Literature Review

The application and challenge of digital technology in the banking industry

2.0 Introduction

This section will discuss existing literature related to the research topic “The application and challenge of digital technology in the banking industry”. The literature used covers research directions such as the applications of digital technology and its impact on the banking sector. These empirical evidences would provide strong support for my research. This section will also explore the theoretical framework and conceptual model of banking digital transformation. At the end of the section, the main findings during the study are summarized briefly.

 

2.1 Empirical review

2.1.1 theme 1 Specific applications of digital transformation in banking

The banking industry has been rapidly adopting digital transformation initiatives to enhance operational efficiency, improve customer experience, and stay competitive in the digital age (Baskerville et al., 2020). Empirical research in this area has identified several specific applications of digital transformation within the banking sector.

 

2.1.1.1 sub-theme 1 Online banking

Online banking technology, also known as internet banking, has revolutionized the banking industry by offering customers convenient and efficient ways to access financial services. Empirical research in this area has extensively examined the adoption, usage reasons, and impacts of online banking technology on both customers and banks. Bradley & Stewart (2003) looks at the adoption of online banking in the retail banking sector and explores the factors driving banks to adopt online banking. An international study by Delphi confirms the high importance of road connectivity for retail banking, with key factors driving the adoption of online banking including adoption by other banks, competitive forces, consumer demand and availability of technology. Moreover, Ahmed et al. (2020) explores how electronic banking service quality affects customer satisfaction using the ES-QUAL model. It investigates mediating factors like customer trust and perceived value. Conducted with 910 account holders from five major commercial banks in Pakistan, the study confirms a direct positive impact of ES-QUAL dimensions on customer satisfaction. It also finds that trust and perceived value mediate this relationship. Financial analysis of the banks' secondary data supports these conclusions, emphasizing the importance of online banking service quality for customer satisfaction and business performance.

 

2.1.1.2 sub-theme 2 Blockchain and banking industry

Blockchain technology is becoming increasingly prominent in the banking industry because of its potential to improve the security, efficiency and transparency of financial transactions. By providing a decentralized and immutable ledger system, blockchain eliminates the need for intermediaries and reduces the risk of fraud. In banking, blockchain is being used for a variety of purposes, including payment processing, trade finance, identity verification, KYC compliance, and asset tokenization. Its adoption is expected to streamline processes, reduce costs, and foster innovation within the industry. According the research of Guo & Liang (2016), blockchain technology is the core underlying technology of banking industry and has broad application prospects. Blockchain can revolutionize the underlying technology of bank payment clearing and credit information systems, thus upgrading and transforming them. Blockchain applications also promote the formation of "multi-center, weak intermediary" scenarios, thereby improving the efficiency of the banking industry.

 

2.1.1.3 sub-theme 3 Artificial intelligence and banking industry

Artificial intelligence (AI) has emerged as a transformative technology within the banking industry, offering numerous opportunities for efficiency improvement, enhanced customer experience, and risk management. With AI’s ability to analyze vast amounts of data, learn from patterns, and make predictions, banks can streamline processes, personalize services, and detect fraudulent activities more effectively. Rashmi & Nirmal (2021) uses a 34-variable questionnaire covering different banking processes, interviews with bank employees and customers, the analysis reveals positive attitudes towards the adoption of AI in banking. They used Cronbach’s alpha, Kaiser-Meyer-Olkin (KMO) and Bartlett's tests to reach conclusions of high reliability and sampling adequacy, respectively. Based on the regression model, the importance of variables such as customer satisfaction, error reduction, risk management and automated compliance is highlighted, showing their impact on bank performance. By contrast, however, understanding customer behavior has little impact. Their study highlights the potential of AI to enhance banking processes and customer experiences, illustrating that specific variables play an important role in driving positive outcomes.

 

2.1.1.4 sub-theme 4 Big data analytic and banking industry

The banking industry is working to analyze this big data by breaking the raw data into several parts so that these departments can facilitate customers by analyzing each part efficiently. Big data analytics can help the banking industry in everything from capital flows to threats and disasters. Today, online banking is one of the universal services offered to all customers. As online banking facilities lead to an increasing number of electronic transactions, so does the risk of fraud, so big data analytics can also help identify fraud detection. Every bank generates critical data from its customers that needs to be efficiently stored and analyzed using big data analytics methods to gain the necessary insights for the banking organization. Today, the banking industry is focusing on analyzing big data to achieve marketing goals. Gupta et al. (2019) states techniques for analyzing big data in banking: cluster analysis, data mining, genetic algorithms, machine learning, and principal component analysis. What’s more, he provides the ways in which big data analytics are useful: fraud detection, application screening, customer acquisition & retention, knowing customer buying habits, cross-selling, collections, better cash/liquidity planning, marketing optimization, and customer lifetime value. Big data analytics plays a vital role in digital banking transformation.

 

2.1.2 theme 2 challenges of digital transformation for banks

The development of financial technology has changed consumer behavior, and digital transformation has become the only way for the banking industry to enter the future. How to understand and adapt to the changes brought about by digital technology is a huge challenge. Wewege et al. (2020) points out that fintech banks generally lack scale and trust, and in some cases are unregulated, carrying credit or liquidity risks. In addition, the existing banking system suffers from difficulties in integrating emerging digital technologies. Together they stymie the development of banking.

 

2.1.2.1 sub-theme 5 Cybersecurity threats

Digital banks use digital platforms to do business. Much of the risk comes from the criminal activities of fraudsters and hackers who aim to steal people's money and information. These problems are collectively referred to as network security threats. Alzoubi et al. (2022) use a secondary research method to draw conclusions by analyzing facts provided by secondary sources. The authors point to five types of cybersecurity issues that could have disastrous consequences for online banking services: unencrypted data, malware, unreliable third-party services, data manipulation and spoofing. The occurrence of cybercrime proves the great threat to digital banks, and many cases around the world provide evidence of the seriousness of cybersecurity threats.

 

2.1.2.2 sub-theme 6 regulatory compliance problem

As an extension of the traditional banking industry, digital banking has also inherited its strict regulatory guidelines. Whether emerging digital technologies are used correctly, whether compliance costs and efficiency are balanced, whether the risk management system built by the supervision is perfect, and whether internal supervision is tight, many hidden dangers need to be solved. Since the 2008 financial crisis, the focus on institutional client behavior and regulatory compliance performance has come to the fore as never before. What’s more, failure to comply can result in litigation, financial penalties, regulatory restrictions, and reputational damage that can have a strategic impact on the organization. In the banking industry, regulatory compliance is a very broad and complex process involving many people at multiple levels within the enterprise. Due to poor governance of the management process, this extensive involvement brings many time-consuming and money-consuming bottlenecks that are difficult to monitor and identify: individual employees in the team work independently on their respective tasks and communicate via email, which makes it difficult to monitor the status of the team. In addition, with multiple entities and institutions constantly introducing new regulations, banks need to be up to date in the shortest possible time and in the most accurate way (Casarico, 2021).

 

2.1.2.3 sub-theme 7 integration of existing systems with emerging digital technologies

The integration of existing systems with emerging digital technologies is a challenge that cannot be bypassing in the process of digital transformation of banks. Technical differences are the root cause of integration difficulties. Different system architectures and data formats mean that banks need to overturn existing systems and rebuild them to accommodate new technologies. Technology integration entails significant costs and is a challenge for digital banking to change. Acar (2019) aims to reveal the process of fintech integration in the banking sector. By referring to the business experience of Kuveyt Turk Participation Bank, the author lays out seven stages of the integration process.

 

2.1.3 theme 3 The strategies adopted by banks to meet the challenges

Measures banks should take to address cybersecurity threats, regulatory compliance and technology integration difficulties in the digital transformation process of banks mentioned above (2.1.2 theme 2).

 

2.1.3.1 sub-theme 8 Biometrics protect against cyber security threats

The key to effectively prevent network security threats is to identify intruders. We can't know who is stealing our data and property if we don't know who the intruder is. Ghelani et al. (2022) consider machine learning, biometrics, data learning and hybrid methods to come up with a banking system model that uses biometrics and digital signatures to support every transaction a bank customer wants. To reduce the number of possible threats posed by intruders.

 

2.1.3.2 sub-theme 9 use RegTech to solve regulatory compliance problem

Regulating technology is an effective response to regulatory compliance challenges. Regulatory technology is a tool that utilizes advanced technology to enhance financial regulatory compliance. Butler & O’Brien (2019) present research on the Bank of England/Financial Conduct Authority, FCA's RegTech Sprint program, demonstrating how RegTech enables pass-through processing of regulations and regulatory compliance reporting using semantic applications. At the same time, he points out that the perfect implementation of RegTech needs to avoid the pitfalls of a fragmented Tower of Babel approach, and semantic standards are the key to achieving this goal.

 

2.1.3.3 sub-theme 10 creating seamless banking experience

The primary goal of technology integration is to adapt the system structure of traditional banks to the current level of consumer demand. Using literature review and qualitative research methods, the authors propose measures to integrate offline and online platforms to achieve a seamless banking experience. It does not completely get rid of the traditional banking model, nor does it stick to the existing banking structure, and is committed to putting consumer rights and interests first.

 

2.1.4 Digital banking ecosystem development

The digital banking ecosystem is a comprehensive network and ecosystem composed of digital technologies in various financial service providers. It has the characteristics of diversity, integration and innovation to provide consumers with a better experience and service. Evdokimova (2021) reveals the main areas that banks consider when building their own ecosystems and analyze the financial ecosystems of countries such as the Russian Federation. Moreover, Pérez & Serrano (2016) elaborate on the prospects for the future development of digital ecosystems, which will bring about a new political and social order. Therefore, we should observe and analyze the Evolving Ecosystem Dynamics.

 

2.2  Theoretical framework

The theoretical framework of this study draws on key concepts and theories to understand the applications and challenges of digital technologies in banking. It includes digital transformation theory, innovation theory, technology adoption theory, regulatory compliance framework, network security theory and ecosystem theory. These theories provide insights into how digital technologies are reshaping banking processes, driving innovation, influencing technology adoption, ensuring regulatory compliance, mitigating cybersecurity risks, and shaping the digital banking ecosystem. Together, they guide the exploration of specific themes and sub-themes identified in the literature review.

 

2.3  Conceptual framework

The conceptual framework will demonstrate the relationship between digital banking technology applications and challenges, and how these factors affect the transformation and development of digital banking. Let me start with four specific applications of digital technology and compare their impact on bank operations. Next, I present three specific challenges to demonstrate the obstacles facing the digital transformation of banks, and then make targeted suggestions to promote the development of digital banks. Finally, I will give a vision of the future of digital banking, that is, the development path of the digital banking ecosystem.

 

2.4  Summary

The banking industry uses technologies such as online banking, blockchain, artificial intelligence and big data analytics to improve efficiency and reduce costs, but also faces challenges in cybersecurity, regulatory compliance and technology integration. To solve these problems, banks have adopted measures such as biometrics, regulatory technology, and strive to achieve seamless integration of online and offline platforms. The development of the digital banking ecosystem has also become a key direction of future development, which will bring about a radical change in the banking business model.