FinTech Revolutionizing the Banking Domain

Javeria Ali^1,2 and Abdus Sami³
1. Department of Electronics Engineering, NED University of Engineering and Technology Karachi, Pakistan
2. Department of Computer Science and Information Technology, NED University of Engineering and Technology Karachi, Pakistan
3. Department of Computer and Information System Engineering, NED University of Engineering and Technology Karachi, Pakistan
Correspondence to: Javeria Ali, Javeria.ned.27@gmail.com

DOI: https://doi.org/10.70389/PJBM.100008

Additional information

• Ethical approval: N/a
• Consent: N/a
• Funding: No industry funding
• Conflicts of interest: N/a
• Author contribution: Javeria Ali and  Abdus Sami  – Conceptualization, Writing – original draft, review and editing
• Guarantor: Javeria Ali
• Provenance and peer-review:
  Commissioned and externally peer-reviewed
• Data availability statement: N/a

Keywords: Fintech, Innovation, Technology, Finance, Banking.

Peer Review
Received: 15 August 2024
Revised: 21 October 2024
Accepted: 24 October 2024
Published: 11 November 2024

Introduction

With the development of the digital world, electronic upgrades and transformations in a variety of sectors have taken place. Information management technology, e-government, e-health care, the revolution of chatbots and artificial intelligence technology, the Internet of Things, cloud computing, and others, were due in the financial sector to experience the flavor of inclusion of technology in the financial domain.^1–5 Fintech has impacted financial aggregators, financial institutions, and the banking sector immensely. Similar to other industries being affected by technology, the economic aspect of an individual and large businesses are impacted too. Initially, technology in finance was not welcomed, and many financial institutions kept themselves away from the amalgamation of technology in finance. However, due to rapid developments and advancements in the banking sector and the increase in the number of modern-day technology users, financial businesses have begun to adapt to the new change. Fintech has challenged the way the ­financial sector runs its day-to-day processes. Also, the inclusion of automation has further influenced the ­financial decision-makers to shift conventional financial processes toward digital operations. Fintech works as an interface between the financial domain and technology with innovation.^6–12

Fintech has brought novelty in several financial domains. These include payment channels comprising of person-to-person payment, electronic fund transfer, and interbank fund transfer, banking channels that include automated teller machines (ATM), Point of Sale (PoS), and merchant terminals; different sorts of financial services such as loans, interests, insurance, stock, and pensions; payment gateway as a financial entity; security including the Payment Card Industry Data Security Standard, encryption, decryption, tokenization, and fraud detection; various authentication techniques such as username, password, single sign-on, secret question, digital, and cryptocurrencies, for example, digital wallets and bitcoin; and reporting and alert management systems along with the inclusion of artificial intelligence, cloud computing, Internet of Things, personalized chatbots, and most importantly banking application programming interfaces. Moreover, the world has seen a sudden rush of Fintech startups and some of them are doing extremely well. These Fintech startups are giving tough time to the conventional banking system considering the feasibility and convenience they provide to the banking process using the technology.^12–15

The review focuses on analyzing three aspects of the inclusion of technology in the banking domain. First, how the technology is penetrating the banking sector so conveniently and getting largely accepted; second, the benefits Fintech and the banking sector are garnering because of the amalgamation; and third, the benefits and risks arising due to the association. The inclusion of technology in financial institutions is not in a mature state and is continuously evolving. Hence, many banking sectors have not completely implemented Fintech, and some are in the transforming stages. For that matter, this review article summarizes the analysis retrieved from the diverse range of Fintech research papers. It brings novelty and worth to the repository of academic searches by introducing an automatic keyword identification process, realizing how Fintech has become a major part of academic research, artificial intelligence, and blockchain methodology implemented for revolutionizing Fintech in banking. The review paper enhances the learning opportunity regarding how relevant data extraction methods have optimized the process for specific content extractions.

Methodology

This review paper utilizes Reporting standards for Systematic Evidence Syntheses (ROSES) for the highlighted Fintech domain points to be investigated.16 The reason for choosing ROSES is that it is a widely accepted reporting standard and is utilized globally for the authenticity of the review articles. ROSES incumbents are the finest way to convey systematic literature review (SLR) process flow. Implementing SLR ensures a clear and guided way to conclude a systematic review process. ROSES has a pre-defined methodological framework that is capable of fitting the diverse form of data for the relevant review topic. The multi-layered data then converges to narrow down the most suitable data. The data then provide a knowledge base for future research.

Application of SLR

The SLR process steps are implemented as follows by accommodating the Fintech revolution in the banking domain. Though SLR has multiple types of process steps, the defined process steps are most suited to the Fintech in the banking research area.

Determining Research Questions

Based on the targeted review topic, we have utilized the Population, Intervention, Comparison, Outcome, Context (PICOC) structure pattern, resulting in the formulation of two research questions.

• Research Question 1: How is Fintech easily getting accepted in banking?
• Research Question 2: What are the risks and benefits of Fintech in banking?

Research Resource

Mendeley Reference Management serves as a tool for article management and reference management. For years, it has also been used by researchers and academicians to extract data from research papers. It has a fine mechanism for desired filtration results. Therefore, we decided to use Mendeley for viewing Fintech-based publications. Mendeley was developed by PhD scholars, but now it is acquired by Elsevier, which is among the top-notch publication companies in the world.
Figure 1 visualize the research database resource.

Target Search Items

We have used two keyword phrases:1 Fintech in banking,2 risk and benefits of banking in Fintech. Due to target keywords, we get the filtered and narrowed results for Fintech publications in the banking domain. Our review heavily relied on the outcome of the keywords. Moreover, the famous publication houses were given priority in terms of the literature these journal articles have. Also, the journals with higher impact factors are given importance. For the Fintech in banking keyword, we get 4,602 results, and for the risk and benefits of banking in ­Fintech, we get 118 results. Both the results generated apply no filter. These search results comprise all conference papers, proceedings, journal articles, abstracts, and citations.

Next, to screen the relevant articles, we limited our systematic review of the research conducted in the year 2024. The reason is that Fintech is evolving so rapidly that it would not be wrong to say that each day we have something new in this domain. Therefore, we included the current year’s research only. This reduces the number of results for the keyword “Fintech in banking” to 168 and for “risk and benefits for banking in Fintech” to 13. Further, we included journal articles only, and this reduces the result to 9 for “risk and benefits for banking in Fintech” and 122 for Fintech in banking. Since it was nearly impossible to conduct the review for this number of articles, we used the advanced search option “open access only.” This reduced the number of articles for “Fintech in banking” to 52, while for “risk and benefits for banking in Fintech,” the filtered result was reduced to 3. The details are summarized in Table 1.

Inclusion and Exclusion Criteria

Out of 55 articles, we manually filtered those journal articles that were read by more than 10 readers. This excluded 29 articles, and we were left with 26 articles. Twenty-six articles were analyzed manually by looking at the article title, abstract and methodology, discussion, and result sections to extract the most suitable and relevant details for the review paper. We limit our explorations to the research papers only. The details are summarized in Table 2.

Process Flow

The SLA process flow is summarized in the diagram as in Figure 2.

Result and Discussions

In this paper, we have extracted the research from eminent authors’ research papers and summarized them using the automated keyword extraction method that we have introduced in this paper. By using an automated keyword identification procedure, we came to know the revolutions that Fintech has created in the banking sector. Utilizing the research of renowned scientists, we have summarized them in a way that will provide a clear picture of what has already been done and the changing dynamics of the banking industry with Fintech. Both the physical and digital worlds are largely affected by Fintech since physical and digital worlds are connected through processes and procedures in the advanced technology era. This section of the review paper discusses the outcome of analyzing the research papers relevant to Fintech’s revolutionizing of the banking industry. The literature extracted has been sorted to make it a useful summary for authors and readers for future research. The section includes.

Data Analysis

We then performed the thematic analysis for these 23 research publications. We do this by identifying similar patterns of texts related to Fintech, in particular to the banking sector, then marking the same codes for similar text patterns, and then categorizing the multiple codes into themes. Next, we compare our themes with the initial pattern of texts we extracted to confirm any misinterpretation of information or identify gaps in data. Finally, we formally name the theme to resume further filtration processes for targeted scope articles. The thematic analysis has been visualized in the process flow chart as in Figure 3.

By limiting our focus area to research articles only, we were left with 18 research papers altogether, i.e., the eight review papers were excluded. Since the coronavirus cases have now been reduced and the world now knows how to tackle coronavirus. Also, there is enough literature on this topic. Therefore, the research papers with literature relating COVID associated with Fintech were filtered out. The remaining research to be reviewed for analysis was 14 research articles. The article filtration process flow utilized from start to end has been visualized as in Figure 4.

Automate Keywords Identification Process

Since it was difficult to review every paper in detail, we picked the most used keywords and then did the analysis. We made use of advanced coding techniques to automate the keyword identification process. By using Python, with the VS code, we automated the keyword identification for every research paper. The code not only tells the repeated keywords but also identifies the frequency of each keyword.

Process Flow Details

We have utilized Visual Studio Code in combination with Python programming language to build the automated keyword identification process. The operation starts with installing both Python libraries and Visual Studio Code application on the machine. After successful installation, initiate VS Code. The next step is to download Python file extensions that are open source, which means they are freely available. By having the Python file extension, the programming experience collectively gets improved. This also means that line-by-line code debugging gets a lot easier. After establishing the Visual Studio Code environment, the next is to make a new task by creating a folder where we desire to keep our project files. This is where we create our main Python file with the extension .py, for example, the file name as main.py. This is the basic file where our Python code is written. Using VS Code, the development procedure is executed. Few Python packages are utilized for automated keyword processes to optimize the code, namely maths, pandas, numpy, matplotlib, and datetime.

Automating Keyword Identification

We limited our automated keyword selection strategy to the 30 most used keywords for every research paper. This becomes the first part of the automatic keyword identification process. Here we ignored helping verbs, pronouns, reference section words, other words that do not produce definite sense, and names. Further to make the graphical representation easy to view, we have presented here the 10–15 most used keywords for every research paper. The research paper pdfs are named after the first author. Two or three research papers are clustered in one graph, depending upon the similarities they have in common. Hence, we get six clusters for 14 research papers. Using the outcome from the thematic analysis, we have clustered the research papers. Below each visualization, important outcomes from the respective papers are discussed in detail. We have used here thematic research clustering methodology for graphical in Figure 5.

Neobanks are the modern approach to dealing with branchless banking. But it has aggravated security risks as well. Though the digital mode of transactions surely brings opportunities, the risks they carry can affect the core banking system. The network attacks have created turmoil in the corporate industry. It gets stimulated when businesses are hit hard in the financial domains. Banks and financial institutions are always in the limelight for cyber attackers. Therefore, the flexibility that neobanks carry for their customers must be accompanied by security aspects. The authors have presented a parametric model to estimate risk factors dependent on certain risk-based components. For that matter, they divide the research methodology into three stages. In the first stage, they will find the neobank’s dynamics from a development perspective; in the second stage, they will focus on the risk factors when dealing with the neobank; and in the third stage, they perform the SWOT analysis with a focus area on the opportunity rather than the threat. The result was that if Neobank prioritized security and inclusion of artificial intelligence, they could save themselves from the risk of cyberattacks.¹⁷

With innovation and developments, the risks are increasing. On one hand, if the banks are taking up financial technology, then on the other hand, they need to find ways to prevent risks. Financial risks are considered critical, and early fraud prevention and fraud detection are the need of the hour to prevent cyberattacks. The authors use their theoretical model with China’s bank data and find out that stabilizing bank leverage structure is necessary since both high and low leverage structures in banks can have a reverse impact. The second countercyclical regulation effect helps in reducing risk elements for the banking industry^.18 To automatically generate collaborative-level agreement, the author utilized a digital financial technology approach. Positive and worthy collaborations have always benefited the business process. But some parameters are to be taken care of. Some of the most important parameters to run collaborative business-to-business processes include trust and security. Both cannot be addressed without having any formal documentation. The blockchain technology is used for this purpose. The fraud detection mechanism is also part of the research methodology.¹⁹ Details are summarized in Figure 6.

Banks are in the middle of deciding whether to follow the traditional banking process management or acquire Fintech. They weigh the situations based on certain conditions. The conditions could be gender- dependent, i.e., either more female employees or male employees, female CEO or male CEO, CEO tenure dependent, CEO age dependent, either younger or older, the bank’s approach to build processes internally or acquire the ready-to-go systems, or the financial stability of the bank. Using manually gathered datasets, the author developed an analysis for each factor either affecting positively or negatively an individual bank’s approach to acquire Fintech for its business.²⁰ Banks are inclined towards accepting Fintech innovation and its implementation. Two main factors are the bank management and the nearest Fintech available to the bank’s physical location, especially near the bank headquarters. The home bias variable represents the mean distance between the bank and Fintech. Three characteristics were found that make the bank implement Fintech. These include more women employees; the second one is the younger board of directors; and the third one is corporate networking. The location and the three characteristics mentioned played a vital role in Fintech’s inclusion in the banking sector.²¹ The data is visualized in Figure 7.

Income inequality is one of the most prominent issues. Even in the developed countries, the problem persists. Sustainable development goals were established by the United Nations to reduce the income disparity among people and even countries. Research on SDG 10 was carried out to analyze the impact of sustainable practices in the banking sector when law-enforcing agencies are not strong enough. It was concluded that application SDG-10 has a positive impact on the banking sector in lethargic lawful circumstances.²² ESG which is Economic, Social, and Governance performance was compared with the four parameters, namely board size and its members, fintech development, and gender diversity. Each parameter was compared with ESG performance using the Fintech index methodology made from 10 keywords. Finally, it was concluded that except for board size, all three parameters have a significant impact on ESG performance.²³ See below Figure 8 for elaboration.

Fintech applications in the Islamic banking system are prominent, especially in developing countries like Jordan. The research was carried out on the real Jordan Bank data from the year 2017 to 2021. The authors utilized quantitative descriptive survey methodology to find out how Fintech is affecting Islamic banking in particular in Jordan. The targeted Fintech services in the research are ATM, crowdfunding, mobile banking, and internet banking. Regression analysis is used as a data analysis method, and ROE and ROA measures are used as the Islamic banking performance analyzer. It was concluded that Fintech indeed is creating an impact on Islamic banking, especially it is true for the Islamic Bank of Jordan.²⁴ Considering a two-stage model, the study investigated how financial technology is impacting the mobile coverage domain. By utilizing a dataset of more than 12,000 mobile phone users from African countries for the year 2017. In the first part, individuals decide to use mobile phone services, and in the second part, they decide between taking up or leaving Fintech services. The estimated result showed a positive response when including financial digital services for mobile network coverage. Prominently, UMTS and GSM depicted greater influence than LTE towers.²⁵ The data is analyzed in Figure 9.

Back then, people were bound to take loans from banks when in need. Today, with the concept of peer-to-peer lending, a person has a provision to take a loan from another person by using digital methods like websites or mobile banking. Hence, it is visible that peer-to-peer lending is in direct competition with traditional banking loans that require extensive documentation and take several days to process. The research provides an empirical analysis of the liquidity levels in different types of SMEs, i.e., small-sized enterprises, medium-sized enterprises, and non-MSMEs. This investigation showed the effect of peer-to-peer lending on non-micro, small, and medium enterprises banking credit. The analysis is conducted province vise. It is concluded that peer-to-peer lending positively influences non-micro, small, and medium enterprises credit banking in regions where liquidity is more relaxed.²⁶

Fintech lending has revolutionized the way to provide loans using the enhanced digital process and keeping the traditional banking lending procedures at bay. Previously, for loan purposes, people visited bank branches physically, stood in long queues, went through extensive processes and documentation. With the inclusion of Fintech in lending, the process is digitally streamlined, and artificial intelligence technologies and blockchain methods are the cherry on top for Fintech in banking. Multiple lending types can be handled, including peer-to-peer, business-to-business lending, and merchant and POS lending using financial technology. Regulatory framework and data privacy are the most prominent risks associated with process flow.²⁷ A comparative study was carried out between conventional banking and Fintech firms in the lending domain. Using the data from Italian banks, it was found that lending practices are more efficient in traditional banks for the year 2021 in Italy. This may be due to the trust levels associated with the traditional banking system, and banks and people in Italy are finding it difficult to accept a new technological framework from financial processes.²⁸ See below for  Figure 10.

Even today, when we have service robots almost everywhere, banks still prefer human financial advisors to accommodate their customers, even for basic and repetitive tasks. However, if not for the sensitive data, the basic information counter advisor could be replaced by a financial robotic mechanism. The authors discussed the process for enforcing service robots in the financial sector. Not the complete financial system, but some modules could be programmed with human interference in the final approval steps. A fragmented knowledge base related to robo-advisors is put together under a single article.²⁹ AI inclusion in the banking sector, especially for insurance, has shown its presence in more than 39,000 articles from the Scopus database. Using the PRISMA protocol and defined criteria, narrow down the count of research articles to a thousand. Ngram and cooccurence analysis was used to find the target keyword clusters. The authors conclude that AI and ML applications in the BFSI sector have much academic weightage. Artificial intelligence has a major contribution to anti-money laundering programs.³⁰ In the second part, to be more specific, we extend our code details such that out of all 15 research papers, the 14 most commonly used keywords were identified using the extended automatic identification code. The Figure 11 presents the final automated visualization.

Limitations of Study

The review too carries limitations. A few ideas that have been debated in several studies related to Fintech have not been demonstrated. For example, the government-level challenges associated with implementing Fintech in banking and the customer-side perspective on whether they completely want to switch to Fintech and leave the traditional banking approach. The ­reason for not discussing them is that they are not dominant themes with relevance to our review domain. Yet, these have their share of creating impacts. Another limitation is the database. Since we keep our review in Mendeley, expanding database research could provide greater insights for Fintech revolutionizing the banking domain. Other limitations of this review include the number of research papers included in the research, which is the sample size. There is a possibility that if we increase the inclusion of research papers in this study, the output will be different.

Conclusion

The goal of this review article is to investigate the revolution that Fintech is creating by innovating the banking industry and integrating knowledge regarding Fintech using the automated keyword identification process. Using the ROSES systematic review process, the articles were filtered, and finally, most related to our review, the 14 articles’ literature was discussed in detail. This review offsets the deficiency of automated keyword identification processes using Python and Visual Studio code that are relevant to the Fintech research domain. We contribute to the literature on Fintech by identifying how different Fintech authors are converging to similar points of interest. Also, with the input of a coding mechanism, we have included research comprising of AI inclusion in Fintech that further enhances the relevance of this review to the current literature. Using the VS code framework and Python code, we have ramified the keyword search automation process. This also helps future researchers in applying the process to other domains of research as well.

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Cite this article as:
Ali J and Sami Ab. Fintech Revolutionizing the Banking Domain. Premier Journal of Business and Management 2024;1:100008

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