The effect of financial technology development on commercial banks’ profitability experimental evidence in vietnamese commercial banks from 2008 to 2022

BANKING ACADEMY BANKING FACULTY GRADUATION THESIS THE EFFECT OF FINANCIAL TECHNOLOGY DEVELOPMENT ON COMMERCIAL BANKS’ PROFITABILITY: EXPERIMENTAL EVIDENCE IN VIETNAMESE COMMERCIAL BAN

INTRODUCTION

Rationale

Industry Revolution 4.0, initiated in 2011 by a high-tech project from the German government, aims to enhance the computerization of production This concept revolves around embedding smart manufacturing facilities and systems to facilitate digital convergence across industries, businesses, functions, and internal processes In 2016, Klaus Schwab, the Executive Chairman of the World Economic Forum, further emphasized the significance of this transformative movement.

The Fourth Industrial Revolution, as defined by the World Economic Forum, represents a transformative era where virtual and physical manufacturing systems collaborate flexibly on a global scale This digital revolution harnesses advanced technologies to enhance and streamline processes, optimizing production and business practices for humanity.

The rise of Industry 4.0 has given birth to Financial Technology (Fintech), which merges finance and technology to innovate financial services and products By leveraging technological advancements, Fintech enhances customer experiences and transforms traditional banking systems into digital platforms This evolution is evident in various banking offerings, including payment solutions, P2P lending, crowdfunding, wealth management, insurtech, Regtech, and the integration of cryptocurrencies and blockchain technology.

Fintech in Vietnam is experiencing rapid growth, emerging as a promising industry with a notable rise in the number of businesses involved (Bryan Carrol, 2022) In 2018, the country had 144 fintech companies, and the COVID-19 pandemic further accelerated this growth, leading to a significant increase in 2019.

2021 By the end of 2022, it is estimated that there were more than 260 companies participating in the fintech sector in Vietnam (according to Nextrans' Vietnam Startup

Since the establishment of the first Fintech companies in Vietnam in 2008, the sector has significantly transformed the Vietnamese banking system, influencing the strategies and operational methods of traditional financial service providers The digitization of banking has accelerated, particularly during the COVID-19 pandemic, as more banks collaborate with both domestic and international Fintech partners to enhance their services Notable partnerships include VietinBank with Opportunity Network (UK), VPBank with BE Group (Sweden), OCB with RippleNet (US), and TPBank with Backbase (Netherlands) This evolution raises important questions about how financial technology applications impact the profitability of commercial banks in Vietnam amidst the ongoing digital transformation driven by Industry 4.0.

Literature review

Banking plays a crucial role in a nation's economy, with the stability and profitability of banks closely tied to the overall economic environment Consequently, bank performance has garnered significant attention from economists and researchers, leading to numerous studies on factors influencing bank profitability Key factors include external elements from the macroeconomic landscape and internal dynamics within the banks themselves Additionally, in the context of the ongoing Industrial Revolution 4.0, which is driving digital transformation in the banking sector, Fintech-related indicators have become increasingly relevant in the analysis of bank profitability.

A study by Dinh Phan et al (2018) investigates the impact of Fintech development on bank performance in the rapidly growing Indonesian market Analyzing data from 41 banks alongside Fintech company metrics, the research highlights the significance of macroeconomic variables such as inflation (INF) and interest rate spreads (IIS), as well as key bank-specific factors including capital (CAP) and bank size.

Funding costs (FC) positively influence the profitability of Indonesian commercial banks, while other factors such as GDP, Loan Loss Provisions (LLP), Cost-to-Income ratio (CTI), and Digital Growth (DG), along with the primary variable of Fintech growth, negatively impact bank performance.

A study by Chi-Chuan Lee et al (2021) investigated the impact of the financial technology (fintech) industry on the performance of China's banking sector from 2003 to 2017 Utilizing fintech enterprise-level data, the research employed the SMETA method and revealed that fintech adoption enhanced banks' cost efficiency and profitability while also improving their access to technology Notably, the equity to asset ratio positively influenced bank profitability, whereas other examined variables yielded negative effects.

In 2021, Ahlem Chhaidar et al investigated the impact of fintech investments on the financial performance of banks, focusing on how bank size influences this relationship amid digital transformation Utilizing the FMOLS model, the study analyzed data from 23 European banks between 2010 and 2019 The findings revealed a significant positive correlation between fintech engagement and bank profitability, indicating that increased digital interaction leads to higher profitability Conversely, liquidity (LIQ), capital adequacy ratio (CAR), non-performing loans (NPL), and inflation (INF) negatively affected the return on assets (ROA).

Researchers in Vietnam are examining how the application of financial technology affects the profitability of commercial banks As part of the significant digital transformation driven by the 4th industrial revolution, Phan highlights the importance of this trend in the banking sector.

A study by Thi Hang Nga et al (2019) examined the impact of technological factors on the profitability of 21 Vietnamese commercial banks from 2008 to 2017 The researchers focused on return on equity (ROE) as the dependent variable and identified five key technology-related dummy variables representing technology applications in various banking areas Utilizing the Pooled Ordinary Least Squares (OLS) model and Fixed Effects Model (FEM), the study provided insights into the relationship between technology and bank profitability.

REM and GMM, the estimation results showed that the profitability of commercial banks was positively influenced by the following factors: use of technology in

The use of automatic payment technology via phones and computers is transforming business activities, driven by technological innovation Among the statistically significant explanatory variables, only inflation has a positive impact on banks' Return on Assets (ROA), while both Cost-to-Income Ratio (CIR) and Efficiency Ratio (ETA) negatively influence bank profitability.

Choosing a newer timeline - from 2011 to 2019, Nguyen Duc Trung et al

In 2022, a study examined the effects of technology spending on the efficiency and stability of 12 Vietnamese commercial banks The findings revealed a positive and significant impact of technology investment on the banks' net interest income (NIM) ratio However, no correlation was found between technology budget expenditures and banking stability The authors suggest that these insights are valuable for regulators and policymakers in the governance and administration of banks, as well as in shaping development policies.

A study by Dinh Thi Thu Hong et al (2021) examined the impact of Fintech companies on the performance of commercial banks in Vietnam, utilizing data from 31 Vietnamese joint-stock commercial banks and Fintech establishment data from 2006 to 2018 The research employed a robust theoretical framework and multivariable linear models to ensure reliable findings Results indicated that the rise of Fintech companies negatively affected key performance indicators such as ROA, ROE, and NIM, with correlation coefficients of 0.0046, 0.04, and 0.009, respectively Additionally, the study provided statistical evidence regarding the influence of bank characteristics and macroeconomic variables on the performance of Vietnamese commercial banks.

There is a clear relationship between financial technology (Fintech) and bank profitability; however, varying methods of measuring the Fintech index can lead to contrasting effects on profitability Understanding this relationship is crucial for stakeholders to formulate effective policies that enhance the benefits of technology while mitigating potential risks to the banking sector A systematic overview is provided in the table below.

Table 1: Research related to Fintech review

Research Author Independent variables Research results

POSITIVE EFFECT ON DEPENDENT VARIABLE

Does fintech innovation improve bank efficiency?

Chi-Chuan Lee et al (2021)

+ Fintech: Fintech enterprise-level data + ETA: The ratio between equity and total assets of commercial banks

+ NPL: non-performing loans ratio of commercial banks + LABOR: the number of employees at the bank

+ W1: Personnel expenses to total assets net of fixed assets + W2: Other operating expenses to fixed assets

+ W3: Total interest expenses to deposits and borrowed money + CR4: the four-bank concentration ratio

Fintech applications helped banks improve cost efficiency, thereby increasing profitability while improving banks' access to technology in China in the period 2003–

+ DIG: banks’ interaction with technology index (measured by the frequency of occurrence of digitized vocabulary in banks’ annual report) + SIZE: The logarithm of the commercial bank's total annual assets

Fintech had a positive and significant impact on the profitability of European banks between 2010 and

Bank liquidity (LIQ) measures the ratio of available liquid assets to liabilities, indicating a bank's ability to meet short-term obligations The solvency ratio (SOLV) assesses the proportion of equity to total assets, reflecting a bank's financial stability The non-performing loans ratio (NPL) signifies the percentage of loans that are in default or close to default, highlighting potential credit risk Lastly, the capital adequacy ratio (CAR) evaluates a bank's capital in relation to its risk-weighted assets, ensuring it can absorb losses and maintain solvency.

+ INF: Annual inflation rate + GDP: Annual GDP growth level of digital interaction of banks, the higher the profitability

Phan Thi Hang Nga et al

+ Technology-related variants + TE/TA: The ratio between equity and total assets of commercial banks

+ CIR: The ratio of operating expenses to total income of commercial banks

+ NPL/TL: Ratio of bad loans to total outstanding loans of commercial banks

+ SIZE: The logarithm of the commercial bank's total annual assets

+ COST: Total interest expense of commercial banks + GDP: Growth of Vietnam's annual GDP

+ INF: Vietnam's annual inflation rate

The profitability of Vietnamese commercial banks in the period 2008 –

2017 was positively influenced by the following factors: use of technology in business activities; use of automatic payment technology via phone and computer; technological innovation

The impact of technological development on Vietnam's banking operations

Nguyen Duc Trung et al

+ TECHINVEST: Logarithm the total annual expenditure of commercial banks on expenses related to software and technology

+ SIZE: The logarithm of the commercial bank's total annual assets

+ CAPITAL: The ratio between equity and total assets

+ LLR: Ratio of risk provision and total outstanding loan

+ EXPENSE: The ratio of operating expenses to total operating income before provisions

+ STATE: equal to 1 for state-owned commercial banks, and zero for the other

+ GRGDP: Growth of Vietnam's annual GDP

+ IFLR: Vietnam's annual inflation rate + GRSERVICE: Growth in annual net service fee collection by commercial banks

Technology investment positively and significantly affected the NIM ratio of 12 commercial banks in Vietnam between 2011 and

2019 However, there was no evidence of a relationship between spending on technology budgets and consumer stability

NEGATIVE EFFECT ON DEPENDENT VARIABLE

+ FINTECH: Number of financial technology (FinTech) companies founded

Using four models related to four dependent

+ SIZE: Log of total asset ($US million) + CAP: Capital ratio equals equity over total assets + CTI: Cost-to-income ratio equals total expenses over total generated revenues

+ LLP: Loan loss provisions equals loan loss provisions over total loans

+ DG: Annual growth of deposits + IIS: Interest income share equals total interest income over total income

+ FC: Funding cost equals interest expenses over average total deposits

Indonesia's annual GDP growth rate and inflation rate are influenced by key financial metrics such as Net Interest Margin (NIM), Return on Assets (ROA), Return on Equity (ROE), and Yield to Equity Average (YEA) Research indicates that the increasing number of FinTech companies has a negative and significant impact on these financial models Notably, state-owned banks are less adversely affected by the rise of FinTech compared to small-sized and privately-owned banks.

Fintech on operational efficiency of

Dinh Thi Thu Hong et al

+ Fintech: Natural logarithm of number of Fintech companies present in year t

+ SIZE: Log of total asset + LOAN: Outstanding Credit/Total asset + LLP: Allowance for Credit Losses/Total loans

The efficiency of Vietnamese commercial banks decreases when the number of Fintech companies increases and

+ EQUITY: Equity/Total asset + COST: Operating expenses/ Equity + DEPOSIT: Deposits and issuance of valuable papers/Total asset

+ INCDIV: Total non-interest income/Total asset + HHI: Herfindahl - Hirschman index The index is from 0 to 1, the closer the index is to a less competitive market

+ GGDP: GDP growth rate at constant prices + SFML: Ratio of short-term capital for medium and long-term loans the parameters of this relationship are all statistically significant

This shows that commercial banks in Vietnam are being competed by Fintech companies and in performance indicators, the increase of Fintech companies has a strong impact on ROE

Numerous global studies have explored the impact of Fintech on the profitability of commercial banks These studies employ various methodologies, focusing on key factors such as the total annual technology-related expenses of banks and the technology interaction index of each institution.

Research by Ahlem Chhaidar and others, as well as studies by Chi-Chuan Lee and Phan Thi Hang Nga, highlights the significance of technology applications in banking Despite employing various methodological approaches, these studies consistently demonstrate that technological variables positively influence bank profitability.

Aim of the study

The author sets out the following research tasks:

+ Firstly, summarize the theoretical framework of factors affecting Vietnamese commercial banks’ profitability

+ Second, study the practical impact of financial technology development on the profitability of Vietnamese commercial banks

+ Third, propose conclusions and practical recommendations to related parties

How does the development of financial technology affect the profitability of commercial banks?

Object and range of study

The effect of financial technology development on the profitability of commercial banks

The study was conducted on 27 Vietnamese commercial banks listed on the stock exchange between 2008 and 2022.

Research methodology

The research paper uses indicators related to the financial position of 27 commercial banks listed on the Vietnamese stock exchange, in particular:

Index Explanation Sources of data

ROA Return on asset Financial report

FC Number of fintech companies in Vietnam Statista

FV The value of total funding in Vietnamese

FI The level of public-interest for financial technology activities

GDP Economic growth World Bank Indicator

INF Inflation World Bank Indicator

CIR Cost income ratio Financial report

ETA Total equity to total asset Financial report

NPL Bad debt ratio Financial report

SIZE Bank size Financial report

CASA Current account savings account Financial report

MC Market Capitalization Financial report

This study employs regression models, including Pooled OLS, REM, and FEM, to analyze how the development of financial technology impacts bank profitability, specifically measured by Return on Assets (ROA) indicators, using secondary data.

Study structure

Chapter 3: Research and evaluation results

Theoretical framework for the effect of financial technology development

Financial technology development

The emergence of Industry 4.0 technologies, including the Internet of Things (IoT), Big Data, Artificial Intelligence (AI), Cloud Computing, Blockchain, and Biometric-Geometric systems, is revolutionizing socio-economic activities globally, marking a significant turning point in history.

In 2022, Phan Thi Hoang Yen and colleagues highlighted the extensive application of technology products in financial activities, enhancing both product value and operational processes This evolution led to the emergence of the term "Fintech."

Originally, the term "Fintech" referred exclusively to data processing and storage systems that support consumer networks within commercial financial institutions However, by the early 21st century, its definition broadened to encompass all technological innovations in the financial sector, including advancements in finance, education, retail banking, investment, and cryptocurrency.

In the wake of the 2008 global recession, Fintech has revolutionized customer experience by delivering affordable, user-friendly, and interactive digital financial products Today, Fintech is revitalizing the banking value chain through partnerships with traditional and Neo Banks, effectively bridging gaps in products and services while enhancing customer choices (MBBank, 2021).

According to a 2017 study by the Financial Stability Board (FSB), Fintech is characterized as the innovative use of technology in financial services, resulting in new business models, applications, processes, or products that significantly enhance the delivery of these services This definition underscores the critical role of technology in shaping the future of financial stability and service provision.

International Monetary Fund (IMF, 2018) in the panorama study when analyzing the

14 development of Fintech in the Central Asia - North Africa - Afghanistan - Caucasus regions, the Middle East and Pakistan

Fintech, a term that encompasses the integration of technology in financial services, aims to enhance the quality of products and services offered by financial institutions This trend is reshaping the management and operational strategies of banks and financial organizations, making it an essential aspect of modern finance.

Fintech, a blend of "finance" and "technology," refers to the integration of modern technology into the operations of financial institutions, enhancing their services and efficiency.

Figure 1.1: A brief story of Fintech

Source: " Fintech and Digital Banks" Report, MBBank (2021)

Research of Doulas D.A Janos N.B and Ross P.B (2016) divided the development process of Fintech into 3 main stages, which are closely linked to the industrial revolutions that have occurred in history

FinTech 1.0 (1866-1967): During this period, the development of technologies such as the telegraph, railway systems, and steam engines allowed international transactions and payments to be made quickly and conveniently between countries However, the arrival of credit cards for the first time in the US in

The introduction of Barclays' first ATM in the UK in 1967 marked a significant milestone in financial technology, signaling the transition from traditional analog financial services to the digital era, known as Fintech 1.0.

Fintech 2.0 (1967-2008): The characteristic of this period was the flourishing development of IT, which has pushed financial automation to a higher level

In 1971, the introduction of the NASDAQ trading system marked the beginning of automated stock trading in the United States, revolutionizing the fixed stock trading practices that had been in place since the 1600s This shift to electronic securities trading has continued to evolve to the present day Additionally, the banking industry saw its first online banking services launched in the US in 1980, followed by the UK in 1983, paving the way for modern digital banking solutions.

The emergence of the Internet in 1995 marked a significant breakthrough in Fintech, exemplified by Wells Fargo's introduction of online checking services By 2005, the first online banks without physical branches emerged, revolutionizing the banking landscape However, this evolution also brought increased complexities, limitations, and risks in computerized risk management systems, highlighted by events such as the "Black Monday" crash in the US stock market in 1987 and the Asian financial crisis of 1997-1998.

Fintech 3.0 (2008-present): In the 3.0 phase, Fintech stands out with the use of Blockchain technology without using the Web like previous 1.0 or 2.0 versions, the Blockchain system helps to carry out global value exchange transactions based on the Internet

The Global Financial Crisis of 2008 marked a pivotal shift in the Fintech 3.0 era, altering public perception of technology companies and their role in financial services As traditional banks struggled and public trust in their stability waned, consumers increasingly turned to non-listed companies, particularly in developing nations like China, for financial services These companies offered convenience and lower costs, leading to a significant rise in public trust in alternative financial providers, where reputation became less critical in consumer decision-making.

The rise of Fintech aligns with the advent of Industry 4.0, which began around 2010, merging physical, digital, and biological technologies to drive significant technological advancements This transformation is set to revolutionize the banking and finance sector, enhancing management models through the robust integration of AI and fundamentally altering traditional banking services and distribution channels.

The global Fintech sector is experiencing significant growth, with revenues soaring A Deloitte report indicates that by the end of 2021, the Fintech market was valued at over $150 trillion and is projected to reach $188 trillion by 2024, presenting a substantial opportunity for banks.

17 because if they take advantage of the modernity of financial technology, this will be a premise to help banks optimize their operations

Figure 1.2: Fintech industry size Figure 1.3: Fintech dominants in 2022

Source: FSI Insights on policy implementation No 23 (2020)

According to the FSB Financial Stability Board classification, Fintech activities are divided into 5 types of financial services, including:

Profitability of commercial banks

Commercial banks prioritize maximizing profits, leading to extensive research focused on "Profitability" as a key evaluation metric in their assessments.

Numerous international studies emphasize the significance of bank profitability Bobáková (2003) asserts that bank profitability is essential not just as an outcome of business operations but also as a crucial element for success in the highly competitive credit market.

Profitability is a crucial financial indicator for evaluating the financial performance of commercial banks, as highlighted by Nguyen Thi Thu Hien (2017) It reflects the combination of business results and available resources, serving as a foundation for banks to innovate and diversify their products for effective operations Similarly, Nguyen Thanh Phuong et al (2022) emphasize that profitability is essential for measuring the financial results of banks and is vital for their existence and development.

Bank profitability is assessed through specific indicators and formulas, lacking a uniform verbal definition However, there is a consensus that bank profitability is crucial for evaluating performance and serves as a fundamental criterion for guiding banks in optimizing their business activities As a result, banks can enhance the quality of their products and services, thereby increasing their competitiveness in the market.

Almajali et al (2012) emphasized the importance of using diverse measures to assess financial activity, highlighting that a comprehensive evaluation of a bank's performance requires considering multiple factors rather than relying on a single indicator.

In particular, the characteristic indicators to measure profitability are widely applied

25 in studies including return on average total assets (ROA), return on average equity (ROE), marginal net interest income (NIM)

First of all is the indicator of return on total assets (ROA)

Return on Assets (ROA) is a key financial metric that evaluates the relationship between a bank's net profit and its total average assets over a specific timeframe This indicator reveals the amount of after-tax profit generated per unit of assets utilized in business operations, highlighting the bank's efficiency in managing revenue and costs Ultimately, ROA reflects the institution's capability to convert its assets into net profit effectively (Halil Emre, 2012).

ROA = (Net Profit/ Total Assets) × 100%

Return on Assets (ROA) is a crucial performance metric used by market analysts to assess how effectively assets generate income It serves as the primary ratio for evaluating a bank's profitability, as it remains unaffected by a high equity ratio In contrast, Return on Equity (ROE) may downplay leverage risks by not accounting for liabilities Additionally, ROA was chosen as a dependent variable in a study conducted by Ahlem.

Next, the return on average equity (ROE)

Return on Equity (ROE) is a key financial metric that assesses the relationship between a bank's net profit and its average equity over a specified period This ratio reveals the amount of after-tax profit generated for each unit of equity invested in operations, highlighting the bank's profitability and efficiency in utilizing its capital to generate profits.

ROE = (Net Profit / Total Average Equity) × 100%

K Bojāre (2017) and Phan Thi Hang Nga et al (2019) chose to use the dependent variable ROE, because it represents the profitability of the bank in terms of invested capital, so this index will be suitable for the bank's shareholders In addition, based on the formula, ROE is assessed to vary by a greater amplitude than ROA and is therefore more sensitive to changes in explanatory variables (K Bojāre

In addition, NIM - net interest income margin is an indicator that also demonstrates the profitability of the bank

Marginal net interest income serves as a key profitability indicator for banks and financial institutions, reflecting the difference between interest earned and interest paid According to Nguyen Duc Trung et al (2021), the net interest income (NIM) ratio is crucial for assessing a bank's efficiency and stability NIM highlights a bank's capability to manage interest rate risk, which significantly impacts its overall profitability.

(Chaudron, 2018) The bank's NIM is calculated by the formula:

This indicator serves not only as a dependent variable to evaluate bank profitability but also as an explanatory variable that enhances the understanding of factors influencing Return on Assets (ROA), as demonstrated in James Gatauwa's 2020 research.

2.3 Factors affecting the bank profitability

2.3.1 Annual GDP growth rate (GDP)

An increase in GDP indicates a growing economy, leading individuals to either seek more loans for spending or deposit more money in banks This behavior allows banks to enhance their profitability and improve the quality of their assets (Anbar and Alper, 2012) Consequently, research by Phan Thi Hang Nga et al (2019) and Ahlem Chhaidar et al demonstrates a positive relationship between GDP growth and bank profitability.

Recent studies, including those by Chi-Chuan Lee et al (2021) and Nguyen Duc Trung et al (2022), indicate that the relationship between GDP and bank profitability is complex and can differ based on market conditions While some research suggests that changes in GDP significantly affect bank profits, other studies highlight that the stability of the State Bank of Vietnam's monetary policy limits bank borrowing, leading to minimal impact on profitability (Athanasoglou et al., 2006).

Inflation significantly influences bank profitability, as it often serves as a key indicator for banks to modify interest rates However, the correlation between inflation and bank profits has yielded inconsistent findings in prior research, including the study conducted by Phan Thi Hang Nga et al (2019).

Research by Nguyen Duc Trung et al (2022) indicates a direct correlation between inflation rates and bank operating efficiency in Vietnam, where stable inflation led to reduced credit demand In contrast, a study by Ahlem Chhaidar et al (2021) highlights that fluctuating inflation rates in Europe negatively impact bank profitability, as rising inflation outpaces bank revenue growth, ultimately reversing the relationship.

The effect of financial technology development on bank profitability

The financial services sector is significantly influenced by the ongoing "4.0 technology revolution," making the development of technology products crucial for its growth Banking is at the forefront of this transformation, actively adopting IT-based solutions In this fast-paced technological landscape, the interplay between Fintech and the banking industry is marked by mutual influence, presenting both opportunities and challenges.

As new entrants in the financial market, Fintech companies address gaps in service delivery that traditional banks have overlooked or inadequately managed (Do Thi Bich Hong, 2022) The impact of Fintech on banking and finance is evident through five significant advantages.

Fintech companies are accelerating the digital transformation of commercial banks by leveraging advanced technology and meeting diverse customer needs This competitive pressure compels banks to enhance their digital capabilities, attracting new clients and establishing a competitive edge over Fintech firms By embracing digital innovations, commercial banks can effectively navigate the evolving financial landscape and better serve their customers (Do Thi Bich Hong, 2022).

30 transformation process, this will be a premise to help these organizations reduce operating costs, thereby optimizing profits

Fintech companies play a crucial role in enhancing traditional banking services for commercial banks By leveraging new technologies, these firms enable banks to innovate and refine their product offerings in commercial and consumer lending, catering to the diverse needs of customers Additionally, fintech provides essential infrastructure that allows banks to adopt new business models characterized by high safety, improved accessibility, and dynamic marketing strategies through effective customer segmentation and screening.

In 2022, Nhat Minh highlighted that the integration of Fintech into commercial banking provides enhanced operational tools, enabling banks to optimize their business strategies and strengthen profit consolidation.

Commercial banks can leverage Fintech's advanced technologies, such as artificial intelligence (AI), machine learning (ML), and cloud computing, to enhance their financial services By adopting these innovations, banks can accelerate banking system reforms, expand their customer base, and improve customer care Collaborating with Fintech companies enables banks to diversify their product offerings and enhance service quality, ultimately creating a more personalized experience for their customers This partnership allows banks to develop a broader portfolio of digital products and services, effectively meeting the evolving needs of their clientele.

Fintech offers innovative financial solutions that cater to customers in remote areas or those facing challenges in accessing traditional financial services due to geographical or procedural obstacles By leveraging new technologies, transactions can now be conducted online, eliminating the need for customers to visit physical transaction counters for basic procedures This shift to online financial services not only enhances accessibility but also allows for a broader reach, delivering faster, more efficient, and cost-effective solutions to a larger customer base.

Commercial banks are partnering with Fintech companies to enhance transaction safety and reduce costs for customers By utilizing advanced technologies like Know Your Customer (KYC), banks can securely manage customer information while providing efficient services This collaboration enables banks to offer appealing promotions, attracting new clients and retaining existing ones Furthermore, the alliance opens avenues for co-investment in innovative business models and technologies, benefiting both sectors.

The rise of Fintech is significantly enhancing the operations of commercial banks, driving their digital transformation and innovation in products and services By optimizing operational processes, banks aim to minimize costs while improving customer experience Fintech also enables banks to expand their customer base and enhance geographical reach Leveraging technology allows commercial banks to reduce operating expenses and offer a diverse range of modern products tailored to the unique preferences of various customer segments, ultimately attracting new clients and boosting profitability year after year.

3.2 Fintech competes with commercial banks

In addition to the benefits, Fintech also puts commercial banks in great challenges Concrete:

The banking sector is increasingly losing ground to Fintech applications, which offer attractive preferential policies at transaction points This competition caters to users who prioritize convenience, savings, and rewards in their daily payments As a result, banks must adapt their strategies to retain customer engagement in payment activities; otherwise, they risk becoming mere intermediaries within the Fintech ecosystem.

Second, the risk of being attacked by technology Fintech products are created on technology platforms, so encountering the risk of attacks from technology

As information technology solutions become increasingly advanced, the likelihood of risks such as financial fraud, system errors, and data theft also rises Customers today are vulnerable to these potential threats, particularly since many applications require users to transfer funds before making payments Any issues within these systems can compromise customer balance data, ultimately jeopardizing their financial interests (Le Huyen Ngoc, 2017).

Third, Fintech is developing too fast compared to the current legal system

Fintech products emerge from ongoing technological innovation and creativity, often outpacing existing legal regulations This gap has contributed to a rise in Fintech-related scams, including capital contribution scams for virtual currency miners, ICO fraud, and cryptocurrency trading scams (Le Thi Khuong, 2020).

The rapid advancement of technology is leading to a significant reduction in the number of bank employees working at traditional transaction counters The rise of "paperless banks" and the integration of artificial intelligence and robotics are becoming increasingly prevalent Consequently, the size and number of bank branches and transaction offices are decreasing (Le Huyen Ngoc, 2017).

The emergence of Fintech companies has introduced credit risks for commercial banks, particularly through peer-to-peer lending services This technology proves most beneficial in venture lending, a sector often overlooked by traditional banks Many commercial banks have effectively leveraged peer-to-peer lending to offer quick loan solutions However, when investors choose to allocate funds via peer-to-peer lending and crowdfunding, the borrowers assume all associated risks.

To address current limitations, commercial banks must implement timely risk prevention strategies while actively developing specific plans to leverage Fintech advancements However, it is crucial for banks to avoid excessive dependence on Fintech, especially given its rapid growth and influence in the financial sector.

Assessing models

Research Process

To carry out this research, the author followed 6 main stages The particular content is shown below

Figure 2.1: Process of the study

Research methodology

In this study, the author utilized secondary data and employed descriptive and quantitative methods, including time regression models, Pooled OLS regression, REM, FEM, and GMM regression/estimation methods, to address and rectify any existing deficiencies.

The Pooled OLS regression model is a regression model that combines all observations The disadvantage of Pooled OLS is a tight binding between cross units, which is a difficult thing in practice

The Fixed Effect Model (FEM) is utilized to analyze the relationship between explanatory and dependent variables, assuming that each unit can impact the explanatory variables By focusing on the correlations between these variables, the model effectively controls for distinct characteristics that remain constant over time, allowing authors to accurately estimate the true effects of the explanatory variables on the dependent variable.

The distinction between Random Effect Models (REM) and Fixed Effect Models (FEM) is primarily based on the volatility between units In REM, it is assumed that fluctuations among units, which are correlated with the independent variable, do not relate to the explanatory variables Consequently, when there is a dependent variable that varies across units, the REM is often more appropriate than the FEM.

The author employs the Hausman test to compare the Random Effects Model (REM) and the Fixed Effects Model (FEM) to identify the more appropriate model Additionally, the author assesses for issues such as autocorrelation, heteroscedasticity, and multicollinearity within the model If any deficiencies are detected, a Generalized Method of Moments (GMM) regression model will be utilized to address these issues effectively.

Based on the theoretical framework in chapter 1 combined with the research model, the author set out some hypotheses for this study

Model (1) - Assessing the effect of number of Fintech companies in Vietnam on the profitability of Vietnamese commercial banks:

+ Hypothesis H0: Number of Fintech companies in Vietnam does not affect the profitability of Vietnamese commercial banks

+ Hypothesis H1: Number of Fintech companies in Vietnam has an effect on the profitability of Vietnamese commercial banks

Model (2) - Assessing the effect of total funding value in Vietnam Fintech industry on the profitability of Vietnamese commercial banks:

+ Hypothesis H0: Total funding value in Vietnam Fintech industry does not affect the profitability of Vietnamese commercial banks

+ Hypothesis H1: Total funding value in Vietnam Fintech industry has an effect on the profitability of Vietnamese commercial banks

Model (3) - Assessing the effect of public-interest in financial technology information on the profitability of Vietnamese commercial banks:

+ Hypothesis H0: Public-interest in financial technology information does not affect the profitability of Vietnamese commercial banks

+ Hypothesis H1: Public-interest in financial technology information has an impact on the profitability of Vietnamese commercial banks

The author anticipates that the evolution of Fintech will significantly influence the profitability of commercial banks The impact's direction may vary based on the specific Fintech-related variables utilized in each analytical model.

The rise of Fintech companies in Vietnam is leading to a decline in the profitability of traditional banks As these innovative firms expand and offer specific financial services, they create direct competition for banks With their advanced technology platforms, which outpace the often outdated systems of banks, Fintech companies are successfully drawing customers away from traditional financial institutions, ultimately harming the profits of commercial banks.

Investment in Vietnam's Fintech industry is poised to enhance bank profitability by driving significant digital transformation As banks embrace Fintech innovations, they can reduce operational costs and boost profits while attracting customers with advanced financial technology products This evolution is essential for banks to remain competitive and avoid obsolescence in a rapidly changing financial landscape.

As public interest in financial technology rises, banks experience increased profitability In the digital transformation era of Industry 4.0, consumers actively seek information about financial technology, recognizing the conveniences it offers to the financial system Consequently, customers are more likely to choose banks that have adopted advanced financial technologies, enhancing their overall experience This influx of customers allows banks to optimize their operations, ultimately leading to greater profitability.

In addition, to summarize hypotheses about the trend of the impact of other independent variables on the profitability of commercial banks, the author developed the following table:

Table 2.1: Expectation in affecting direction

FC - Dinh Phan et al (2019), Dinh Thi Thu Hong et al

FV + Nguyen Duc Trung et al (2021), Jinsong Zhao et al (2022)

FI + Chi-Chuan Lee et al (2021), Ahlem Chhaidar et al (2021) Macroeconomic variables

GDP + Athanasoglou et al (2006); Anbar and Alper

INF +/- (+) Phan Thi Hang Nga et al (2019) and Nguyen

CIR - Phan Thi Hang Nga et al (2019)

ETA +/- (+) Chi-Chuan Lee et al (2021), Nguyen Duc

Trung et al (2021) (-) Phan Thi Hang Nga et al (2019)

NPL - Phan Thi Hang Nga et al (2019), Chi-Chuan Lee et al (2021), Ahlem Chhaidar et al (2021), Nguyen Duc Trung et al (2021)

SIZE + Ahlem Chhaidar et al (2021)

Source: Author collecting 2.2.2 Research model

The author uses year-on-year secondary data on financial and technological indicators of 27 commercial banks listed on the stock exchange (2008–2022) of 406 observations to test and run the model

To evaluate the factors affecting the profitability of commercial banks listed on the stock exchange in different periods, the authors rely on the research model of

In their 2019 study, Phan Thi Hang Nga et al explored the impact of Fintech development on the profitability of 27 Vietnamese commercial banks Unlike previous research, the author opted to utilize specific data for the explanatory variable rather than a dummy variable This approach led to the development of three models, each corresponding to three key explanatory variables associated with Fintech growth.

(1) Assessing the effect of number of Fintech companies in Vietnam on the profitability of Vietnamese commercial banks:

(2) Assessing the effect of total funding value in Vietnam Fintech industry on the profitability of Vietnamese commercial banks:

(3) Assessing the effect of public-interest in financial technology information on the profitability of Vietnamese commercial banks

+ 𝑅𝑂𝐴 𝑖,𝑡 : rate of return on total assets

+ 𝐹𝐶 𝑡 : Number of Fintech companies in Vietnam in year t

+ 𝐹𝑉 𝑡 : Total funding value in Vietnam Fintech industry in year t

+ 𝐹𝐼 𝑡 : The level of public interest in information about financial technology in year t

+ 𝐺𝐷𝑃 𝑡 : GDP growth rate in Vietnam in year t

+ 𝐼𝑁𝐹 𝑡 : Vietnam's inflation rate in year t

+ 𝐶𝐼𝑅 𝑖,𝑡 : expense to income ratio at bank i in year t

+ 𝐸𝑇𝐴 𝑖,𝑡 : The ratio of capital sources to total assets at Bank i in year t

+ 𝑆𝐼𝑍𝐸 𝑖,𝑡 : logarithm the size of total assets at Bank i in year t

+ 𝑁𝑃𝐿 𝑖,𝑡 : NPL ratio at Bank i in year t

+ 𝐶𝐴𝑆𝐴 𝑖,𝑡 : the ratio of demand deposits at Bank i in year t

+ 𝑀𝐶 𝑖,𝑡 : logarithm value of Bank i’s shares in year t

The study analyzed the effect of financial technology development on profitability of 27 commercial banks listed on the stock exchange in the period 2008-

In 2008, the Government and the State Bank of Vietnam initiated the pilot program for "e-wallets," marking a significant milestone in the digital transformation of the Vietnamese banking system This period set the foundation for the evolution of digital financial services in Vietnam, paving the way for advancements in technology and convenience in banking By 2022, the impact of this transformation was evident, showcasing the growth and adoption of digital payment solutions across the country.

39 time With i representing commercial banks listed on the stock exchange and t representing time, the variables used in the proposed model are as follows:

The dependent variable, ROA(i,t), indicates the rate of return on total assets for bank i in year t This financial metric is manually calculated by the author using a specific formula, utilizing data gathered from the financial statements of bank i for that year.

In year t, the number of fintech companies in Vietnam, denoted as FCt, was obtained through meticulous manual collection from Statista's website (https://www.statista.com) The final data was then processed using logarithmic calculations to provide a comprehensive analysis of the fintech landscape in Vietnam.

The total funding value (FVt) in the Vietnam Fintech industry for year t was obtained through manual collection from Statista's website The author then processed this data by calculating its logarithmic value for analysis.

The level of public interest in financial technology information in year t is represented by FI_t, which was gathered from Google's keyword statistics on Google Trends This data provides insights into the evolving interest in financial technology among the public over time For more details, visit Google Trends at https://trends.google.com.vn/home?geo=VN&hl=vi.

The author assessed FinTech developments by measuring public interest in financial technology information, primarily through headline searches In Vietnam, Google serves as the most popular search tool, reflecting the significant attention Vietnamese people are paying to the global FinTech trend Public interest is largely gauged through Google search levels Utilizing the FSB's classification of FinTech activities and considering the evolution of financial services in Vietnam, the author identified five specific Vietnamese keywords that correspond to various FinTech activities.

Keyword 1 Non-cash payment Online loan Wealth management Big Data Keyword 2 E-wallets Crowdfunding

Keyword 4 Digital bank P2P Insurtech Biometrics

Banking peer-to-peer lending Digital investment

Research and evaluation results

Descriptive statistics

Obs Mean Std Dev Min Max

Source: Result collected from Stata

Table 3.1 displays the statistical outcomes of the variables utilized in the model, derived from secondary data collected from all listed banks on the stock exchange between 2008 and 2022.

The average Return on Assets (ROA) for commercial banks was 1.0449% with a standard deviation of 1.0023%, indicating minimal variation in returns over the years Notably, the highest recorded ROA was 11.9% from Vietnam Export-Import Commercial Joint Stock Bank (Eximbank) in 2013, while the lowest was -5.99% from Tien Phong Commercial Joint Stock Bank (TPBank) in 2011.

The CIR recorded the largest difference when the smallest value was 16.28% while the largest value was 8630.24% (belonging to Tien Phong Commercial Joint

Stock Bank - 2011), the average value of this index was at 71.3%

The ETA variable was a minimum value of 0.04% (belonging to Asia

Commercial Joint Stock Bank -2011), the largest value was 0.9% (belonging to Vietnam Prosperity Commercial Joint Stock Bank - 2009), the average value was

With the largest non-performing loan ratio of 17.93% (belonging to National Citizen Commercial Joint Stock Bank - 2022), the smallest value was 0.02% (Tien

Phong Commercial Joint Stock Bank - 2010), while the average value was 2.08% and the standard deviation with the annual NPL ratio at 1.44%

The SIZE variable exhibited a minor variation, with an average value of 11.36 and a standard deviation of 1.71 The highest recorded value reached 14.567, attributed to the Joint Stock Commercial Bank for Investment and Development of Vietnam.

2022) and the smallest value would be 7,791 (belonging to Tien Phong Commercial

CASA recorded an average value of about 13.611% while the largest value was 46.98% (belonging to Vietnam Technological and Commercial Joint Stock Bank

- 2021) and the smallest value was 0.93% (belonging to Bac A Commercial Joint Stock Bank - 2015)

In 2022, the Joint Stock Commercial Bank for Foreign Trade of Vietnam reported the highest market capitalization (MC) at 13%, while the lowest market capitalization was recorded at 6.016%.

National Commercial Joint Stock Bank - 2008), this is the variable with the lowest difference in the internal variables of commercial banks

The analysis of two key macroeconomic variables, GDP growth rate and inflation rate, reveals contrasting trends The average GDP growth rate stands at 6.017%, with an annual standard deviation of approximately 1.496% Notably, the highest recorded GDP growth rate is 8.02%, while the lowest is 2.56%.

The analysis revealed that the variable with the lowest difference in the model was INF, which exhibited a significant range with a maximum value of 23.11% and a minimum value of 0.631% The mean value of INF stood at approximately 6.594%, while the standard deviation over the years was recorded at 6.161%.

For the main independent variable FC, the maximum value was 5.1705 (in

Between 2008 and 2022, the number of Fintech companies has significantly increased, reflecting a growing demand for financial technology products The mean value of the main independent variable during this period was 3.4266, with a standard deviation of 1.2925, while the minimum recorded value was approximately 1.0986 in 2008 Notably, the COVID-19 pandemic starting in 2020 has further accelerated this trend, leading to a marked rise in the demand for financial services and consequently, the number of Fintech companies.

In Vietnam's Fintech sector, total investment funding peaked at 10.3093 in 2021, significantly higher than the lowest value of 5.3327 recorded in 2008 The average investment during this period was 7.4477, with a standard deviation of 1.5815 This notable increase can be attributed to COVID-19, which acted as a catalyst for innovation and digital transformation, prompting investors to pivot towards less impacted industry groups Consequently, total investment in innovation reached a record high in 2021.

The main independent variable, FI, exhibited an average value of approximately 16,578% with a standard deviation of 8% over the years Meanwhile, the primary explanatory variable, FINTECH, achieved a peak value of 31.09%.

(referring Fintech was most interested in 2021) and the smallest value of 7.29%

The interest in financial technology (Fintech) has significantly increased since 2012, particularly during the COVID-19 pandemic in 2021, which transformed daily activities and heightened the demand for online services This shift has led to challenges in payment processing and access to credit, highlighting the growing need for innovative financial solutions in response to the crisis.

46 accelerated the shift to digitization of financial services while stimulating large investment flows into the financial technology sector of commercial banks.

Assess the current situation

2.1 Overview of Fintech development in Vietnam market

Fintech in Vietnam has experienced remarkable growth, significantly transforming the financial services sector, with further potential for expansion (Marco Nicoli - Senior Financial Sector Specialist, World Bank, 2022) The rapid rise of Fintech is driven by increased access to technology and a young population with rising per capita income This growth is particularly evident in the burgeoning number of startups, which surged from 39 companies in 2015 to 175 by 2022, marking a 4.5-fold increase in just five years.

Figure 3.1: Number of Fintech company in Vietnam

A recent survey by HyperLead, an affiliate marketing platform, reveals that the number of Fintech startups in Vietnam rose by nearly 13%, increasing from 156 companies in 2021 to 176 in 2022 The payment services sector remains the largest, comprising 22.6% of all Fintech companies, followed closely by personal lending and the blockchain/crypto segment.

47 significant growth in the number of startups compared to 2021 can be mentioned in wealth management, technology insurance (insurtech) and buy now pay later (BNPL)

Figure 3.2: Domain of Fintech companies 2022

Digital payment remains the dominant sector in Fintech, representing the largest share of startups and investments Statista forecasts that by 2023, Vietnam will rank fourth in Southeast Asia for digital payment transaction value, outpacing countries like Singapore and Malaysia Additionally, Vietnam has outperformed developed nations such as the UK, Germany, and the US in mobile POS payment penetration, fueled by a rapidly expanding e-commerce market and widespread smartphone usage among its population.

Figure 3.3: Top countries with the largest number of smartphone users

By the end of 2022, Vietnam ranked among the top 10 countries globally in terms of smartphone users accessing the internet, boasting over 60 million users The growth of mobile internet users has shown a consistent upward trend over the years, with Statista projecting this increase to continue throughout 2023 and beyond.

By 2028, the number of digital payment users in Vietnam is projected to rise for the fifth consecutive year, reaching 83.01 million This growth is anticipated to serve as a foundation for the continued advancement of digital payment activities in the country.

The rise of Fintech is positively transforming Vietnam's banking system, significantly influencing the strategies and operations of traditional financial service providers A 2017 survey by the SBV revealed that 96% of Vietnamese banks are exploring Industry 4.0, with 84% learning through media, 48% via known customers and partners, and 16% through product marketing companies Furthermore, 92% of banks are ready to invest in technological innovation, 76% are focused on attracting new tech talent, and 44% are developing financial resources while restructuring branch and transaction staff models.

2.1.2 Total funding value in Fintech industry

The Fintech industry in Vietnam is rapidly growing, attracting significant funding each year as the number of Fintech companies continues to rise, highlighting its potential as a lucrative market.

Figure 3.4: Total funding value in Vietnamese Fintech industry

The FinTech ASEAN 2021 report by UOB, PwC Singapore, and the SFA highlights that the ongoing pandemic has driven unprecedented investment in FinTech across the ASEAN region, with capital reaching nearly $4 billion—an increase of almost four times from 2020 This surge reflects a growing adoption of FinTech solutions, including e-wallets, cryptocurrencies, and online investment platforms Notably, Vietnam secured the third position in FinTech investment, attracting over $1.4 billion, marking the highest funding amount in the country's FinTech history.

Figure 3.5: Value of total funding in ASEAN Fintech industry

At the end of 2022, ASEAN's Fintech funding rose by 7% to $4.3 billion, despite a 19% decline in investment deals year-on-year, largely due to the Russia-Ukraine conflict and rising interest rates aimed at curbing inflation Vietnam and the Philippines experienced the most significant drops in investment deals, contributing to a reduced total funding value for these countries Specifically, Vietnam's Fintech market attracted only $211 million in 2022, which represented just 14% of its 2021 value and accounted for a mere 4% of all deals in the ASEAN region that year.

In 2022, Vietnam's Fintech market experienced significant growth, aligning with the government's 5-year plan (2021-2025) to promote non-cash payments The e-payment sector saw remarkable success, with the acceptance rate of non-cash payments rising to 95% This trend highlights the increasing interest among the Vietnamese population in digital financial products and the widespread adoption of technology applications in the country.

Figure 3.6: The popularity of Fintech application in Vietnam

Since 2008, interest in Fintech in Vietnam has fluctuated, initially peaking with the rise of digital transformation concepts, then declining as the novelty wore off However, from 2015 onwards, when the term "Fintech" officially emerged in the country, public interest surged again and has remained strong This trend indicates a persistent demand for digital financial products and highlights the urgent need for transformation within the financial and banking sectors in Vietnam.

In recent years, "Blockchain" has garnered significant public interest, particularly in the context of digital development As concerns about information security grow alongside the rise of digital financial services, blockchain technology offers distinct advantages over traditional processing methods It enables instant updates on transaction status for all parties involved, ensuring integrity and transparency of information while significantly enhancing security.

The integration of blockchain technology significantly enhances processing times and reduces costs throughout the entire transaction cycle Additionally, it offers improved protection for users against fraud risks in international trade Consequently, this technology is anticipated to emerge as a key trend in the evolution of Fintech in Vietnam.

As smartphone usage continues to rise, social networks have become a key source for technology-related information, alongside Google According to Reputa's report, discussions surrounding fintech are particularly prominent on social media platforms.

Figure 3.7: Three most discussed types of Fintech in social media

By the end of 2022, "E-payment" and "Fundraising model" emerged as the most searched fintech types in Vietnam, alongside payment, lending, and blockchain/virtual currency E-wallets, a key form of cashless payment, dominated this sector, with MoMo leading the E-Payment Company Ranking MoMo's content consistently engages the online community, with over 134,000 interactions on its posts in the past month, including a peak of 15,300 interactions for the popular minigame "QR Hunting Gold."

53 interactions This increases the attractiveness of financial technology products to users

Regression results

Before carrying out the regression of three proposed models, the author proceeds to check the correlation coefficient between the variables used.

Table 3.2: Correlation matrix among variables

ROA FC FV FI GDP INF CIR ETA NPL SIZE CASA MC

Source: Result collected from Stata

The correlation matrix reveals no multicollinearity issues among the variables in the model, as multicollinearity is defined by correlations exceeding 0.80 The analysis indicates a positive correlation between the explanatory variables 𝐸𝑇𝐴 𝑡, 𝑆𝐼𝑍𝐸 𝑡, 𝐶𝐴𝑆𝐴 𝑡, 𝐼𝑁𝐹 𝑡, 𝑀𝐶 𝑡, and 𝐹𝐼 𝑡 with the dependent variable 𝑅𝑂𝐴 𝑖,𝑡 Conversely, a negative relationship is observed between 𝐹𝐶 𝑡, 𝐹𝑉 𝑡, 𝐶𝐼𝑅 𝑖,𝑡, 𝑁𝑃𝐿 𝑖,𝑡, and 𝐺𝐷𝑃 𝑡 with 𝑅𝑂𝐴 𝑖,𝑡.

3.2 Regression model 3.2.1 Determine the research model

This study examines the impact of financial technology development on bank profitability using regression methods with panel data, including Pooled OLS, Random Effects Model (REM), and Fixed Effects Model (FEM) To determine the most appropriate regression model, the author conducted several tests: the Breusch & Pagan test for REM and Pooled OLS, the F test for FEM and Pooled OLS, and the Hausman test for REM and FEM The findings from each method are presented in Table 8.

Table 3.3: Results of testing appropriate models

The results were derived from Stata analyses, focusing on three distinct models Model (1) examines the impact of the number of Fintech companies in Vietnam (FC) as the primary explanatory variable Model (2) explores the total funding in the Vietnam Fintech industry (FV) as the main explanatory variable Finally, Model (3) investigates public interest in Fintech (FI) as the key explanatory variable.

Based on the results in Table 3.3:

+ In the Breusch and Pagan test, all three suggested models have Prob>chibar2

= 0.000, which is smaller than 0.05 Therefore, REM models will be accepted compared to Pooled OLS

The F test results indicate that all three proposed models have a Prob>F value of 0.000, which is below the 0.05 threshold, confirming the acceptance of Fixed Effects Models (FEM) over Pooled Ordinary Least Squares (OLS) Conversely, the Hausman test shows that all three models have a Prob>chibar2 value exceeding 0.05, leading to the acceptance of Random Effects Models (REM) in preference to FEM.

After analyzing the relevant regression outcomes, the author implemented three Random Effects Models (REM) corresponding to the three primary explanatory variables The regression results from the REM, which focus on the key factors associated with Fintech development, are presented below.

Table 3.4: Regression results of REM models

Source: Result collected from Stata Note: The coefficients denote the statistically significant values Asterisks indicate the significance at 10% (*), 5% (**) and 1% (***) level

The effect of Fintech development on bank profitability

The evaluation of the model revealed that key explanatory variables positively influence the Return on Assets (ROA) of Vietnamese commercial banks, with confidence intervals ranging from 95% to 99% This indicates that the growth of Fintech is correlated with improved profitability for these banks The findings align with the Vietnamese market context, characterized by significant interactions between Fintech companies and traditional banks The advancement of Fintech facilitates the adoption of the latest technological trends, accelerating digitalization within the banking sector Consequently, banks can lower operating costs, attract a larger customer base, and enhance their profitability.

65 each development aspect of Fintech on the performance of commercial banks in Vietnam

A 1% increase in the number of Fintech companies in Vietnam leads to a 0.1230% rise in the return on assets (ROA) of commercial banks, contrary to earlier studies by Dinh Phan et al (2019) and Dinh Thi Thu Hong et al (2021), which indicated that the growth of Fintech negatively impacted bank profitability This discrepancy can be attributed to the different research periods, as the earlier studies were conducted before the COVID-19 pandemic, during which Fintech firms directly competed with traditional banks for financial product offerings However, since 2020, the pandemic has heightened customer demand for digital solutions, prompting Fintech companies to collaborate with banks to navigate these challenges Consequently, the increase in Fintech firms has provided banks with multiple partners in various financial service segments, enhancing operational efficiency and boosting profitability.

(2) Total funding value in Fintech industry

A 1% increase in investment value within the Vietnamese Fintech market correlates with a 0.0958% rise in the return on assets of commercial banks, assuming all other factors remain constant This finding aligns with the research conducted by Nguyen Duc Trung and colleagues.

Investing in the financial technology industry is crucial for its development, prompting banks to embrace digital transformation to remain competitive This shift accelerates the adoption of new technologies, leading to cost reduction and enhanced profitability by attracting customers with innovative financial technology products.

(3) Public-interest in Fintech information

As interest in financial technology rises by 1%, the return on assets for commercial banks is expected to increase by 0.0402%, indicating that enhanced engagement with banking-related technologies—such as payment systems and credit services—can significantly boost bank profitability The COVID-19 pandemic has accelerated public awareness of the convenience offered by financial technology, particularly in the context of digital transformation among Vietnamese commercial banks By diversifying their digital product offerings, banks can attract more customers, leading to a substantial improvement in revenue, a finding supported by research from Ahlem Chhaidar et al (2021) and Chi-Chuan Lee et al (2021).

The effect of macroeconomic variables

The analysis reveals that the GDP growth rate significantly influences the Return on Assets (ROA) of banks, particularly in model (3) with a 95% confidence interval Specifically, a 1% increase in GDP growth rate correlates with a 0.0617% decline in the profitability of commercial banks' total assets, assuming other factors remain constant This finding aligns with prior research, suggesting that as GDP rises, the economy is perceived as expanding, leading to increased borrowing and higher deposit rates among consumers Consequently, banks can enhance their profitability and improve asset quality.

The inflation rate significantly influences a bank's Return on Assets (ROA), exhibiting a positive relationship By forecasting the expected inflation rate, banks can strategically develop financial products aimed at enhancing profitability, as the inflation rate serves as a key determinant for setting deposit and lending interest rates This finding aligns with previous research conducted by Phan Thi Hang Nga et al (2019) and Nguyen Duc Trung and colleagues (2022).

The effect of explanatory variables related to commercial banks

Internal variables related to commercial banks such as CASA and MC also have a positive influence on ROA in all three suggested models While the

The independent variable MC has the most significant positive impact on ROA, while CASA shows only a minor effect This aligns with previous studies referenced by the author, indicating that reputable listed commercial banks with strong branding and equity foster customer trust This trust enables them to lower capital mobilization costs and enhance liquidity through interbank market transactions or dealings with the SBV on the open market Consequently, these banks can increase working capital and decrease their liquid asset reserves.

The analysis reveals that the Cost-to-Income Ratio (CIR), Non-Performing Loans (NPL), and bank size (SIZE) negatively impact Return on Assets (ROA) across three models Specifically, a 1% increase in CIR results in a 0.0001% decrease in ROA for commercial banks Additionally, an increase in NPL correlates with a decline in total asset returns, indicating that reducing bad debt relative to outstanding loans can enhance ROA Moreover, maintaining capital reserves to mitigate risks adversely affects profitability Notably, SIZE has the most significant negative effect on ROA, suggesting that larger banks experience reduced returns on assets Overall, the findings indicate that higher debt levels, increased lending, and liquid asset holdings do not substantially improve bank profitability in terms of asset returns.

3.3 Defects of regression model testing

To enhance the accuracy of the regression model, the author performs an inspection to identify existing defects The findings reveal that the REM study model exhibits two key issues: variable variance and autocorrelation among the errors.

Source: Result collected from Stata

Recommendations

Conclusion

This thesis investigates the impact of financial technology advancements on the profitability of commercial banks, focusing on Vietnamese joint stock commercial banks from 2008 to 2022 Utilizing data spanning this 14-year period, the study aims to provide experimental evidence on how fintech development influences bank performance in Vietnam.

The findings from the REM and GMM regression models indicate a clear consensus on the positive impact of financial technology (Fintech) development on the profitability of Vietnamese commercial banks from 2008 to 2022 Specifically, an increase in Fintech—reflected by the number of companies, investment value, and public interest—correlates with enhanced profitability for these banks This trend aligns with the current 4.0 era, where Fintech serves as a catalyst for financial institutions and commercial banks to engage in digital transformation, effectively addressing the diverse demands of customers.

The macroeconomic factors, specifically GDP and inflation rates, significantly influence the profitability of Vietnamese commercial banks A stable economy characterized by positive GDP growth and controlled inflation allows banks to operate more efficiently, ultimately enhancing their profitability.

The explanatory variables affecting bank performance indicators exhibit diverse effects While the ETA variable does not influence the dependent variable, its correlation coefficient indicates a positive relationship with Return on Assets (ROA) Additionally, both the CASA ratio and market capitalization (MC) contribute positively to bank profitability, reflecting optimized capital mobilization costs Conversely, the cost-to-income ratios (CIR), non-performing loan ratio (NPL), and the size of commercial banks (SIZE) negatively impact the return on total assets of commercial banks from 2008 to 2022.

The findings of this research enable commercial banks to better evaluate the impact of financial technology advancements on their profitability, guiding their investment decisions towards technologies that yield long-term gains While this research represents a novel approach, it is constrained by time and data collection limitations, as well as certain qualifications Therefore, the author welcomes feedback and suggestions from readers to enhance the study's quality.

Recommendation

Firstly, building a legal basis supporting the cooperation between banks and Fintech

The regression model results indicate that Fintech development positively impacts bank profitability, suggesting that enhancing Fintech can advance the Vietnamese banking system While existing regulations, such as Regulation No 291/2006/QD-TTg, provide a foundation for non-cash payments, they primarily address payment systems and do not encompass the broader range of Fintech applications, including insurance technology and investment management As these sectors require stringent oversight, it is crucial for the Government to enhance the legal framework to facilitate better cooperation between Fintech and banks.

Secondly, creating a supportive environment for the development of financial technology in Vietnam

Vietnam's Fintech industry is characterized by management mechanisms and priorities that foster an open market environment, making it an attractive destination for Fintech start-ups This openness not only encourages the growth of new ventures but also significantly boosts the value of funding within the sector.

To pursue that goal, the Government needs to have policies to support facilities and infrastructure so that Fintech firms have the opportunity to develop synchronously

The Government should implement targeted policies for lending activities aimed at Fintech start-ups to effectively address market demands and mitigate risks associated with entering the new industry Additionally, in light of the growing number of Fintech companies in Vietnam, it is essential for the Government to establish regulations that ensure the quality of industry operations This approach will help balance quantity with quality, preventing the emergence of companies that do not contribute positively to the socio-economic landscape.

Thirdly, control and improve information security systems

The digitization process is closely linked to information risks and virtual security, prompting the Government to invest in robust information technology infrastructure and connectivity to enhance information security in today's network environment As internet access and the use of financial and technology products continue to rise, individuals increasingly face risks such as online transaction information theft and Fintech-related scams, including capital contribution schemes for virtual currency miners and ICO fraud Therefore, authorities must prioritize communication and education to improve public financial literacy regarding digital finance, empowering consumers to navigate the digital economy safely and protect themselves from potential Fintech risks.

Firstly, develop mechanisms to timely support and guide commercial banks in the process of cooperating with Fintech companies

The integration of technology through Fintech companies is essential for banks to advance their digitization efforts and diversify financial products To achieve a cohesive banking system, this transformation must occur concurrently The State Bank of Vietnam (SBV) should provide clear direction and guidance to commercial banks regarding Fintech, facilitating their digital transformation Additionally, organizing training sessions, seminars, and discussions with industry leaders and banking educators will enhance understanding and implementation of these changes.

77 knowledge related to Fintech will be a solution that should be implemented regularly

The State Bank of Vietnam (SBV) should organize seminars to address the challenges faced by commercial banks in their collaboration with Fintech companies, enabling timely solutions to overcome existing limitations.

Secondly, proactively have policies and testing mechanisms to develop the relationship between Fintech and commercial banks

The State Bank of Vietnam (SBV), as the leading authority in the commercial banking sector, has effectively implemented policies to mitigate the effects of the macro environment on bank operations According to the GMM model, macroeconomic factors exert a limited influence on the performance of Vietnamese commercial banks In light of the growing collaboration between Fintech and traditional banks, the SBV can enhance governance and management efficiency to foster this partnership The introduction of a regulatory sandbox could be beneficial for various Fintech sectors within banking, such as insurance and investment management, which are still emerging in Vietnam This sandbox approach aims to innovate and evaluate the compatibility of banking services with Fintech solutions, thereby promoting the growth of Fintech and digital banking services in the country.

Thirdly, promote communication and dissemination to the market- knowledge and adapt to Fintech

To prepare finance and banking students for the evolving job market, educational institutions must enhance their curricula with comprehensive training in technology areas such as big data, digital banking, and information security Implementing a Fintech bachelor's program alongside disciplines like data science and business analysis is essential Additionally, schools should collaborate with banks and Fintech companies to facilitate internships and exchanges, allowing students to gain practical experience and explore innovative technological solutions, business models, and services.

2.3 For the Vietnamese commercial banks

First, banks need to expand technology investment as a key strategy in the near future

The regression analysis from the REM and GMM models indicates that fintech development positively impacts the profitability of commercial banks This suggests that by prioritizing financial technology in their operations, banks can enhance their profitability In a rapidly evolving technological landscape, investing in technology has become a crucial trend for the banking sector The Covid-19 pandemic in Vietnam further highlighted the importance of technology, as its application not only improved bank operations but also supported broader economic activities Consequently, banks have experienced increased revenue, enhanced cost efficiency, and improved return on equity Additionally, banking experts must continually assess market needs and interests to identify key products that attract customers and enhance market applicability.

Second, continue to develop plans to limit information security and security risks

Technology introduces significant information security risks for commercial banks, which handle vast amounts of customer data and transactions To mitigate these risks, banks must increase their technology budgets to effectively combat cybersecurity threats Additionally, optimizing public solutions is essential for enhancing the information security of Fintech applications, enabling prompt responses to potential malicious attacks An example to consider is Switzerland, where banks utilize anonymous numbered accounts to protect customer anonymity, ensuring that account holder names do not appear on most public or internal documents, such as statements.

Third, pay attention to the quality of human resources

In the face of competition from financial technology companies, banks must prioritize the training and development of high-quality human resources with strong customer care skills By capitalizing on their existing strengths in financial operations and market positioning, banks can enhance long-term relationships with customers While collaboration with Fintech firms is a growing trend, banks should avoid over-reliance on these partnerships Engaging with technology companies allows banks to exchange valuable insights and implement affiliate programs that foster the application and management of technology products, ensuring the security of sensitive information.

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