—Original Draft Preparation, A.O.A. and N.A.; Writing—Review & Editing, A.O.A., S.H.J. and M.H.; Visualization, M.H.; Supervision, M.S.M.R., I.A. and M.H.; Project Administration, A.O.A., M.H. and N.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research work is funded under the project number IFPIP:536-830-1442.

**Institutional Review Board Statement:** Data is being taken from an authorized open source site (Kaggle).

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The current study data are publicly available online (https://www. kaggle.com/benroshan/factors-affecting-campus-placement 7 November 2021) for research purposes. No participants' personal information (e.g., name or address) was included in this study.

**Acknowledgments:** The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number IFPIP:536-830-1442 and King Abdulaziz University, DSR, Jeddah, Saud Arabia.

**Conflicts of Interest:** The authors have no conflict of interest.

## **References**

## *Review* **Trends and Directions of Financial Technology (Fintech) in Society and Environment: A Bibliometric Study**

**Adeel Nasir 1,† , Kamran Shaukat 2,3,\* ,† , Kanwal Iqbal Khan 4,\*, Ibrahim A. Hameed 5,\* , Talha Mahboob Alam 6 and Suhuai Luo 2**

**Abstract:** The contemporary innovations in financial technology (fintech) serve society with an environmentally friendly atmosphere. Fintech covers an enormous range of activities from data security to financial service deliverables that enable the companies to automate their existing business structure and introduce innovative products and services. Therefore, there is an increasing demand for scholars and professionals to identify the future trends and directions of the topic. This is why the present study conducted a bibliometric analysis in social, environmental, and computer sciences fields to analyse the implementation of environment-friendly computer applications to benefit societal growth and well-being. We have used the 'bibliometrix 3.0' package of the r-program to analyse the core aspects of fintech systematically. The study suggests that 'ACM International Conference Proceedings' is the core source of published fintech literature. China leads in both multiple and single country production of fintech publications. Bina Nusantara University is the most relevant affiliation. Arner and Buckley provide impactful fintech literature. In the conceptual framework, we analyse relationships between different topics of fintech and address dynamic research streams and themes. These research streams and themes highlight the future directions and core topics of fintech. The study deploys a co-occurrence network to differentiate the entire fintech literature into three research streams. These research streams are related to 'cryptocurrencies, smart contracts, financial technology', 'financial industry stability, service, innovation, regulatory technology (regtech)', and 'machine learning and deep learning innovations'. The study deploys a thematic map to identify basic, emerging, dropping, isolated, and motor themes based on centrality and density. These various themes and streams are designed to lead the researchers, academicians, policymakers, and practitioners to narrow, distinctive, and significant topics.

**Keywords:** fintech; financial technology; blockchain; deep learning; regtech; environment; social sciences

### **1. Introduction**

As the world is entering the digitalisation age, many organisations are adopting and investing in new technologies to cope with societal and environmental needs. The financial industry is not so different; it evolves day by day with various fintech technologies. Fintech

**Citation:** Nasir, A.; Shaukat, K.; Iqbal Khan, K.; A. Hameed, I.; Alam, T.M.; Luo, S. Trends and Directions of Financial Technology (Fintech) in Society and Environment: A Bibliometric Study. *Appl. Sci.* **2021**, *11*, 10353. https://doi.org/10.3390/ app112110353

Academic Editor: Jianbo Gao

Received: 9 October 2021 Accepted: 1 November 2021 Published: 4 November 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 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/).

is the combination of financial technology used to enhance financial operations' effectiveness and efficiency. Its rise has changed the ways of businesses of commercial banking systems [1]. It is implied that it is an emerging area of finance with a significant contribution to technology [2,3]. Nowadays, it has become an industry that successfully takes advantage of the recent development of information technology tools such as cloud computing, big data, the internet of things, social computing, etc. [4]. This advancement supports the existing business structure but also helps the financial service industry to introduce new processes, systems, products, and services that can enhance their efficiency [5]. The companies are intended to restructure their businesses and are more inclined towards hybrid client interactions and more customer self-services, [6], particularly during Covid-19. effectiveness and efficiency. Its rise has changed the ways of businesses of commercial banking systems [1]. It is implied that it is an emerging area of finance with a significant contribution to technology [2,3]. Nowadays, it has become an industry that successfully takes advantage of the recent development of information technology tools such as cloud computing, big data, the internet of things, social computing, etc. [4]. This advancement supports the existing business structure but also helps the financial service industry to introduce new processes, systems, products, and services that can enhance their efficiency [5]. The companies are intended to restructure their businesses and are more inclined towards hybrid client interactions and more customer self-services, [6], particularly during Covid-19.

*Appl. Sci.* **2021**, *11*, x FOR PEER REVIEW 2 of 19

The latest fintech technology trends include categorising and assessing various artificial intelligence technologies based on their availability and maturity [7]. Furthermore, it provides studies such as the contextualisation of users' facilities and experience of the web interface in the financial service industry [8], machine learning tools in electronic finance market trading [9], and advanced modelling for stock movements [10] and settlement models with renewable energy that are based on blockchain technology [11]. The researchers believe that there is a need to develop a conceptual framework to understand the perspectives of Fintech [12]. Few researchers presented the Fintech framework based on the flow of money theories applied in the E-commerce system. However, still required is a suitable conceptual framework developed in the context of relevant fields [13]. As well, there is a still need to identify the core contributors in the field of computer, social, and environmental sciences, as well as the future research streams and themes that will lead the scholars to make a significant contribution in the area. Therefore, the purpose of the current study is to conduct a bibliometric analysis for the period of 2010 to 2021. The latest fintech technology trends include categorising and assessing various artificial intelligence technologies based on their availability and maturity [7]. Furthermore, it provides studies such as the contextualisation of users' facilities and experience of the web interface in the financial service industry [8], machine learning tools in electronic finance market trading [9], and advanced modelling for stock movements [10] and settlement models with renewable energy that are based on blockchain technology [11]. The researchers believe that there is a need to develop a conceptual framework to understand the perspectives of Fintech [12]. Few researchers presented the Fintech framework based on the flow of money theories applied in the E-commerce system. However, still required is a suitable conceptual framework developed in the context of relevant fields [13]. As well, there is a still need to identify the core contributors in the field of computer, social, and environmental sciences, as well as the future research streams and themes that will lead the scholars to make a significant contribution in the area. Therefore, the purpose of the current study is to conduct a bibliometric analysis for the period of 2010 to 2021.

The current study highlights various influential and conceptual aspects of fintech technology in computer, social, and environmental sciences from the last decade. The significant growth of fintech technologies has raised specific questions for academicians and practitioners. These questions are: (1) What are the contributory key authors and journals of the fintech literature? (2) What are core affiliations, sources and contributing countries in the field of fintech and computer, management, and environmental sciences? (3) What key themes does fintech offer in the field of computer, management, and environmental sciences? (4) With the help of research streams offered by fintech literature, what are the future research gaps to fill by researchers, academicians, and practitioners? The workflow of the study is presented in Figure 1. The current study highlights various influential and conceptual aspects of fintech technology in computer, social, and environmental sciences from the last decade. The significant growth of fintech technologies has raised specific questions for academicians and practitioners. These questions are: (1) What are the contributory key authors and journals of the fintech literature? (2) What are core affiliations, sources and contributing countries in the field of fintech and computer, management, and environmental sciences? (3) What key themes does fintech offer in the field of computer, management, and environmental sciences? (4) With the help of research streams offered by fintech literature, what are the future research gaps to fill by researchers, academicians, and practitioners? The workflow of the study is presented in Figure 1.

**Figure 1. Figure 1.**  Workflow of study. Workflow of study.

#### **2. Study Design and Descriptive Outlook**

This study aims to conduct a comprehensive bibliometric analysis to identify the influential and conceptual structure of the fintech literature in computer, social, and environmental sciences. The procedure and steps of studies are shown in Figure 1, using

the workflow followed by Nasir et al. [14]. The study proceeds with the study design, where we define our research questions and methodology in step 1. We have used the 'bibliometrix 3.0' package of r-studio to analyse various influential and conceptual aspects of fintech literature [15]. We have used Scopus, emerald, and science direct databases for searching fintech literature. The finalised search query is simply fintech in the computer, social, and environmental science fields. We analysed on 17 March 2021. There is a minimum contribution of fintech literature in the rest of the areas. Furthermore, we have removed the duplicates from our data and ended up with 1556 documents, from which 786 are research articles, 26 are books, 86 are book chapters, 553 are conference papers, 61 are reviews, and 22 are conference reviews and editorials.

Table 1 also highlights the total number of author keywords and keyword plus used by the literature; furthermore, we have taken the literature from 2010 to 2021. The table describes various characteristics such as the collaboration between countries index, authors per documents, and single and multiple-authored documents in fintech literature. Figure 2 represents the annual production of publications per year; fintech is a recent trend globally as, in 2020, there were 724 publications. The growth is substantial, from 1 publication in 2010, to 10 publications in 2015, to 724 publications in 2020. The year 2021 is still ongoing, however, we can see 69 publications in fintech, more than the 30 publications in 2016. The relevant growth in financial technology literature starts in 2017, where we can see 107 publications, followed by 262 in 2018 and 348 in 2019.

**Table 1.** Main information about fintech literature.

**Figure 2.** Annual publications production radar. **Figure 2.** Annual publications production radar.

**3. Bibliometric Analysis**

fintech literature.

fintech literature.

*3.1. Influential Aspects*  3.1.1. Core Sources

marketing platform [27].

erature. The bibliometric analysis is twofold. First, we study dynamic, influential aspects such as core authors having a considerable impact in fintech. We highlight core sources and how they impact the literature on various topics. We propose the main countries and corresponding countries of fintech literature. Then, we discuss primary affiliations or institutions conducting significant research on multiple topics of fintech. We discuss the main contributions of highly ranked documents. We lastly indicate the main keywords in

The second part of the bibliometric analysis provides a conceptual framework. In this section, we discuss various themes and streams of fintech literature. It provides an understanding of the core topics of fintech in computer, social, and environmental sciences, and helps us propose what the future holds for fintech technologies. This section presents a co-occurrence network which creates a matrix of keywords and links them together in various clusters that suggest the main topics. Furthermore, we investigate the thematic map, which divides multiple topics into four quadrants with different characteristics. Finally, from emerging and developing issues, we propose a future research agenda in

Figure 3 represents the top 10 critical influential sources of fintech literature. ACM International Conference Proceedings is the core source in the field. Contemporary research is often presented at the ACM international conference. The core source represents recent studies of diverse, agile tailoring models [16], the advanced mechanism for agricultural marketing and smart contracts using blockchains [17], and the facilitation of decentralised ledger technology for financial derivative markets [18]. T he second leading source of fintech literature is Advances in Intelligent Systems and Computing. This journal publishes literature on the digitalisation of the banking and insurance sector [19], introducing the QR-code-based payment systems at fintech future [20] and dynamic uses of blockchain technology such as e-voting [21]. The third primary source is lecture notes in computer sciences that involve literature on fintech innovation with patent data [22], the role of information technology (IT) in developing fintech business model canvases [23], and the use of fintech AI that is interpretable in evaluating the efficiency of lending risk [24]. IEEE access provides literature on the development of a blockchain-based microgrid transaction model for optimised bidding [25], market settlement models based on blockchain technology while securing efficient energy trading mechanisms [26], and contract production and transaction with dynamic technology such as a block-enabled integrated

#### **3. Bibliometric Analysis**

The forthcoming segment represents the holistic bibliometric analysis of fintech literature. The bibliometric analysis is twofold. First, we study dynamic, influential aspects such as core authors having a considerable impact in fintech. We highlight core sources and how they impact the literature on various topics. We propose the main countries and corresponding countries of fintech literature. Then, we discuss primary affiliations or institutions conducting significant research on multiple topics of fintech. We discuss the main contributions of highly ranked documents. We lastly indicate the main keywords in fintech literature.

The second part of the bibliometric analysis provides a conceptual framework. In this section, we discuss various themes and streams of fintech literature. It provides an understanding of the core topics of fintech in computer, social, and environmental sciences, and helps us propose what the future holds for fintech technologies. This section presents a co-occurrence network which creates a matrix of keywords and links them together in various clusters that suggest the main topics. Furthermore, we investigate the thematic map, which divides multiple topics into four quadrants with different characteristics. Finally, from emerging and developing issues, we propose a future research agenda in fintech literature.

#### *3.1. Influential Aspects*

#### 3.1.1. Core Sources

Figure 3 represents the top 10 critical influential sources of fintech literature. ACM International Conference Proceedings is the core source in the field. Contemporary research is often presented at the ACM international conference. The core source represents recent studies of diverse, agile tailoring models [16], the advanced mechanism for agricultural marketing and smart contracts using blockchains [17], and the facilitation of decentralised ledger technology for financial derivative markets [18]. T he second leading source of fintech literature is Advances in Intelligent Systems and Computing. This journal publishes literature on the digitalisation of the banking and insurance sector [19], introducing the QR-code-based payment systems at fintech future [20] and dynamic uses of blockchain technology such as e-voting [21]. The third primary source is lecture notes in computer sciences that involve literature on fintech innovation with patent data [22], the role of information technology (IT) in developing fintech business model canvases [23], and the use of fintech AI that is interpretable in evaluating the efficiency of lending risk [24]. IEEE access provides literature on the development of a blockchain-based microgrid transaction model for optimised bidding [25], market settlement models based on blockchain technology while securing efficient energy trading mechanisms [26], and contract production and transaction with dynamic technology such as a block-enabled integrated marketing platform [27]. *Appl. Sci.* **2021**, *11*, x FOR PEER REVIEW 5 of 19

Table 2 represents the primary sources listed and arranged based on h-index and g-

h-index of 7 and a g-index of 13. Its 15 publications have 186 total citations, and the first article on fintech was published in 2015. The journal highlights the key areas of fintech, which are initial coin offerings (ICO) and information asymmetries associated with it [28], the proposed value created by the mobile payment ecosystem [29], and contract signing protocol with blockchain technologies [30]. Financial innovation comes on the second number in the source impact list with an h-index of seven, a g-index of nine, and its total 9 publications having 148 total citations. It covers the main topics of fintech governance and emergence in Peer to Peer (P2P) lending [31,32] and the growth of digital banking in the financial industry [33]. The first article on technological forecasting and social change (ranked third) was published in 2018; however, it comes in third place with 89 total cita-

**Figure 3.** Most relevant sources, according to publications. **Figure 3.** Most relevant sources, according to publications.

tions with 11 fintech technology publications.

3.1.2. Influential Authors in Fintech Literature

**Table 2.** Top ten sources with impact.

Electronic commerce research and applications 7 13 1 186 15 2015

Technological forecasting and social change 5 9 1.25 89 11 2018 European business organisation law review 5 7 1.25 53 9 2018 Handbook of blockchain, digital finance, and inclusion 5 5 1 38 9 2017

Industrial management and data systems 5 6 1.25 85 6 2018 Journal of economics and business 5 6 1.25 231 6 2018 Journal of management information systems 5 6 1.25 204 6 2018

**Source h\_Index g\_Index m\_Index TC NP PY\_Start**

Financial innovation 7 9 1 148 9 2015

IT professional 5 7 1 150 7 2017 Electronic markets 5 6 1.25 124 6 2018

to h, g and m-index, while panel B grades authors according to total citations.

This section represents the core authors who contributed significantly to fintech in the computer, social and environmental sciences. Table 3 describes the ranking of the principal authors and divides the order into two panels. Panel A represents ranking according

Table 2 represents the primary sources listed and arranged based on h-index and g-index. It shows the impact of the journal on fintech literature. Electronic commerce, research, and applications are top-ranked sources for producing fintech literature with an h-index of 7 and a g-index of 13. Its 15 publications have 186 total citations, and the first article on fintech was published in 2015. The journal highlights the key areas of fintech, which are initial coin offerings (ICO) and information asymmetries associated with it [28], the proposed value created by the mobile payment ecosystem [29], and contract signing protocol with blockchain technologies [30]. Financial innovation comes on the second number in the source impact list with an h-index of seven, a g-index of nine, and its total 9 publications having 148 total citations. It covers the main topics of fintech governance and emergence in Peer to Peer (P2P) lending [31,32] and the growth of digital banking in the financial industry [33]. The first article on technological forecasting and social change (ranked third) was published in 2018; however, it comes in third place with 89 total citations with 11 fintech technology publications.

3.1.2. Influential Authors in Fintech Literature

This section represents the core authors who contributed significantly to fintech in the computer, social and environmental sciences. Table 3 describes the ranking of the principal authors and divides the order into two panels. Panel A represents ranking according to h, g and m-index, while panel B grades authors according to total citations.

**Table 3.** Top authors in fintech literature.

D.W. Arner is ranked first according to the top five author list in panel A. one of his main contributions is drivers of financial inclusion and compliance with sustainable development goals (SDGs) [34]. Furthermore, his work covers the protection against fraud and detection through digital identity infrastructure to fulfil the obligations of knowing your customers [35]. He also worked on fintech regulations, digitalisation of manual reporting, and compliance processes such as regtech (regulation technology) [36–38]. In panel A, R.P. Buckley (ranked second) collaborated with D.W. Arner in most of his work [39]. His work as the principal author is related to dependence on digital technologies and cybersecurity during the time of the COVID-19 pandemic [40]. With D.W. Arner, he worked on regtech to control systematic risk in financial crisis, data protection rules, and electronic identification legislation [41]. P. Gomber in Panel B has the highest rank concerning the number of citations. His most cited work is related to technology innovation, transformation, and disruption in financial services [42]. The common name in both panels is R.J. Kauffman, with five publications from 2015. He is the second-highest cited author in the field of fintech in computer, social, and environmental sciences. His apparent work represents the computational social sciences [43] and the changing environment concerning payments through cards [44].

The most cited publication is done by S. Adhami, with only one publication in 2018 and with a total number of citations of 126. He is the fifth most highly cited author because he has produced only one publication in fintech technologies. His work is about the ICOs phenomena, and he addressed the success of token offerings [45].

#### 3.1.3. Most Relevant Affiliations

This section deals with the most relevant affiliations. As shown in Figure 4, Bina Nusantara University is the core affiliation, with 33 publications in fintech technology. The university evolved from a computer training institute founded on 21 October 1974 [46]. The affiliation conducts studies on asset baked tokens with blockchain networks [47], consumer protection in lending transactions [48,49], mobile payment application and acceptance [50], and unified theory of acceptance and use of technology [51]. *Appl. Sci.* **2021**, *11*, x FOR PEER REVIEW 7 of 19

**Figure 4.** Most relevant affiliations. **Figure 4.** Most relevant affiliations.

3.1.4. Influential Countries

cited significantly.

Soongsil University is the second-largest affiliation with 18 publications. The university was founded in 1897 by William M. Baird as a private school. Soongsil pioneered a computer science program in Korea, ranked number two in Soul National University [52]. The main topics that Soongsil University covers are small foreign currency remittance with blockchain technology [53], machine learning and adaptive fintech security provision [54], the emergence of mobile-accessible payment services [55,56], and peer to peer (P2P) lending applications [57]. In third place, Universitas Indonesia published 17 documents. The significant studies conducted are related to the effect of fintech on stock re-Soongsil University is the second-largest affiliation with 18 publications. The university was founded in 1897 by William M. Baird as a private school. Soongsil pioneered a computer science program in Korea, ranked number two in Soul National University [52]. The main topics that Soongsil University covers are small foreign currency remittance with blockchain technology [53], machine learning and adaptive fintech security provision [54], the emergence of mobile-accessible payment services [55,56], and peer to peer (P2P) lending applications [57]. In third place, Universitas Indonesia published 17 documents. The significant studies conducted are related to the effect of fintech on stock returns [58], fintec-

turns [58], fintec- based financial instructions, financial performance and consumer be-

Concerning Table 4, the USA and China are leading in terms of citations and number of publications. It is seen that China has well over 400 publications, which is 41% higher than the USA's number of publications. Still, on the other hand, in the citations section, the USA has published some of the most fascinating and citable work in fintech and has secured 742 citations. China has 419 publications got 457 citations. There are 114 publications from South Korea; however, the country earned 411 citations. The UK is in the third position in terms of publications; however, UK publications obtained 394 citations. Indonesia and India are in the fourth and fifth positions in terms of publications; however, these countries are not on the citations list. In contrast, Italy and Taiwan are the last two countries in terms of the number of publications. However, a small number of publications from these two countries have significant contributions, as their publications are

Figure 5 represents the top 10 corresponding author countries, and it is divided into two parts. The figure's first part shows that the orange colour represents many multiple country publications (MCP). These publications are collaborated on by at least one foreign country author. Bars in green represent single country publications (SCP), where corre-

spondence and collaborations are from the same country [61].

based financial instructions, financial performance and consumer behaviour [59] and P2P lending, and women's empowerment [60].

#### 3.1.4. Influential Countries

Concerning Table 4, the USA and China are leading in terms of citations and number of publications. It is seen that China has well over 400 publications, which is 41% higher than the USA's number of publications. Still, on the other hand, in the citations section, the USA has published some of the most fascinating and citable work in fintech and has secured 742 citations. China has 419 publications got 457 citations. There are 114 publications from South Korea; however, the country earned 411 citations. The UK is in the third position in terms of publications; however, UK publications obtained 394 citations. Indonesia and India are in the fourth and fifth positions in terms of publications; however, these countries are not on the citations list. In contrast, Italy and Taiwan are the last two countries in terms of the number of publications. However, a small number of publications from these two countries have significant contributions, as their publications are cited significantly. *Appl. Sci.* **2021**, *11*, x FOR PEER REVIEW 8 of 19 **Table 4.** Core Countries in Terms of Number and Citations. **Country Total Citations Country Number of Publications**

**Table 4.** Core Countries in Terms of Number and Citations. USA 742 China 419 China 457 USA 296

Figure 5 represents the top 10 corresponding author countries, and it is divided into two parts. The figure's first part shows that the orange colour represents many multiple country publications (MCP). These publications are collaborated on by at least one foreign country author. Bars in green represent single country publications (SCP), where correspondence and collaborations are from the same country [61]. USA comes in second place with 118 corresponding articles, 90 SCP, and 28 MCP. The exact amount of SCP is produced by Indonesia (ranked third); however, it lacks in MCP with only four publications. India (ranked fourth) and the United Kingdom (ranked fifth) have more inter-country collaboration than Indonesia. India has 9 MCP, and the United Kingdom has 23 MCPs.

country has produced 170 corresponding articles, of which 124 are SCP, and 46 are MCP.

**Figure 5.** Corresponding author country. **Figure 5.** Corresponding author country.

and banks, as shown in Table 5.

3.1.5. Keyword Analysis Keywords are an essential factor in searching the literature. We have found essential As shown in Figure 5, China has the highest number of both SCP and MCP. The country has produced 170 corresponding articles, of which 124 are SCP, and 46 are MCP.

keywords in fintech literature, shown in the shape of word clouds in Figure 6. It is evident

word in studies relating to technology, market, and blockchain. Similar keywords are found in the study's title; besides, the title covers keywords related to the digital market

Keryword plus author represents authentic fintech topics such as financial markets, investments, electronic trading, artificial intelligence, financial services, blockchain, and commerce. In addition to keyword plus author, keywords represent thematic topics in fintech such as blockchain technologies, financial technology, machine learning, financial inclusion, big data, artificial intelligence, and cryptocurrency bitcoin fintech innovation. This study suggests that author keywords represent significant and authentic keywords compared to other sources, and these keywords cover and analyse a wide variety

USA comes in second place with 118 corresponding articles, 90 SCP, and 28 MCP. The exact amount of SCP is produced by Indonesia (ranked third); however, it lacks in MCP with only four publications. India (ranked fourth) and the United Kingdom (ranked fifth) have more inter-country collaboration than Indonesia. India has 9 MCP, and the United Kingdom has 23 MCPs.

#### 3.1.5. Keyword Analysis

Keywords are an essential factor in searching the literature. We have found essential keywords in fintech literature, shown in the shape of word clouds in Figure 6. It is evident that fintech is the main keyword for the literature; our focus is on other keywords representing various topics or fields. In the abstract, financial is the most commonly used keyword in studies relating to technology, market, and blockchain. Similar keywords are found in the study's title; besides, the title covers keywords related to the digital market and banks, as shown in Table 5. *Appl. Sci.* **2021**, *11*, x FOR PEER REVIEW 9 of 19 of topics in fintech. We can find and propose key themes and streams in fintech in computer, social, and environmental sciences by analysing the author's keywords.

**Figure 6.** Word clouds.

**Table 5.** Research stream of Fintech literature. **Keywords Clusters Research Stream**  Blockchain, bitcoin, cryptocurrency, entrepreneurial finance, smart contracts, security, internet of Red Cryptocurrencies, smart contracts, and financial tech-Keryword plus author represents authentic fintech topics such as financial markets, investments, electronic trading, artificial intelligence, financial services, blockchain, and commerce. In addition to keyword plus author, keywords represent thematic topics in fintech such as blockchain technologies, financial technology, machine learning, financial inclusion, big data, artificial intelligence, and cryptocurrency bitcoin fintech innovation.

**Figure 6.** Word clouds.

things, venture capital, the financial industry nology Fintech, financial inclusion, regulatory sandbox, innovation, financial technology, big data, regulations, regtech, banking, artificial intelligence, fi-Blue Financial industry stability, service, innovation, and reg-This study suggests that author keywords represent significant and authentic keywords compared to other sources, and these keywords cover and analyse a wide variety of topics in fintech. We can find and propose key themes and streams in fintech in computer, social, and environmental sciences by analysing the author's keywords.

> This section will study various themes and streams that are a significant part of the fintech literature. In addition to tools such as co-occurrence network, thematic map, and thematic evolution, we have proposed a potential future agenda for researchers, educa-

ulatory technology (regtech)

Machine learning, deep learning and artificial intelligence

nancial# (regulations, innovations, service, and stability), digital economy, digitalisation, mobile payment, p2p lending, business model, digital finance.

tionists, engineers, and policymakers.

*3.2. Conceptual Framework*

**Table 5.** Research stream of Fintech literature.

#### *3.2. Conceptual Framework*

This section will study various themes and streams that are a significant part of the fintech literature. In addition to tools such as co-occurrence network, thematic map, and thematic evolution, we have proposed a potential future agenda for researchers, educationists, engineers, and policymakers.

#### 3.2.1. Co-Occurrence Network

We have conducted the co-occurrence analysis using the author keyword and identified three main clusters of fintech literature, as shown in Figure 7. These clusters represent distinct research streams which can help the researchers to differentiate the literature significantly. A highly centralised cluster is a blue cluster representing the research stream we have given the name of digital transformation, innovation financial industry, and regulations. There are many topics to seek in this research stream, most prominently, the regulations related to analysing the application and implementation of fintech technologies. Skilful regulations are required for the outbreak of financial innovations throughout the economic systems [62].

Regulatory technology or regtech is a prominent topic that uses information technology for monitoring, compliance, and reporting [39]. Regtech is a better financial solution. Furthermore, certain shared ledger technologies provide potential solutions for finance and banking procedures [63].

Financial regulatory technology would help shape the digital currency's internationalisation by strengthening blockchain's legal context [64]. Tsai et al. [65] introduced the framework for online supply chain finance for small and medium enterprises and suggested replacing it with a traditional supply chain financial mechanism. The innovative regulatory framework streamed by fintech provides a conducive innovative environment, protecting consumer rights and ensuring financial stability with advanced ecosystems [66–72]. The red stream proposed by the co-occurrence network represents the overall research stream of financial technology in cryptocurrencies and smart contracts. Some of the literature is dedicated to the combination of the latest financial technologies and environmental sciences. Le et al. [73] studied the spillovers and connections among green bonds, fintech, and cryptocurrencies, and found long term hedging benefits among them. Many traditional offline activities have been become easy, comfortable, and secure with the penetration of blockchain technology. Rao et al. [21] suggest that the Ethereum blockchain has made e-voting secure and transparent. It has enhanced the scope of smart contracts. Fintech technologies have helped to develop new innovative financial products that transform traditional payment systems into mobile-based application payment systems [74]. Various fields of research are yet to be explored in fintech in relation with the blockchain technology.

Nasir et al. [55] studied blockchain technologies' core theme and research streams, and identified fintech technologies as the key emerging and developing theme. A part of fintech is about making smart contracts, and from the passage of time, the smart contract procedures have evolved significantly. They have become secure, regulated, and user friendly [75,76]. Fintech is making progress in preserving the environment. Hu et al. [77] proposed the trading system that is blockchain distributed carbon emission. significantly. A highly centralised cluster is a blue cluster representing the research stream we have given the name of digital transformation, innovation financial industry, and regulations. There are many topics to seek in this research stream, most prominently, the regulations related to analysing the application and implementation of fintech technologies. Skilful regulations are required for the outbreak of financial innovations throughout the economic systems [62].

We have conducted the co-occurrence analysis using the author keyword and identified three main clusters of fintech literature, as shown in Figure 7. These clusters represent distinct research streams which can help the researchers to differentiate the literature

*Appl. Sci.* **2021**, *11*, x FOR PEER REVIEW 10 of 19

3.2.1. Co-Occurrence Network

**Figure 7.** Co-occurrence network. **Figure 7.** Co-occurrence network.

Regulatory technology or regtech is a prominent topic that uses information technology for monitoring, compliance, and reporting [39]. Regtech is a better financial solution. Furthermore, certain shared ledger technologies provide potential solutions for finance and banking procedures [63]. Financial regulatory technology would help shape the digital currency's internationalisation by strengthening blockchain's legal context [64]. Tsai et al. [65] introduced the framework for online supply chain finance for small and medium enterprises and suggested replacing it with a traditional supply chain financial mechanism. The innovative regulatory framework streamed by fintech provides a conducive innovative environment, protecting consumer rights and ensuring financial stability with advanced ecosystems [66–72]. The red stream proposed by the co-occurrence network represents the overall research stream of financial technology in cryptocurrencies and smart contracts. Some of the The green research stream is away from centrality, indicating that some potential topics can set the future research agenda. The stream relates fintech literature with machine learning, deep learning, and stock markets. Certain studies study machine learning techniques to simplify, develop, and increase the online financial market trading systems [6]. Oliveira et al. [78] use machine learning techniques such as the k-mean algorithm, long short term memory (LSTM), and the density-based spatial clustering (DBSC) algorithm to forecast stock market price movements. Abe et al. [79] deploy deep learning on crosssections of various stock markets with multifactor models and indicate that deep learning is a significant model for the prediction of the cross-section of stock returns. There is a substantial development in the machine and deep learning techniques to propose the prediction model for stock markets [80–82]. Machine learning is also helpful in portfolio risk management [83].

#### literature is dedicated to the combination of the latest financial technologies and environ-3.2.2. Thematic Map

mental sciences. Le et al. [73] studied the spillovers and connections among green bonds, fintech, and cryptocurrencies, and found long term hedging benefits among them. Many We have a thematic map (Figure 8) in the study that proposes research themes according to four quadrants. According to Nasir et al. [14], the thematic map divides themes according to two factors (centrality and density). Centrality represents the high volume of work in a specific theme, and density means the importance of a particular theme. The first quadrant in Figure 8 indicates low-density themes with low centrality. These themes are either new, developing, or emerging because they have low density and low centrality. The

second quadrant represents basic themes that provide high volume (centrality) but that are less critical (density). The third quadrant represents essential themes in the field; however, less work has been done in such areas. This field should be discussed significantly because these themes can provide potential future directions. The fourth quadrant has themes with both high centrality and density. The second quadrant represents basic themes that provide high volume (centrality) but that are less critical (density). The third quadrant represents essential themes in the field; however, less work has been done in such areas. This field should be discussed significantly because these themes can provide potential future directions. The fourth quadrant has themes with both high centrality and density.

We have a thematic map (Figure 8) in the study that proposes research themes according to four quadrants. According to Nasir et al. [14], the thematic map divides themes according to two factors (centrality and density). Centrality represents the high volume of work in a specific theme, and density means the importance of a particular theme. The first quadrant in Figure 8 indicates low-density themes with low centrality. These themes are either new, developing, or emerging because they have low density and low centrality.

*Appl. Sci.* **2021**, *11*, x FOR PEER REVIEW 11 of 19

traditional offline activities have been become easy, comfortable, and secure with the penetration of blockchain technology. Rao et al. [21] suggest that the Ethereum blockchain has made e-voting secure and transparent. It has enhanced the scope of smart contracts. Fintech technologies have helped to develop new innovative financial products that transform traditional payment systems into mobile-based application payment systems [74]. Various fields of research are yet to be explored in fintech in relation with the blockchain technology. Nasir et al. [55] studied blockchain technologies' core theme and research streams, and identified fintech technologies as the key emerging and developing theme. A part of fintech is about making smart contracts, and from the passage of time, the smart contract procedures have evolved significantly. They have become secure, regulated, and user friendly [75,76]. Fintech is making progress in preserving the environment. Hu et al.

[77] proposed the trading system that is blockchain distributed carbon emission.

The green research stream is away from centrality, indicating that some potential topics can set the future research agenda. The stream relates fintech literature with machine learning, deep learning, and stock markets. Certain studies study machine learning techniques to simplify, develop, and increase the online financial market trading systems [6]. Oliveira et al. [78] use machine learning techniques such as the k-mean algorithm, long short term memory (LSTM), and the density-based spatial clustering (DBSC) algorithm to forecast stock market price movements. Abe et al. [79] deploy deep learning on cross-sections of various stock markets with multifactor models and indicate that deep learning is a significant model for the prediction of the cross-section of stock returns. There is a substantial development in the machine and deep learning techniques to propose the prediction model for stock markets [80–82]. Machine learning is also helpful in portfolio risk

#### **Figure 8.** Thematic map. **Figure 8.** Thematic map.

management [83].

3.2.2. Thematic Map

Digital economy, internet of things, and financial industry-related themes are in the first quadrant. These keywords represent the topic with low centrality and low density. These themes either emerge as a strong future agenda or drop out from the future literature.

In the digital economy, Rozi et al. [84] study innovations and privacy issues for startup companies. In the digital economy era, Jiang et al. [85] analysed the significant risk profile by deploying conditional value at risk models on China's financial banks and found significant systematic risk exposure of small banks compared to big financial institutions. The digital economy is possessed with considerable challenges, such as centralisation of technology, trust, and security; e-commerce transactions are shaping the digital economy's future. Ferrer-Gomila [30] uses the blockchain for contract signing between various parties, making e-commerce transactions easy and cost-effective. One of the emerging topics is industry 4.0. Li et al. [86] use blockchain for immutable, secure, transparent, and auditable peer to peer energy transactions in the industrial internet of things. Moreover, the industrial and financial internet of things is discussed by [87–90].

The basic themes of fintech literature are filled with various significant topics. It is obvious that, for topics related to financial technology, fintech will have strong centrality in the literature. Furthermore, in recent times, blockchain technology for auditable, immutable, transparent, and secure transactions is considered the main fintech tool [91–95]. The blockchain also reduces financial comfort and cost efficiency, as Šapkauskiene [ ˙ 92] studied initial coin offerings of new companies and explained how blockchain reduces cost with ICOs. Machine learning, artificial intelligence, and big data themes are also common in the fintech literature. Significant machine learning literature is related to estimating the movement and pricing of financial assets [96–102]. Kulshrestha et al. [103] deploy technical, fundamental, and artificial intelligence to propose optimal portfolio performance. Big data is about considering various techniques to ensure the quality of the data and its estimations and prediction ability [104]. Fintech has a significant role in shaping the architecture of big data [105].

E-commerce is a highly developed theme; however, its centrality is very low. The growth of e-commerce is significant, and there is a lot to offer by deploying various fintechs such as payment applications, blockchain, bitcoins, price disruption, and channel modelling [106]. Certain issues related to e-commerce business proliferation include online payment models and B2C market supply chain management [107]. There are certain gaps

and challenges related to digital wallet payments [108]. Government regulations and understanding of the mobile payment system is another challenge for e-commerce development [109]. Furthermore, fintech contributions in e-commerce is studied by [110–114].

Crowdfunding, regulations, and technology are the motor theme, which indicates high centrality and high density. In contemporary times, raising capital from many people to fund innovative projects is an integral part of startup growth. Jin [115] studies the various patterns of crowdfunding and the various stages of the project. Various technologies and topologies of crowdfunding are studied by [116]. Zetzsche et al. [117] consider studying crowdfunding and propose harmonising or standardising the crowdfunding procedure. It is the stepping stone towards developing and changing businesses' technological environments and significantly achieving entrepreneurial goals [118]. Regtech represents regulation technology and is mainly associated with data protection [41], algorithmic regulations [112], decentralized blockchain [70,118,119], financial markets and risk management [119,120], artificial intelligence [121], anti-money laundering [29], and superior supply chain management [122].

#### **4. Future Research**

With the help of fintech literature, we can identify critical areas where further research can be pursued. The following are some recommendations for researchers, policymakers, academicians, and information technology people.

#### **5. Limitation of Study**

#### **6. Conclusions**

The study suggests that fintech is the future of business, economy, and information technology, and will help preserve the world's environment. The research indicates various influential and conceptual aspects of the fintech literature from the past decade. We have discovered some of the core factors of fintech literature. It is worth considering that the development of fintech is contemporary, and research has grown substantially in recent years. Among influential aspects, it is observed that the ACM International Conference Proceedings is the core source of fintech publications. It is the source with the highest impact in fintech literature is Electronic commerce research and applications. D.W. Arner is the most prolific author in fintech publications, while P. Gomber can capture the highest number of citations in the field of fintech. With 33 publications, Bina Nusantara University is the top affiliation. Among top countries, China, the USA, and the UK scored in the top three in terms of publications; however, the USA scores total citations.

Overall, China is the core country, ranked first with the highest correspondence in multiple and single country publications. We have addressed conceptual aspects of fintech literature with a co-occurrence network and thematic map. We have shortlisted three main research streams of fintech literature to narrow down. The first research stream is 'cryptocurrency, smart contract, and financial technology.' The second research stream is 'financial industry stability, service, innovation, and regulatory technology (regtech).' The third research stream, which divides the overall fintech literature, is 'machine learning, deep learning, and artificial intelligence.' These research streams have narrowed down the vast literature of fintech into parts to understand mechanics. We further conceptually differentiate the fintech literature in terms of emerging, basic, isolated, and motor themes. We found that blockchain, cryptocurrency, financial inclusions, machine learning, artificial intelligence, and big data-related topics have high centrality and low density in the fintech literature. It may be difficult to find gaps in said topics. E-commerce is a highly anticipated topic as it is isolated with low centrality and high density. The digital economy, internet of things, and financial industry in fintech are emerging and dropping themes. Crowdfunding and regtech are motor themes with high centrality and high importance.

**Author Contributions:** Conceptualization, A.N. and K.S.; methodology, A.N., K.S., K.I.K., I.A.H. and T.M.A.; software, A.N., K.S., K.I.K., I.A.H. and T.M.A.; validation, A.N. and K.S.; formal analysis, A.N., K.S., K.I.K., I.A.H., T.M.A. and S.L.; investigation, A.N., K.S., K.I.K., I.A.H., T.M.A. and S.L.; resources, I.A.H. and S.L.; data curation, A.N., K.S., K.I.K., I.A.H., T.M.A. and S.L.; writing—original draft preparation, A.N. and K.S.; writing—review and editing, A.N., K.S., K.I.K., I.A.H., T.M.A. and S.L.; visualization, A.N., K.S., K.I.K., I.A.H., T.M.A. and S.L.; supervision, I.A.H. and S.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

## MDPI

St. Alban-Anlage 66 4052 Basel Switzerland Tel. +41 61 683 77 34 Fax +41 61 302 89 18 www.mdpi.com

*Applied Sciences* Editorial Office E-mail<EMAIL_ADDRESS>www.mdpi.com/journal/applsci

MDPI St. Alban-Anlage 66 4052 Basel Switzerland

Tel: +41 61 683 77 34

www.mdpi.com

ISBN 978-3-0365-5115-9