BANKING ACADEMY OF VIETNAM THE FACULTY OF FINANCE ------ GRADUATION THESIS TOPIC: THE IMPACT OF FINTECH DEVELOPMENT ON DEFAULT RISK OF VIETNAM COMMERCIAL BANKS... ACKNOWLEDGEMEN
LITERATURE REVIEW
THEORETICAL FRAMEWORK
Fintech is a term that derives from the combination of the words finance and technology and is defined as an interdisciplinary field that combines finance, technology management and innovation, describes the connection of internet-related technology with commercial service activities of the financial sector such as banking transactions and money lending
The International Monetary Fund (IMF) (2018) defined fintech as "advances in technology that have the potential to transform the provision of financial services by stimulating the development of new business models, applications, processes and products" The Financial Stability Board (FSB) (2019) described the term “Fintech” as “the technology that has enabled innovation in financial services and could lead to new business models, applications, processes or products with a material effect associated with the provision of financial services” In addition, The Organization for Economic Co-operation and Development (OECD) (2018) defined Fintech as
“innovative applications of digital technology for financial services" Besides, the World Economic Forum (WEF) (2016) stated that Fintech is a broad category that refers to the innovative use of technology in the design and delivery of financial services and products In comparison with the definition provided by WEF, the International Organization of Securities Commissions (IOSCO) claimed that "Fintech is not only about the application of digital technologies to financial services but also the development of business models and products based on these technologies and more generally on digital platforms and processes"
However, until now, there is no unified legal definition for this term The definition of it is described in many academic researches and in business journals Specifically, Duong Tan Khoa (2019) described fintech as the use of technology to automate the provision and use of financial services, thereby better responding to financial transactions and trade In a broader understanding, Arner et al., (2015) considered Fintech as a new market that integrates finance and technology, and
Hochstein (2015) documented that fintech is replacing traditional financial structures with new technology-based processes Mackenzie (2015) and Schueffel (2016) found that through reviewing the current concept of Fintech in scientific articles, the most comprehensive and universal concept is that Fintech is the application of innovative, creative and modern technologies to in the financial sector, in order to provide customers with transparent, efficient and convenient financial solutions/services at a lower cost than traditional financial services
Meanwhile, ASIC (2016) defined fintech companies as a term for startups using new business models and new products that compete with core banking, insurance or payment services Regarding the concept of Fintech company, Boldt
(2017) stated: “Fintech companies are businesses that use new technology to create better new financial services for both consumers and businesses Fintech companies include companies of all types that can operate in financial management, insurance, payment, asset management " Fintech, or financial technology, encompasses all technological advancements in the financial sector, including innovations in financial literacy and education, retail banking, investment, and cryptocurrencies Normally, Fintech companies are classified into 2 main groups (1) Consumer service companies that provide technical tools to improve the way individuals borrow, finance startups, and manage money; (2): Companies in the form of “bank-office”, specializing in technology support for financial institutions
Overall, the Fintech company is the latest successful application in the field of information technology to create better new financial services for both users and businesses The highlight of Fintech companies in the financial services market is in the fast payment speed (peer-to-peer payments), convenience in personal financial management, the ability to access loans (financial financing, community, crowdfunding) Besides, Thakor, (2019) researched that the Fintech company also brings many benefits to banks, businesses and users such as: (i) Reducing the cost of searching for suitable transaction parties; (ii) Achieve economies of scale in the fall and exploit big data; (iii) Transactions become safer and cheaper; (iv) Reduced verification costs
Most of the products in the financial sector today are applying Fintech, in other words, Fintech is already present in the majority of financial services products In
2017, the FSB published "Financial Stability Implications from FinTech", classifying FinTech activities focused on the services provided by new business models with the help of technology In specific, FSB classified the implications of Fintech into five groups: payments, clearing and settlement; deposits, loans and capital raising; insurance; investment management; market support a Payments, clearing and settlement
This is one of the most popular types of Fintech services A number of innovations that use mobile devices and connectivity to make payments easier and more valuable have come to market Digital wallets and automated machine-to- machine payments are two examples While the underlying payments infrastructure will not be disrupted, the majority of these innovations will modify front-end processes to enhance the customer and merchant experience (WEF, 2015)
The Internet and e-commerce have changed the traditional buying method of consumers Consumers will no longer be limited in time and place, they can buy products and services anywhere and anytime (Hasslinger et al., 2007) Along with the development of information technology, digital has changed the business environment in the world, so business transactions also changed from cash transactions to electronic money transactions (Mohamad, Haroon and Najiran, 2009) Transactions between business partners continue to grow on the basis of e-commerce, electronic payment solutions appear to replace cash payment systems (Dennis,
2004) In the e-commerce environment, payment for the exchange of money in electronic form is called electronic payment, electronic payment is an integral part and the most important part of e-commerce, in general Electronic payment is used in paying for goods and services purchased online through the use of the Internet (Roy and Sinha, 2014)
In accordance with their current responsibilities for payments infrastructure, the majority of jurisdictions have issued new rules or plans to issue new rules regarding mobile payments, non-bank payments, and digital currencies These
7 regulations aim to increase financial inclusion, provide greater consumer access to payment services, and ensure the smooth operation of payment systems In specific, up to 2022, The State Bank of Vietnam has granted licenses to 48 non-bank organizations to provide electronic payment services (e-wallet services, online payment gateways, and mobile payments) to meet the requirements of e-commerce transactions and small money transfers In Vietnam, e-payment was born in 2008 with the first model being an e-wallet Currently, there are many businesses exploiting the e-wallet model, but according to information from the State Bank, only 9 enterprises such as Payoo, MoMo, Mobivi, are licensed to test this type of service Previously, the EU made it possible for non-banking service providers to enter the market for payment services by establishing a suitable regulatory framework for payment institutions in 2007 After that, in 2015, the directive on payment services that govern new FinTech players like payment data aggregators and payment initiation service providers was also revised by the European Union (EU) Concerning digital currencies, there are efforts to address fraud, money laundering, and terrorist financing risks as well as to clarify the regulatory framework for storing or transferring value
The World Economic Forum (2005) stated that electronic transactions bring many advantages to individuals in specific, and to the economy overall It reduces the need for customers and businesses to carry cash, lowering associated costs such as bank trips, price inflexibility, and opportunity costs (i.e., interest earned) Besides, businesses and financial institutions can save money on cash management since fewer bills are exchanged by hand and money transactions are resolved electronically Electronic transactions also allow financial organizations and authorities more visibility into the flow of money, simplifying taxation, transparency, and information collecting Therefore, by preserving transaction records and decreasing the need to store currency, it protects customers and merchants from fraud and theft b Deposits, loans and capital raising
To remedy weaknesses in the traditional lending model, alternative lending institutions have formed New industry players are emerging all over the world,
LITERATURE REVIEW
The scope of application of Fintech is increasingly wide, especially Fintech also has activities in the main business of commercial banks Typical products of Fintech can be mentioned as payment, digital banking, peer-to-peer lending (P2P lending), crowdfunding, insurance technology (Insurtech), investment and asset management (retail investment) blockchain/ cryptocurrency, credit scoring, SMEs Financing, Comparison, POS3 Because of its convenience and ability to save costs, Fintech applications in solving financial problems are increasingly popular and tend to develop in the future Research on the impact of Fintech on commercial banks’ default risk is becoming increasingly popular in both scope of international and national because by assessing the impact and impact level, researchers or policy makers can come up with solutions to promote the positive impact of Fintech on commercial banks’ default risk In addition to the research results that Fintech has positive impacts on default risk of banks, many studies suggest that in some economies, Fintech negatively impacts default risk of banks
In terms of international research, there are many articles supporting Fintech have a negative impact on the default risk of commercial banks, saying that Fintech companies are the driving force for commercial banks to transform more strongly to meet the increasingly diverse and demanding needs of customers, thereby attracting new customers and creating a competitive advantage for banks Fintech companies entering the market with technological advantages cause banks to actively transform digitally to improve bank performance as well as customer experience and somewhat compete with Fintech companies Deloitte's Digital Banking Maturity Report (2020) surveyed 318 banks with online retail services in 39 countries and found that the banks that lead digital trends and have the most positive digital transformation processes (digital champions) are the banks that provide the most variety of services and provide the best customer experiences The paper also points out that through the adoption of fintech, commercial banks can improve traditional business models, reduce operating costs, improve service efficiency, and enhance risk-taking
Moreover, Inna Romānova and Marina Kudinska (2017) stated that the development of Fintech has growing impact on banking business as many banking products are information-based and therefore can be purchase from different financial service providers Besides, the development of fintech has enhanced competitiveness of commercial banks, as digital technologies have played a significant role in improving the efficiency of services provided by banks and other financial institutions to small and micro enterprises, and to private enterprises (Berg and Burg, 2019) Banks and other financial institutions are seeking to minimise the costs of customer acquisition and risk control, reduce operating costs and improve efficiency, and enhance the user experience for a wider range of consumers, leading to increasingly strong demand for fintech applications (Allen and Gale, 2000) Intelligent decision-making, marketing, risk control, operations, and customer service enabled by fintech can optimise an institution’s credit process and customer evaluation model Aylin Aslan (2020), enable the quick lending of money, reduce the overall cost of corporate financing, and enhance the economic efficiency of financial services (Bartlett, 2018) Fintech lenders also respond more elastically to demand shocks and have a higher propensity to refinance, especially for borrowers who are likely to benefit from it In this way, fintech lenders have improved the efficiency of financial intermediation in mortgage markets (Egan, 2016) Moreover, FSB points out that fintech can expand banking business organizations in terms of scale and space and promote the construction of a system architecture of intelligent banks, so as to improve the efficiency of the entire financial system (FSB, 2017) Specifically, the leading role of the fintech industry in technological innovation is in providing a demonstration to banks Banks can make technological progress and improve productivity by applying such technology and increasing the related research investment (Berger, 2003) In addition, the credit system developed by a bank based on big data and other technologies can reduce the risk in the transaction process (Dynan et al., 2006), while applications based on cloud computing can improve the internal management efficiency of the bank, make the information communication between departments more efficient, and help the bank expand its organizational scale (IMF, 2017) There are also studies from the perspective of bank risk management showing that banks that actively embrace
27 financial innovation exhibit better risk management performance (Norden et al.,
2014) Financial innovation alsp increases the diversity of banking services (Berger, 2003; Merton, 1992), strengthens the risk-sharing ability of banks (Allen & Gale,
1994) and improves the efficiency of resource allocation (Houston et al., 2010; Ross,
1976) The findings of existing studies show that commercial banks can enjoy the benefits of the technology spillover effect by using FinTech innovation, such as optimizing operating performance and improving risk control capabilities In terms of operating performance, commercial banks can be empowered by FinTech innovation to augment service options, meet the diverse needs of customers, and boost their growth space (Gomber et al., 2017), thus improving profitability and stability In terms of risk control, FinTech innovation can use advanced technologies, including biometrics and voice recognition, to reduce labor, capital, and time costs in order to improve data accuracy, which in turn can reduce the internal risk of fraud as well as systemic risk (Fuster et al., 2019) In addition, FinTech innovation can also combine with banks’ loan services to reduce information asymmetry between banks and borrowers, thereby making banks more secure and flexible (Gomber et al., 2017) while reducing the probability of borrowers’ defaulting It can be said that most previous studies have agreed that FinTech increases the competition of banks Ding et al (2022) found that FinTech development promotes lending to firms because internet credit intensifies bank loan competition
In contrast, many other studies show that the impact of Fintech on commercial banks’ default risk is the positive effect Furthermore, some academics say that the rapid development of credit and non-compliant creditworthiness assessment operations by Fintechs, particularly neo-banks, digital banks, and peer-to-peer lenders, creates systemic risks and enhances default risk (Mild et al., 2015; IMF,
2022) The FSB (2017) found that aggregators (service providers that use technology to facilitate money transfers between financial institutions) increase the speed and ease of moving cash between banks in response to financial market performance, which may increase volatility caused by investors who are overly sensitive to market news Many past research had concluded that FinTech increases bank competitiveness Ding et al (2022) discovered that FinTech development encourages
28 lending to businesses because internet credit increases bank loan competitiveness In addition, fintech has also intensively impacted the intermediary businesses of commercial banks, as well as the incentives within organisations (Foà, 2015) Payment settlement has always been one of the most basic and traditional intermediate businesses of commercial banks According to information asymmetry theory, commercial banks, as financial intermediaries, help alleviate information asymmetry to a certain extent (Kelly, 2016) Their information-based advantage and the resulting monopoly position have granted commercial banks long-term and unique advantages Fintech, which enables third-party and mobile payments, has reduced these advantages (Berger, A., R Demsetz, 1999) Moreover, in key areas such as residential mortgages, commercial banks have lost market share to shadow banks and fintech lenders, which are subject to different regulations and enjoy technological advantages (Buchak et al., 2018) The China Banking and Insurance Regulatory Commission has also emphasized that banking financial institutions should embed big data applications into the process of business operations, risk management, and internal control to effectively capture risks FinTech could improve banks' business models and increase bank diversification, thus reducing bank credit risk, and therefore decrease default risk (Demirgỹỗ-Kunt and Huizinga, 2010) Moreover, some research found that although emerging intelligent investment consultants can save a bank’s labor costs, they may homogenize investors’ behavior and lead to scale risk in the financial industry (Baker & Dellaert, 2018), which eventually affects bank operations through the external environment (Vives, 2017) Another study concluded that financial innovation improves banks’ ability to bear risks, resulting in excessive credit expansion in financial markets and leading to the occurrence of financial crises (Brunnermeier, 2009) In addition, Fintech can also contribute to default risk in terms of financial institutions that can access a lot of information to assess customer financial capacity, thereby reducing bad debt risk (Furche et al., 2017) Moreover, there are some studies found that the effect of FinTech on bank risk-taking is more salient in banks with greater shadow banking and lower efficiency, where shadow banking and bank efficiency depend on the structure of bank ownership (Ding et al., 2020; Figueira et al., 2009; Lensink et al.,
2008; Wang et al 2021) Therefore, whether banks in emerging countries need to consider restructuring their ownership structures in the context of an increasingly high level of FinTech development to maintain stability needs to be studied more closely Chan et al (1986) proposed the “competition-stability hypothesis” and argued that “the quality of screening loan requests and the quality of its loans portfolio depend upon the surplus that results from such a screening process” They also argued that, if the level of competition in the market increased, the surplus decreases and results in a decline in the quality of bank loan assets Mishkin (1999) suggested that banks with lower competition usually receive public guarantees and support, which may result in increased bank risk and reduced bank stability, with a resultant moral hazard problem Some previous empirical studies have supported this hypothesis (Albaity et al., 2019; Boyd & De Nicolo, 2005) Moreover, the research of Maoyong Cheng and Yang Qu (2020) documented that FinTech also has some negative effects on commercial banks, such as technical risk and regulatory risk In another study, Li et al., (2020) examined the risk spillover between FinTech companies and conventional financial institutions during a period of rapid technological advancement Using the U.S financial and FinTech firms’ stock returns and the Granger causality framework, the authors investigated pairwise risk spillovers across quantiles The main findings from the study indicated that FinTech firms’ risk spillover to financial institutions positively correlated with an increase in the systematic risk of financial institutions Additionally, using a sample of 41 banks and FinTech firms in Indonesia, Phan et al., (2020) examined whether the growth of FinTech firms negatively influences banking performance Their main results demonstrated that FinTech firms’ growth negatively influences banks’ performance
In addition, a number of research articles on the impact of Fintech on bank’s risk in Vietnam also returned mixed results The development of Fintech is a major challenge for key managers in Vietnam in terms of stable management and development of financial markets as well as a challenge for traditional financial institutions due to the emergence of more and more fintech companies operating and providing all services of both banking and regulatory financial institutions traditional non-bank financial institutions (Tran Thi Xuan Anh et al., 2020) Quang Khai Nguyen
30 and Van Cuong Dang (2022) discovered that FinTech development often reduced financial stability and increase risk probability by gathering data from 37 commercial joint-stock banks in Vietnam from 2010 to 2020 Their study is the first to look into the function of market discipline in limiting the impact of FinTech development on bank’s risk They suggested that market discipline aids an emerging market by mitigating the negative effects of FinTech development They also discovered that the detrimental impact of FinTech on default risk increase when banks have a higher percentage of state ownership but decreases when banks have a bigger percentage of foreign ownership Furthermore, the problem of knowledge asymmetry was discovered to be particularly severe in developing countries such as Vietnam (Huynh et al., 2020) The development of Fintech may threaten the banking industry may be threatened with profits, increase the level of other risk groups in business, fail to meet or violate the requirements of regulatory agencies such as customer information security, anti-money laundering, financing terrorist activities, as a result, the stability of the financial system will be negatively affected is inevitable (Tran Thi Xuan Anh et al., 2020) In contract, the research of Hoang Duc Sinh and Dao Duy Tung (2021) documented that the development of Fintech increases profits, leads to innovations and improves risk control for commercial banks.
RESEARCH FRAMEWORK
In order to better understand the elements that influence default risk and how they vary depending on the circumstances, a measure of default risk has been established and refined via prior research However, the degree of development of the nation and the banking system are often disregarded in empirical studies of the effect of Fintech on default risk Therefore, the key aims of this study are to investigate the impact of FinTech growth on the default risk of commercial banks in Vietnam To begin, our research bolstered the limited body of literature on how FinTech innovation affects default risk Ting Yao and Liangrong Song's (2021) study is the first to examine the effect of FinTech growth on bank-level default risk in developing economies Since banking system stability is seen as the primary factor driving economic development, especially in less developed nations, research on banking system stability is crucial Two, in keeping with other studies by Agrawal et al
(2015), Heimer (2016), Qasim and AbuShanab (2016), and Narayan et al This investigation into the effect of the various indicators of Finech's growth on the default risk of commercial banks is the first of its kind in Vietnam
This study answers two research questions: (i) does Fintech have a positive or negative impact on the default risk of commercial banks, and (ii) how can Vietnamese commercial banks reduce default risk and improve their performance in light of the rapid growth of the Fintech industry? The authors provide research ideas concerning the effect of Fintech on banks' default risk in light of existing theories on Fintech's part in promoting Financial Inclusion:
H1a: FinTech is negatively associated with default risk of commercial banks H1b: FinTech is positively associated with default risk of commercial banks H2: Fintech start-ups is positively correlated with commercial banks’ default risk
The research used a linear regression model to evaluate the influence of Fintech on the default risk of commercial banks Next, by examining the linear regression model, the paper will show the factors that affect commercial banks’ default risk and the extent of their impact
In order to conduct the graduation thesis with the topic: "The effect of FinTech development on default risk of Vietnam commercial banks", the research paper is conducted with a layout of 3 chapters The content of each chapter is summarized as follows:
First of all, Chapter 1 will provide a theoretical framework for the financial indicators used in the paper Specifically, the theories presented in the paper mainly focus on the concept of Fintech, the scope of application of Fintech and the research framework on the influence of Fintech on baking sector Next, the author will review published experimental works related to the above topic to learn research methods, and at the same time find research gaps From here, the author provides research questions and hypotheses of the article
Next, in Chapter 2, the author proceeds to describe the data and give the research method of the whole article In particular, in the data description, the author
32 presents the database, processes the data, and describes the processed data using both qualitative and quantitative methods
Last but not least, in Chapter 3, the author applies the above methods to run the research results, then validates the models and analyzes and discusses the research results further At the same time, the author will make conclusions and recommendations for economists and policy makers in Vietnam
DATABASE AND RESEARCH METHOD
DATABASE
From 2017 through 2022, the authors of this research gathered data from 33 commercial banks in Vietnam The State Bank of Vietnam reports that there will be
43 commercial banks as of December 31, 2021 However, several banks' data were not completely disclosed throughout the research period; hence, 33 commercial banks with the most comprehensive data were selected in the study sample to guarantee the balance sheet data And the commercial banking system's total assets were 18,275,903 billion VND as of December 31, 2022 (State Bank of Vietnam, 2022) Meanwhile, the author estimates that on December 31, 2022, the combined assets of the 33 commercial banks he or she utilized amounted to VND 15,182,206 billion, or 84.07% of the total assets of commercial banks This means that the 33 commercial banks chosen by the authors are really representative of Vietnam's commercial banking sector
In particular, the state-owned commercial banks include 7 banks, Agribank, CTG, VCB, BID and 3 banks acquired by the state for 0 VND, namely OceanBank, VNCB, GPBank (The State Bank of Vietnam, 2022) With the research sample, due to the limited accessibility and data collection, the state-owned commercial banks included by the author include 4 commercial banks: Agribank, CTG, VCB, BID, 4 foreign commercial banks including Public Bank Vietnam, Shinhan Bank, ANZ, HSBC; The rest are 25 joint stock commercial banks In addition, the selection of the time period from 2017 to 2022 to carry out research comes from the fact that this is the period when Vietnam's Fintech market has made remarkable developments, commercial banks participate in the race for digital transformation The total sample of the study was 198 observations While collecting data, the author has tried to collect secondary data from financial statements, cash flow statements and notes to audited financial statements and published on the official website of the bank In addition, the endogenous variables of each bank are also carefully calculated through Excel software based on the above data In addition, the paper examines secondary data extracted by the author from commercial banks’ financial reports, the World Bank database, Statista's database, and The International Monerary Fund
The project measures the default risk of Vietnamese commercial banks by the Z-score bankruptcy risk index inherited from the study of Boyd and Runkle (1993); Beck et al., (2008); Laeven and Levine (2009); Hesse, Cihak (2010), and Jabra
(2020), the Z-score index for banking groups are calculated based on the following formula:
In which ROA is the annual average return on total assets of each bank, EA is the ratio of equity to total annual assets for each bank, SD(ROA) is the standard deviation of the return on assets in the research period (6 years)
The author calculates the Z-score of 33 commercial banks in each year from 2017-2022, and calculates the average Z-score in 6 years 2017-2022 to rank and evaluate the default risk of banks in the research period In addition, the thesis also divides the group of observation according to the form of ownership and the size of charter capital to evaluate default risk according to different classification criteria
The author used three proxies—FinTech company count (FINC), FinTech transaction count (FINT), and ATM count—to evaluate the growth of the FinTech industry In year t, the entire number of FinTech startups is taken into account for FINC, and the total value of all FinTech transactions is recorded as FINT multiplied by the natural logarithm The growth of the FinTech industry is proportional to the number of FinTech deals and startups The standard for ATM density is the number of machines available per 100,000 residents The number of ATMs available to the public is determined by these numbers More consumers utilized FinTech services when there were more ATMs available The last variable the author employs is INNOV, which stands for "innovation index" and measures the ability and results of innovation in global economies
2.1.2.2 Bank characteristics a Bank total assets
Total assets can present a bank’s size; the higher the total assets, the bigger the bank, with loans and deposits making up a substantial amount of total assets Therefore, the power to mobilize deposits and large outstanding loans is stronger the higher the overall assets According to international standards, this is one of the crucial elements to evaluate the financial stability of the bank's commercial operations
The Basel Accord stipulates that a bank's operational safety, as well as the safety of the financial system as a whole, depends on the bank's ability to compete in the market, implement an effective risk management model, increase liquidity, and maintain an adequate capital adequacy ratio Saunders et al (1990) and Chen et al
(1998) discovered a negative correlation between credit risk and the size of a bank on addition, the size of the bank has a significant effect on systemic risk, as shown by Hovakimian et al (2012) Large bank management will take far more risk if they think the government's too-big-to-fail policy would shield their institutions during financial crises (Zardkoohi et al., 2018) Further, despite government backing in the form of wide guarantees for creditors, Carlson and Rose (2019) find that institutional creditors with high exposures choose to withdraw money during runs on systemically important financial institutions Souza (2016) also asserts that large, well-known banks are a major cause of systemic losses when sudden shocks occur Therefore, the quantity of a bank's total assets is often inversely related to its default risk b Asset Growth
Asset growth is the difference between the current year's total assets and the prior year, expressed as a percentage increase If a bank quickly extends loans in response to abnormal asset growth, its risk may dramatically rise (Foos et al., 2010) c Deposit to total asset ratio
Low deposits to assets ratio increase the likelihood of a financial crisis spreading, according to Ahrend and Goujard (2015) The greater the amount of money a bank can generate from its clients, the more reliable that bank is since society supplies the bulk of the bank's overall assets If a bank doesn't have enough deposits, it may potentially incur economic shocks during a crisis
36 d Debt to total asset ratio
The proportion of loans to total assets reveals how well a bank can handle risks and maintain the quality of its assets This ability to offset the dangers of bank borrowing is also shown Increasing the bank's loan amount will increase its revenue, but only if the bank takes steps to better manage its operations and account for credit risks When a bank's total assets fall, it may scale down its operations and reduce its reliance on short-term borrowing, so lowering its risk profile Therefore, the proportion of the bank's debt to its total assets might increase or decrease the risk of default Banks with an overconfident CEO are more likely to increase their debt in the years leading up to a crisis, as shown by research by Ho et al (2016) Fahlenbrach et al (2012) argue that leverage has an effect on bank performance in times of crisis e Outstanding balance to customer’s deposit ratio
The bank is regarded as a reputable lending organization However, client deposits account for the majority of the bank's capital, which will be utilized to extend credit
The bank makes appropriate use of its capital when the ratio of outstanding loans to client deposits is high, which would boost the bank's income but require the bank to tighten risk management According to preliminary study by Keeton (1999), the origin of credit growth might either raise or decrease credit risk If a rise in outstanding loans results from increasing capital demand rather than increased capital supply, then the credit risk for the total existing loan cannot be reduced As a result, there is frequently a negative correlation between the bank's default risk and the client's outstanding debt ratio on customer deposits f Diversification ratio
The author adopts the use of income diversity as a control variable after reading Chen et al (2017) Williams (2016) suggests that a larger concentration of income might make a bank safer Lepetit et al (2008) found that small banks' asset and default risks decreased when trading accounted for a larger share of noninterest income The literature provides little clear proof either way Diversification of income may be determined by subtracting the ratio of interest and noninterest income from total income, then dividing the result by the absolute value of the ratio g Noninterest income to total income ratio
Author employs income diversity as a control variable, per Chen et al (2017) Williams (2016) suggests that banks with more consolidated sources of income may be safer Lepetit et al (2008) found that small banks' asset and default risk decreased when trading activities accounted for a bigger share of noninterest income There is a lack of evidence in the published literature Diversification of income is determined by taking the absolute value of the ratio of interest income minus noninterest income to total income and dividing it by one h Net interest margin ratio
RESEARCH METHODOLOGY
This study uses quantitative research methods, using OLS regression models to test and analyze the impact of Fintech development on default risk The model is based on data from 2017 to 2022 for 33 commercial banks in Vietnam Accordingly, the research process consists of 3 steps:
Before building a regression model, the author makes data processing and remove incompatible variables After the Pearson results, the authors found that the model may encounter multicollinearity, thus to eliminate multicollinearity defects of the author's model to conduct defect treatment
Based on the VIF variance magnification factor in the above result, it was found that the VIF value of the ATM, FINT and INOV variables is very high (greater than 10) in which the VIF value of the ATM variable is the largest (104.56) This means that the phenomenon of multicollinearity in the above model is sure to occur
To overcome the phenomenon of multicollinearity in the research model, the authors implemented a solution to remove independent variables, with the assumption that there is no relationship between dependent variables and independent variables that are excluded from the model
First, remove the INOV variable because this is the variable that has the highest correlation with many other variables, and also remove the DFI and GDPC variables because when regressing, multicollinearity occurs, the model still occurs
39 multicollinearity with VIF value of the FINT and ATM variables are very high (greater than 10)
The author continues to remove the ATM variable because this is a variable that is correlated with another independent variable larger than the FINT variable and then performs multivariate analysis with the new model After performing the above process with the INOV and ATM variables, the final model no longer has multicollinearity with VIF values both less than 10
Next, based on previous studies and analysis, comparison and synthesis, the authors provide a theoretical framework for related issues and research gaps Then, build a direction for the research model in the following order: identify the target factors, identify the impact factors, and finally determine the relationship of the target factors and the impact
By identifying the theoretical framework as well as synthesizing and observing from previous studies, the authors collect secondary data that have been published in reports and international databases directly or indirectly related to the content of the topic From the direction for the initial model built in step 1 and the observations and data collected, the authors determine the research hypothesis and the official research model Next, the collected data will be analyzed using Stata statistical software This data analysis process includes descriptive and inferential statistics to derive results on correlations between variables, testing research hypotheses From there, the authors proceed to handle the defects of the model The new research model is proposed to perform regression analysis, giving formal research results after processing Based on the results from the model, the authors analyze and evaluate the research results
In this step, the research summarizes the evaluation and analysis of the research results, thereby making the final conclusion on the research Starting from the above foundation, contacts related to Vietnam were formed, besides, the authors proposed recommendations for developing countries like Vietnam
RESEARCH MODEL AND HYPOTHESE
Based on previous research on Fintech and default risk, combined with the review of the authors' factors suitable for inclusion in the empirical model, along with the data warehouse of humanity; the authors use the regression model with the dependent variable of Z-score index and 13 independent variables described in table 2.1
Table 2.1 Variables definition and data sources Type of variables
ZSCORE Nature logarithm of Bank Z-score
Bank Z-score = [ROA + (equity/total assets)]/Std(ROA)
Bank’s financial report Independent variable
FINT Nature logarithm of transaction value in a year
Statista database FINC Nature logarithm of number of new FinTech companies established in a year
Statista database ATM Number of total automated teller machines per 100,000 adults
INNOV The innovation index The global economy database Control
BSIZE Bank total assets Bank’s financial report AGROW Asset growth ratio measured by total assets year(t) to total assets year(t-1)
Bank’s financial report DTA Deposits to total assets ratio Bank’s financial report
LEV Debt to total assets ratio Bank’s financial report OBTD Outstanding balance to customer’s deposits ratio
Bank’s financial report DIVI The diversification index is measured as:
1 – [(Net interest income – Other operating income)/Total operating income]
Bank’s financial report NIIC Non-interest income ratio is measure as noninterest income to total income
NIM Net interest margin Bank’s financial report AFI “Access to financial institutions” is measured as the total bank branches in a year per 100,000 adults
World Bank Bank’s annual report DFI “Depth of financial institutions” is measured by The ratio of central bank assets to GDP
World Bank SBV’s reports GDPC GDP per capita is measured as the natural
The logarithm of GDP per capita in a year
To understand the impact of Fintech on default risk, the author applies panel regression to match each variable's characteristics The regression method of array data in this study has been commonly used in many other studies by some author groups such as Quang Khai Nguyen et al., (2022); Derrick W.H Fung et al., (2020); Chioma P Nwosu et al., (2020); Dao Hong Nhung et al., (2019) There are three types of array data models, namely, least squares model (OLS), random impact model (REM), and fixed impact model (FEM) Some author groups such as Quang Khai
Nguyen et al., (2022); Derrick W.H Fung et al., (2020), and Dao Hong Nhung et al.,
(2019) have applied the FEM method, while Chengming Li et al., (2022) used OLS methods There has been no other related study using the REM model
Therefore, the authors used a panel regression model based on the variables in the table as an empirical model as follows:
ZSCOREit = β0 + β1*FINTit + β2*FINCit + β3*INNOVit+ β4*BSIZEit + β5*AGROWit + β6*DTAit + β7*LEVit + β8*OBTDit + β9*DIVIit + β10*NIICit + β11*NIMit + β12*AFIit+ β13*DFIit + β14*GDPCit + eit
In which: β0: intercept βk with k = (1,14): slope i: bank i t: year t eit: residuals
In this study, the Hausman test was performed to determine whether the collected array data would be consistent with the REM model or the FEM model
The rapid development of Fintech raises many questions about the impact of this phenomenon on commercial banks’ default risk Previous studies have produced mixed results:
First, many studies back the idea that Fintech can aid in banks' risk-taking and lessen the likelihood of bankruptcy of commercial banks by allowing financial institutions to access a wealth of data to evaluate their clients' financial capabilities and, in turn, mitigate bad debt risk (Furche et al., 2017) In general, Chengming Li et al (2022) observed that a commercial bank's ability to take risks rises in proportion to the degree to which it has innovated in the field of financial technology They also found that there are two ways in which a bank's usage of FinTech raises its risk taking: increased operational revenue and a higher capital adequacy ratio This makes sense, since Fintech's enabling technologies might be used by traditional banks to provide innovative solutions to traditional financial problems The bank has the ability to improve the quality of financial services and to provide a variety of new products The bank collaborates with Fintech firms that may assist in using and expanding a
43 wider array of digital goods and services for the bank's clients, satisfying their individualized wants, boosting revenue, and lowering the likelihood of bankruptcy Fintech can optimize banking business processes and reduces overall risk levels (Xue
& Hu, 2020) In many ways, it can lower bank risks Fintech can utilize data mining, machine learning, neural networks, and other cutting-edge technologies to address non-linear issues that conventional rating models cannot manage, supply data sources references for bank credit, and improve the ability of commercial banks to grant credit Fintech may improve capital operation efficiency, assess client risks quickly, forecast capital position more precisely, and track reports in terms of the market risk management process Innovative technologies like biometrics, speech recognition, and intelligent robots may be used by fintech to manage operational risk processes more successfully, increase data accuracy, and reduce systemic risks as well as internal employee fraud risks (Fuster, Plosser, Schnabl, & Vickery, 2019) One way that banks may lower risks is by employing automated credit scoring and decision algorithms to rate critical factors, match them to their risk tolerance, and then establish a loan limit following a comprehensive examination One way in which FinTech innovation in post-loan management might reduce loan risks is through facilitating information sharing among lenders, influencing borrower behavior, and enhancing lenders' ability to analyze risk data (Livshits et al., 2016; Sutherland,
2018) However, if banks make use of pertinent machine learning and big data, they may be able to swiftly detect irregularities in fund consumption and other potential default indicators
Second, in contrast to the results of some of the above studies, the research paper by Quang Khai Nguyen et al., (2022) states that the development of Fintech has a positive impact on commercial banks’ ability to take risk in Vietnam because in this country, the regulations on Fintech have not been finalized This makes it challenging for regulators to run and oversee fintech services, which increases a variety of hazards in the business operations of fintech firms Next, one of the issues Vietnam faces has to do with cybersecurity and technological infrastructure The financial system is increasingly vulnerable to cyberattacks the more it depends on electronic platforms and digital data, according to best practices from prior Fintech
44 nations, and one event can increase the danger to the system as a whole Fintech may also play a role in asymmetric information, adversarial selection, and ethical concerns in the financial markets, which are the main factors contributing to the banking sector's growing vulnerability In addition, the rapid development of fintech will increase the level of information technology dependence among market participants and market infrastructure, which can push technology incidents into a system crisis, from which banks can face default risks
Third, commercial banks already face competition from the rapidly growing fintech industry because most of these businesses are now engaged in the payment and lending sectors, two of the bank's primary business divisions When Phan et al
(2019) came to the conclusion that the operation of Fintech firms has a detrimental impact on the business performance of commercial banks, the profitability and financial status of commercial banks may be at danger According to Aaker & Keller's customer Theory from 1990, new services (like those offered by Fintech companies) that match the same customer demands might take the place of older ones (like those offered by conventional commercial banks) The danger of bankruptcy increases if the economic condition remains uncertain for an extended length of time, which will reduce the bank's profitability Another scenario that may develop and endanger the bank's operations is when the bank's partner, a Fintech firm, is in danger The danger that arises in Fintech businesses might transfer to banks and their other partners because of the two sides' tight collaboration
As such, there are clear arguments about Fintech's impact on commercial bank’s default risk However, the role of Fintech development on lower commercial banks’ probability to defaut risk is shown in many different aspects such as: risk management and business efficiency Therefore, the author hypothesizes:
H1a: FinTech is negatively associated with default risk of commercial banks H1b: FinTech is positively associated with default risk of commercial banks H2: Fintech start-ups is positively correlated with commercial banks’ default risk
RESEARCH RESULTS AND RECOMMENDATIONS
ANALYSIS OF THE SITUATION OF VIETNAM COMMERCIAL
From 2017-2022, the system of commercial banks continued to achieve positive business results, their financial capacity, asset quality and bad debt handling were all improved; system liquidity was maintained stably; sense of law compliance and quality of governance and administration were improved The operation of the system of commercial banks continued to improve, the total assets of the system over the researched period increases year by year In particular, 2022 witnessed the highest increase in total assets in the research period with 2,314.8 trillion VND, equivalent to 14.5% in comparision with the previous years The period 2020-2022 is also the period when the total assets of the group of joint-stock commercial banks are larger than the total assets of the group of state-owned commercial banks This is due to the growth rate of total assets of the group of joint-stock commercial banks through 2020-
2021, respectively, at 16.13% and 18.16%; while that index in the group of state- owned commercial banks tends to be lower at 6.47% and 11.39% By 2022, the growth rate of the group of state-owned commercial banks is 19.03%, this value is higher than that of the group of joint stock commercial banks, which reaches 11.86% Finally, for joint-venture, foreign commercial banks, except for 2020, the total asset growth rate of 13.14% is higher than that of the state commercial banks, in the period 2021-2022, the total asset increase of this bank group is the smallest at 7.02% and 8.67%, respectively
Table 3.1 Total assets of Vietnam's commercial banking system in the period
(Source: State bank of Vietnam)
By the end of 2021, the banking system has 97 banks in total, including 07 State-owned Banks, of which: 01 bank with 100% state capital, 03 State-owned Banks with over 50% of charter capital, 03 State-owned Joint Stock Commercial Banks bought back at the price of 0 dong; 28 Joint Stock Commercial Banks; 09 banks with 100% Foreign capital; 02 Joint Venture Banks; 51 Foreign Bank Branches (The State Bank of Vietnam, 2121) In terms of market share, in the research period, four state banks accounted for over 50% of the market share In particular, Vietnam Bank for Agriculture and Rural Development (Agribank) leads in total assets in the group of state-owned commercial banks with nearly 1.9 million billion VND Military Joint Stock Commercial Bank (MBB) leads the group of private equity commercial banks, with total assets of VND 789,906 billion (Commercial banks’ financial statements, 2022) Similar to capital mobilization and outstanding loans, the group of state-owned commercial banks also accounts for over 50% of the market share of the whole industry because these are banks with a long and stable history of operation Regarding capital mobilization, Agribank continues to be the leading bank with a customer deposit balance of VND 1.6 million billion (Commercial banks’ financial statements, 2022) In the group of private commercial banks, STB leads the group with a deposit balance of VND 454.740 billion (Commercial banks’ financial statements, 2022) In terms of outstanding loans, BID is the leading bank with a loan balance of more than VND 1.5 million billion, while MBB leads in the group of private commercial banks with a deposit balance of VND 460,574 billion (Commercial banks’ financial statements, 2022)
Table 3.2 Banking system of Vietnam in 2021
(Source: State bank of Vietnam)
Data on financial statements showed that during the research period, profit targets of commercial banks including NIM, ROA, ROE increased at a fairly stable rate, mainly due to high revenue from credit activities In 2020, despite being heavily affected by the Covid-19 pandemic, the pre-tax profit of banks still increased compared to 2019 (up to 16.12%) (The State Bank of Vietnam, 2022) The main reason for this profit growth is due to the characteristics of the banking industry, in the context of the epidemic, people and businesses using non-cash payment services increased sharply (The State Bank of Vietnam, 2022) Moreover, in order to stimulate the demand for loans and support the development of the economy in this period, the lending interest rate of commercial banks tends to decrease, the interest rate adjusted for some subjects and some areas of production and business borrowing, so the NIM index increases over the years, showing that the bank's interest income is higher than the interest payable to customers According to the State Bank of Vietnam's annual report for the period 2018 - 2021, the epidemic also creates opportunities for the banking industry to pioneer in the application of science and technology, leading to the downsizing of payroll that may be the cause of increased bank profits (The State Bank of Vietnam, 2022) Especially in 2022, the ROA of commercial banks was severely reduced to the lowest level in the period of 0.95%; However, thanks to the financial leverage effect in the industry, ROE increased quite strongly, specifically in this year, ROE reached 20.2% (Vu Thi Thanh Thuy and Vu Thi Anh Tuyet, 2023) This is explained by the theory of the trade-off relationship between risk - expected return according to the portfolio theory When a commercial bank improves its equity ratio, the overall risk of the bank is minimized, so that the expected profitability is not as high as in the case of a lower equity ratio or in other words, the use of greater financial leverage (Vu Thi Thanh Thuy and Vu Thi Anh Tuyet, 2023)
Besides, capital adequacy ratio (CAR) is one of the important indicators when evaluating the safety and risk management activities of banks Currently, the car coefficient is calculated according to Circular No 41 of 2016 approaching Basel II international standards, with a minimum of 8% According to the recent update of the State Bank to the end of October 2022, the total equity capital of State-owned commercial banks applying Circular 41 is VND 422,786 billion, an increase of
15.23% compared to the beginning of the year (The State Bank of Vietnam, 2022) The capital adequacy ratio (car) is at 9.04%, while the total equity capital of joint- stock commercial banks that have applied Circular 41 is 722,854 billion VND, up 18.52% compared to the beginning of the year and has a capital adequacy ratio much higher than that of State commercial banks, reaching 12.29% (Minh Vy, 2022) Foreign banks operating in Vietnam also have a higher capital adequacy ratio, reaching 18.61%, of which, Shinhan Bank Vietnam, the capital adequacy ratio on June 30, 2022 reached 17.13%; HSBC Vietnam also announced that the car reached 16.32%, much higher than the majority of state-owned banks and joint-stock banks (Minh Vy, 2022) In addition, 3 other banks with car of 15% or more (updated to the end of quarter 3/2022) are TCB (15.7%), HDBank (15.3%), VPBank (15%) (Minh
Vy, 2022) State-owned banks such as VCB, CTG and BID are currently the state- owned commercial banks with the lowest capital adequacy ratio among banks listed on HOSE, but still meet the requirement of over 8% (Minh Vy, 2022)
Table 3.3 Summary of performance indicators of commercial banks in the period 2017-2022
(Source: State Bank of Vietnam and Author’s calculation)
Next, in terms of credit risk, 2017 witnessed the highest NPL of 9.5%, however, on-balance NPL was 1.99%, which was improved and lower than that ratio of 2.46% in 2016 From 2018-2022, the ratio of NPL fluctuated from 3.69% to 4.43%, especially that ratio of 2018 is relatively lower than 10,8% in 2016 (Vu Th Thanh Thuy and Vu Thi Anh Tuyet, 2023) However, 2019 experienced an increase in NPL to 4.43%, then that ratio decreased in the next period of 2020-2022 In specific, In
2021, the whole system of credit institutions handled 151.95 trillion VND of bad
49 debts, of which credit institutions used risk provisions to handle accounted for 47.1%, customers paid 30.2% and sold to VAMC accounted for 12.6% (The State Bank of Vietnam, 2021) Regarding bad debt settlement data according to Resolution No 42/2017/QH14 of the National Assembly, accumulated from August 15, 2017 to the end of December 2021, the whole system of credit institutions has handled 380.2 trillion VND of bad debt (The State Bank of Vietnam, 2021) Overall, with efforts to control NPL through debt restructuring and debt sale to VAMC, improving credit quality, from process to investment in infrastructure of Vietnam's commercial banking system, debt ratio bad has basically decreased, but still above 3% In addition, In general, in terms of liquidity risk, in the period 2017-2022, the liquidity of the whole banking system has remained stable, the management efficiency has improved over the years Therefore, the liquidity of the whole system is guaranteed, foreign currency transactions take place smoothly, legitimate foreign currency needs are fully and timely met The average liquidity reserve ratio reached 17.95%; most credit institutions reached the ratios and operational safety limits in accordance with the law (State Bank of Vietnam, 2021)
Chart 3.1 Ratio of non-performing loan and on-balance non-performing loan of commercial banks in the period 2017-2022
(Source: State Bank of Vietnam and Author’s calculation)
FINTECH DEVELOPMENT SITUATION IN VIETNAM
3.2.1 Overview of Fintech development in Vietnam
Fintech firms with the following major features emerged in Vietnam's financial sector in 2017 and expanded rapidly through 2022:
First, the financial technology industry is expanding rapidly
Many foreign investors are drawn to Vietnam because of its promising Fintech industry According to Statista's database (2023), the number of fintech firms in Vietnam has expanded from about 33 at the end of 2012 to over 263 as of September
2022 About 70% of Vietnam's Fintech firms are themselves startups Increases in the number of Fintech firms operating in Vietnam over time indicate the country's potential as a market for this sector
Chart 3.2 Number of Fintech companies operating in Vietnam in 2012-
In addition, according to research by Solidiance - a leading strategic consulting firm, Vietnam's Fintech market reached $4.4 billion in transaction value in 2017 and reached about $7.8 billion in 2020, equivalent to a 77% increase within 03 years (Tuyet & Thuy, 2021) 2021 has witnessed the leap of Vietnam's Fintech market when the Internet economy reached a value of 21 billion USD, standing at position 14/50 in Asia and position 70 on the global rankings (Le, 2022)
Second, payments and P2P lending are the most lucrative segments of the FinTech industry
Two services—payment and P2P lending—comprise the bulk of Vietnam's FinTech business right now Asset management, liquidity management, investment management, insurance, automated financial advice services, etc are all in their infancy and have a long way to go
Chart 3.3 Fintech startup companies and fields of activity over the years
Approximately 115 Fintech Startups will be active in Vietnam by the end of
2020, as seen in Chart 3.3 These businesses will focus on areas such as non-cash payments, peer-to-peer lending, digital banking, asset management, blockchain and virtual currency, data management, and credit scoring There are more businesses providing payment services than any other service category, with P2P lending a close second Advantages including a sizable population, government encouragement of cashless transactions, and widespread access to the internet and mobile devices are propelling fintech startups in Vietnam's payments industry This is also true of established Fintech marketplaces, especially those in their formative years (Vu Cam Nhung and Lai Cao Mai Phuong, 2021)
Third, the size of fintech companies is relatively modest
Vietnam's fintech industry is still in its infancy, thus most businesses are very small According to a study from the State Bank of Vietnam's survey in 2021, most Fintech businesses in Vietnam are young, local startups Specifically, 47% are in the early stages of business and have not yet reached the break-even point; 28% have
52 launched a minimum viable product and generated sales revenue in the preceding six months as of the survey's release; 13% are developing a business model; 9% have generated profits; and 3% are still proving their ideas but have not yet generated any revenue (The State Bank of Vietnam, 2021)
As a fourth point, most Fintech endeavors take place inside the financial industry and involve substantial engagement with conventional banks
According to the 2019 Fintech Ecosystem Survey Report published by the State Bank of Vietnam, the vast majority of Fintech services provided in Vietnam are either directly related to the banking industry or have the nature of banking activities like payment, lending, capital mobilization, personal financial services, credit scoring, or solutions applied to the operation of credit institutions Despite the increased rivalry brought on by technological advancements, banks and fintech firms may also benefit from working together Collaboration between established financial institutions and fintech companies has emerged as a major trend on a worldwide scale in recent years Most Vietnamese Fintech firms are moving in the direction of cooperation, becoming partners with traditional financial institutions (banks, insurance, securities ), but a select few are developing independently and could become competitors (Kieu Huu Thien et al., 2021) According to data collected by the State Bank of Vietnam in 2019, eighty percent to ninety percent of Fintechs in Vietnam work in tandem with traditional financial institutions Because banks and fintech companies both have their own advantages, cooperation between fintech companies and banks can accelerate the process of reforming the banking system (Infosys, 2018) Many new, digitized services have been implemented by Vietnamese banks: Tien Phong Commercial Joint Stock Bank (TPBank) with LiveBank automatic bank, this service helps to open an account just by taking fingerprints with biometric technology to enhance security and automatic operation process and OCR technology (optical character recognition) Vietnam Prosperity Commercial Joint Stock Bank (VPBank) with Timo digital banking application, Vietnam Foreign Trade Commercial Joint Stock Bank (Vietcombank) with Digital Lab digital banking space, Military Commercial Joint Stock Bank (MB) with ChatBot virtual assistant application for 24x7 on social networks
3.2.2 The development of Fintech products in Vietnam
Currently, payment is the most popular Fintech service in Vietnam Most banks already offer Internet Banking, Mobile Banking, other non-cash payment services (card transactions, ATM & POS transactions, e-wallet) to provide various payment options to customers, and the internal banking process is also computerized
In particular, by the end of the first quarter of 2019, in Vietnam, there were 24 banks with QR code payment services with more than 50,000 acceptance points, 76 banks providing Internet Banking services and 44 banks with Mobile Banking services (Nguyen Vu, 2019) At the same time, by the end of the first quarter of 2019, the volume and scale of transactions through Internet Banking were 204.22 million transactions (up 60.64% compared to the first quarter of 2018) and VND 9,500 trillion (up 18.5% compared to the first quarter of 2018), the volume and scale of transactions through Mobile Banking were 169.86 million transactions (up 109.48% compared to the first quarter of 2018) and VND 1,761 trillion (up 160.5% compared to the first quarter of 2018), respectively (Tran Nguyen Minh Hai, 2020) This shows that electronic payment is growing very fast, especially Internet Banking/Mobile Banking and mobile payment services
There will be over $9 billion worth of transactions in the Vietnamese Fintech sector by 2020 (Tran Trong Triet, 2020), up from $4.4 billion in 2017 Vietnam's 51st place on the list of global fintech hubs is positive when compared to other nations with developing fintech marketplaces (Findexable, 2019) The COVID-19 pandemic and the government's social-distance directives are two major factors that have influenced a shift in Vietnamese consumers' buying habits With mobile banking transactions increasing by about 150% annually and e-wallet and mobile device payments increasing by over 160% and 125%, respectively, cashless payments are thriving (Phan Thi Hoang Yen et al., 2021) According to data from the Vietnam National Payment Joint Stock Company (Napas) as of December 31, 2020, the total number of non-cash payment transactions processed through their system increased by 76% compared to 2019, and the total transaction value increased by 124%
54 compared to 2019 (Phan Thi Hoang Yen et al., 2021) Allied Market Research predicts that Vietnam's mobile payments market could reach $70.9 million by 2025
Chart 3.4 Value of electronic transactions in Vietnam in the period 2012-
(Source: Statista, Iris Report) 3.2.2.2 Peer to peer lending
P2P Lending activities in the Vietnamese market are in the development stage, need to be improved in all aspects, need to be under the close management of the regulator In the coming time, P2P Lending activities in Vietnam are expected to contribute to supporting financial universalization, expanding capacity and creating more access to financial resources and ways of lending to the economy, especially for vulnerable people in society who have access to the Internet, thereby contributing to repelling "black credit"
In recent years, the banking and finance sector in Vietnam has witnessed strong growth of fintech companies In Vietnam, there has been a company operating like P2P Lending since 2016 with Huydong.com Since then, many other P2P Lending companies have gradually come into operation, such as Tima, SHA, Mobivi, Vaymuon.vn, Mofin, etc Particularly for the P2P Lending sector, in Vietnam currently, through a preliminary survey, there are about 100 P2P Lending companies (including companies that have gone into official operation and some companies are
55 in the testing phase) In particular, some companies originate from China, Russia, Indonesia, Singapore This shows that foreign investors are very interested in Vietnam's consumer lending market and see this as a potential market for P2P Lending to develop As some countries in Asia tightenen the management of P2P Lending activities (China, Indonesia, Singapore ), foreign companies, especially Chinese P2P Lending companies, are looking to shift their operations to the Vietnamese market However, the actual number of P2P Lending enterprises is very difficult to be accurate because currently management agencies in Vietnam have not organized official statistics of information related to enterprises implementing P2P Lending activities in Vietnam (number, name, scale, actual operation, scope, field of operation ), because this activity does not currently have a regulated legal framework (Bui Thuy Hang et al., 2022)
Innovations in the field of P2P lending have brought great benefits to financial and banking institutions by complementing, improving or solving inefficiencies in current financial products and services such as time limits, transaction locations, customer contact points according to physical channels, customer identification and authentication (KYC), as well as relatively complex transaction procedures P2P Lending also plays an important role in creating more access to financial resources, ways of lending to the economy, promoting national financial inclusion, contributing to financial universalization through increasing access to financial services for a part of the population who do not have an unbanked account or have difficulty accessing traditional financial and banking services (underbanked), people in remote areas have little access to traditional banking services
Up to now, the segment of fintech companies investing and managing assets in Vietnam is quite exciting when a series of names such as Finhay, Infina, StockBook, Fmarket This rapid emergence is not incomprehensible for a booming stock market By the end of October 2021, more than 3.86 million securities accounts were opened, equivalent to 100,000 new accounts entering the market each month (Vietnam Securities Depository, 2021) The number of new players is expected to continue to increase The representative of Dragon Capital Vietnam predicted that
EXPERIMENTAL RESEARCH RESULTS
The study uses data from a subset of 33 Vietnamese commercial banks that were open for business on a regular basis from 2017 to 2022 These 33 banks' audited financial statements provided the secondary data utilized to generate variables in the study model There are 198 observations total in table 13 Table 13 provides a quick summary of the variables' descriptive statistics
The data in table 3.4 shows that the average value of the natural logarithm Z- score among the samples of banks included in the study was 3,63332 In particular, the bank with the lowest default risk was PBVN bank in 2019 with a log(Z-score) of 5.952973 (Z-score value equal to 384.90), in contrast, the bank recorded the lowest log(Z-score) of 2.263423 (Z-score value equal to 9.62) as Seabank in 2017
Next, the descriptive statistics table 13 shows that indicators representing Fintech development tend to increase over the years The natural logarithmic average value of the number of Fintech companies established each year from 2017-2022 is 3.1122 In particular, the largest log value (FINC) reached 5.9529, showing the highest number of newly established Fintech companies in 2021, namely 71 companies Next, the natural logarithmic average value of electronic transactions is 10.04081, of which in 2022 the highest value of electronic transactions is 18.06 billion USD Next, the average number of ATMs per 100,000 adults is 26.06 This figure reached its lowest value of 24.13 in 2017 and gradually increased over time, finally reaching its highest value of 27.7 in 2022 The variable representing the development of Fintech is followed by the innovation and development indicator The index averaged 37.23333, the highest value reaching 38.8 in 2019 In the research period, Vietnam is always in the top 50 countries with the highest innovation and development index in the world, the highest ranking is 42 in 2019
Next, the descriptive statistics also show the indicators showing the default risk of the group of banks Firstly, the BSIZE bank-scale logarithm of the study sample has an average value of 14,255 The largest value belongs to BIDV Bank in
2022 and the smallest bank is PBVN Bank in 2017 Second, the average growth rate of total assets of banks is 16.2246% Of which, the largest value bank reached 46,3307% is KLB's bank in 2021 and the bank reached the smallest value -33,382% is ANZB in 2022 Third, the ratio of deposits to total assets averaged 78,5953%, the bank with the highest ratio is KLB in 2021 reached 92.0637% and the bank with the lowest value is VPB in 2017 Fourth, the ratio of outstanding loans to total assets of the research sample has an average value of 90,4854%, with the largest value of 95,93823% belonging to BIDV in 2017 and the smallest value from BaoVietBank with 31,8236% in 2020 Next, in 2020, ANZB achieved the smallest ratio of outstanding loans to deposits with 21.46%, VPB bank achieved the highest ratio with 146.91% in 2021 while the average value of this variable was only 91.0477% Next, the average income diversification index reached -0,5002, the highest value was 1.2934 of BaoVietBank in 2020 and KLB reached the lowest value of -5,084 in 2019 Next, the bank with the highest rate of interest-free income is VBB with a value of
2.5954 in 2020, the lowest value is -0.3615 of VAB in 2017 while the average value is 0.5038 Finally, for NIM, the bank with the highest index is VPB in 2019 with 9.41% while BaoVietBank in 2020 has the lowest index with only 0.0552%
Finally, with the macro data group, the AFI index shows the number of commercial bank branches averaging 100,000 adults reaching an average value of 3,5333, the largest value reaching 4 in 2019-2020 and the smallest value reaching 2.9 in 2021 Next, the DFI shows the ratio of central bank assets to GDP reaching the lowest value of 0.0017 in 2019 with the highest value of 0.0191 in 2019, while the average value is only 0.0063 For log(GDPC) (the natural logarithm of GDP per capita), this value increased steadily over the years from 8 in 2017 to the largest value reaching 8.33 in 2022, the average value reaching 8,1672
3.3.2 Results of measuring default risk of the commercial banking system
Based on the calculation formula used by Bourkhis and Nabi (2013), Beck et al (2013) to calculate Z-score to measure default risk of banks, the author calculated Z-score index for 33 commercial banks in the period of 2017 – 2022 based on secondary data collected from audited annual financial statements of banks Accordingly, VIDBank in 2014 had the highest Z-score of 384.9, in contrast, the bank recorded the lowest Z-score of 9.62 as SSB in 2017 The average Z-score in this period of commercial banks is 53.53, higher than the 32.65 in the period 2005-2013 (Hoang Cong Gia Khanh & Tran Hung Son, 2015) and 37.03 in the period 2014-2018 (Nguyen Quy Quoc, 2020) In addition, compared to similar studies conducted in countries and regions around the world, the average Z-score level of 53.53 is higher than the level of 30.59 of the Asia-Pacific region in the period 2003-2009 (Fu & ctg,
2014), the average 41.78 of 12 Asian countries in the period 2001-2007 (Soedarmono
& ctg, 2011), and the level of 46.50 of OECD commercial banks in the period 1994-
2004 (Hesse & Cihák, 2007) However, this average Z-score of 53.53 is still much lower than the average of 86.57 of banks in 12 European countries operating stably in the 2008-2011 period (Chiaramonte & ctg, 2015) It can be seen that the operation of the Vietnamese commercial banking system in recent years has improved more stable than banks both domestically and in other countries during the period affected
60 by the US-China trade war and the epidemic, but it is still more unstable than countries with developed economies like in Europe
Chart 3.5 Average Z-score of Vietnamese commercial banks in the period of
The chart 3.5 shows the volatility of default risk of commercial banks in Vietnam in the period of 2017 – 2022 It can be said that the Z-score of commercial banks in this period fluctuates over the years Starting at a fairly high Z-score in 2017 with 51.72 points, the index steadily declined to 51.17 in 2018 This slight decline can be explained by the impact of the US-China trade war, however, it can be said that this is a slight decrease that shows that the Vietnamese banking market is still quite stable Next to 2019, this index increased to 55.23, which is a positive sign when this year recorded Vietnam's economic growth of over 7% In addition, this year, Vietnam and the EU signed a Free Trade Agreement and an Investment Protection Agreement after nine years of negotiation, which is a good sign for exports in particular and the whole economy in general However, 2020 saw a sharp decline of this index to 53.19, which is also a general trend of the world economy In 2021-
2022, the average Z-score index has gradually increased and reached 54.7 and 55.14, respectively Until 2021, the world economy is still under threat from the Covid-19 epidemic, but the average Z-score of the banking industry is still increasing, showing Vietnam's reasonable management and policy capacity Continuing the momentum
61 of 2021, this index still increases in 2022, this is also the year when Vietnam's banking industry has many outstanding events to ensure the default risk of this industry group such as promoting digital transformation, promoting non-cash payment, reducing deposit interest rates, increasing charter capital, etc In general, the general trend of the Z-score index of Vietnamese commercial banks over the years has increased, but there are still many fluctuations, showing that the operation of the Vietnamese commercial banking system is still in the process of finalizing mechanisms and management policies
Chart 3.6 Z-score of the average commercial banks in Vietnam in the period
The chart 3.6 describes the average Z-score of 33 banks from 2017 to 2022, of which the five banks with the highest Z-score are Public Bank Vietnam with 338.93, followed by 4 banks NCB, Bab, Shinhan Bank, SGB with 213.93, 105.30, 86.64, 82.34 The bank with the lowest average Z-score in the study sample is MSB with 15.53; this is explained by the decline in the equity-to-asset ratio in 2019, besides the fairly high ROA standard deviation, which makes the bank's default risk throughout the 5-year period very high In contrast, Public Bank Vietnam has a relatively low standard deviation of ROA for 5 years plus the equity ratio on EA assets is always maintained at a high level of 19%-26%, so this bank receives the highest Z-score in the study sample
3.3.2.1 The comparison of default risk of three different groups of banks with different forms of ownership
Chart 3.7 Average Z-score of State-owned commercial banks, joint-stock commercial banks, and foreign commercial banks in 2017-2022
State-owned commercial banks, Joint-stock commercial banks, and Foreign commercial banks In particular, the state-owned commercial banks include 4 banks: Agribank , CTG, VCB, BID; Foreign commercial banks include 4 banks: ANZ Bank, Shinhan Bank, Public Bank Vietnam, HSBC; The remaining 25 banks belong to the joint-stock commercial banks In general, in the whole period, the average Z-score of the two groups of state-owned commercial banks and shares tended to increase, but the group of private banks still had lower default risk than the group of state-owned commercial banks In contrast, the Z-score of foreign commercial banks tends to decrease Specifically, in 2018, while the group of state-owned commercial banks and shares with Z-score increased slightly from 27.35 to 28.69 and 44.74 to 45.85, the group of foreign commercial banks dropped sharply from 119.7 to 106.93 In particular, the Z-score of foreign commercial banks increased significantly to 129.98, while the Z-score of the other two banking groups increased slightly to 46.91 and 33.18, respectively, this is also the year that recorded two notable world economic events, namely the FED reducing interest rates and the US-China trade war gradually cooling down, creating conditions for the development of the banking industry
Specifically, while the Z-score of the group of joint-stock commercial banks tends to increase steadily over three years, the group of state-owned commercial banks decreases in 2020 then increases in the next two years, while the group of foreign commercial banks records Z-score tends to decrease Conversely, except for 2020, the Z-score of the group of joint-stock commercial banks decreased to 45.24 probably due to the impact of the epidemic, the remaining period of Z-score gradually increased and reached 50.9 in 2022, which shows that the business situation of this group has gradually stabilized after the pandemic Compared to the two groups mentioned above, the group of state commercial banks has quite a lot of fluctuations in Z-score when increasing in 2020 to 33.58, then decreasing slightly and reaching 33.41 in 2021, and finally increasing and reaching 34.17 in 2022
DISCUSSING RESEARCH RESULTS
Initially, Fintech has positively enhanced on bank’s performance and reduce bank’s default risk The regression coefficient of the variable deposit to total assets (DTA), outstanding balance to deposit (OBTD), and net interest margin (NIM) is 0.445; 0.4732; and 0.4732 respectively and is statistically significant at 1% shows that there is a positive correlation between the ratio of deposit over total assets, outstanding balance to deposit, and net interest margin of commercial banks and Z- score index, indicating that Fintech offers cutting-edge technologies to enhance bank’s core business operations
In terms of deposit to total assets, mobilized deposits account for a large proportion of the bank's total assets A bank with a high amount of customer deposits shows the credibility and credibility of that bank in the customer's perspective, and also shows that the bank's performance is good and there is less risk of default Especially, in the period of digital transformation, customers are always looking for smarter, safer and faster technology, so Fintech application helps banks to automate faster, provide personalized services and better customer needs Customers who make
70 transactions directly with the bank reduced (39.7%) instead, they made transactions through digital banks (22.1%) (Le Thanh, 2020) Therefore, the race for digital transformation is increasingly fierce, which banks want to retain market share and competitive advantage must focus on the strategy of digital banking development Currently, digital banking helps customers make financial transactions quickly and conveniently, from account opening to registration, issuance, management of products and services such as accounts, credit cards, loans Moreover, recently, banks attract customers to deposit money through the provision of online savings deposit products with higher interest rates than the traditional form In specific, the highest online deposit interest rate can be up to more than 9%/year while the traditional deposit interest rate ranges from 3.95% - 8.05%/year (Nguyen Huong Ly and Ha Ly, 2023) This helps banks increase liquidity, reduce the risk of default
Next, in terms of outstanding balance to deposit, when this ratio is high, the bank makes good use of its capital, which will cause the bank's income to increase, but it means that the bank must strengthen risk control Currently, the application of technology in credit risk management in banks is becoming more and more popular Fintech helps banks make lending decisions using a variety of factors that help them more accurately evaluate traditional assessment methods One specific example is the application of machine learning to analyze the repayment capacity of borrowers based on personal data such as income level, age, residence, spending level, etc In addition, banks also use AI technology in customer credit scoring to make accurate lending decisions, helping to minimize credit risk In addition, cloud computing technology helps the bank store information of borrowers, all information is fully and transparently censored and publicized, so the bank can control customers' loan records This helps minimize the risk of information asymmetry leading to adverse selection risks when banks lend to the wrong objects This conclusion is also similar to the studies of Nguyen Thanh Thien (2019); Vo Minh Long (2019)
Next, in terms of net interest margin, a high NIM represents higher income from lending than from paying savings deposits, making the bank more profitable In addition, NIM is also an effective measure to assess the profitability of a bank's cash flow Fintech applications help banks attract borrowers through the provision of
71 online lending products Currently, this form is favored by customers because of its quickness and convenience when borrowers do not need collateral and are disbursed quickly Currently, banks are collaborating with technology companies to offer financial supply chain solutions, one of the typical examples is TASConnect Since its launch in February 2022, the TASConnect solution has integrated with large enterprises and banks in the region, helping to process more than 250,000 transactions and reach a total transaction value of over 13 billion USD (Phuong Thu, 2023) Therefore, this solution brings benefit for bank as income from lending helps the bank to ensure the operation situation, as well as balance the costs of investing in technology application It can be said that in this aspect, technology helps banks increase income, thereby minimizing the risk of default This conclusion is also consistent with the study of Quang Khai Nguyen et al., (2022)
Secondly, in contract to the first aspect, Fintech can also increase bank’s default risk The regression coefficient of the variable debt to capital (LEV) is -1.334 and is statistically significant at 1% shows that there is a negative correlation between the ratio of debt to capital and Z-score index, indicating that the application of Fintech can increase the probability to bank’s default risk The bank is a special financial institution because customer deposits account for a large proportion of the total liabilities of the bank, the rest is debt owed to the state bank and other financial institutions With such large amount of deposits, the bank may have to pay more interest to depositors if interest rate risk occurs Besides, the application of technology can also be used to explain this correlation One of the shortcomings when investing in technology is that the bank's activities will depend on third parties, and the risks in terms of security and information security for customers also appear If there is a sudden technological incident and affects customers, they can quickly withdraw deposits from the bank, from which liquidity risks can occur, leading to the risk of default when the bank cannot collect debts from lending to businesses to pay customers This conclusion is similar to previous studies by Fahlenbrach et al., (2012); Ho et al., (2016)
Last but not least, Fintech start-ups can negatively impact commercial banks’ business and increase bank’s probability to face up with risks Eventhough the
72 statistically significant is over 10%, the regression coefficient of the (FINC) variable is -0.0093 still shows that the number of new Fintech companies established in the year increases, the Z-score decreases, or the bank’s default risk increses In Vietnam, there is no clear legal framework for fintech products, services as well as operating models of fintech companies, so regulators face difficulties in operating and managing fintech services Especially, most Fintech companies in Vietnam are developing payment and lending activities, which are core business activities of commercial banks This threatens the bank's business because Fintech is also considered a potential competitor when it has advantages in technology and easily provides services to customers quickly and conveniently Currently, Fintech enterprises in Vietnam are mainly associated with commercial banks to jointly provide products and services (72%), only about 14% develop new products and services and 14% are ready to compete compete with commercial banks in providing banking services (Tran Thi Kim Chi, 2021) However, this cooperation may not only be temporary, but can also bring some potential risks for the bank The first risk is that Fintech companies may gradually dominate in providing products and services, banks may lose a large number of customers to Fintech companies The second risk is the risk of contagion when the Fintech company's partner is at risk, the bank is also affected and may face the risk of default This result is consistent with previous studies by Derrick W.H Fung et al., (2020), Quang Khai Nguyen et al., (2022).
RECOMMENDATIONS
Recommendations for state management agencies
Firstly, it is important to keep working to strengthen Fintech's regulatory environment There must be rules and regulations in place for the Fintech ecosystem, with a primary focus on creating a legal corridor for the provision of Fintech services/products and secondary focus on regulating the standards of the product and service portfolio to ensure that Fintech firms can conduct their operations openly and honestly Additionally, the growth of the financial-banking system and the direction of overall financial development should inform the construction of a development strategy, development policy, and Fintech development vision
Secondly, unifying and raising awareness of information security, identifying the work of ensuring information security and security is an indispensable requirement to ensure the security and order of society On June 12, 2018, the Law on Cyber Security was promulgated, including regulations on activities to protect national security and ensure social order and safety in cyberspace, but the guidance and implementation should be more drastic and continuous The legal system and regulations on information security and consumer protection need to be further improved
Third, establishing a reliable monetary framework, which will allow for the growth of several cutting-edge goods, services, and distribution mechanisms To achieve this goal, it is essential to implement technological improvements that are in line with the current scientific and technical base, and to implement a policy of continuous training aimed at increasing the number of qualified individuals who are able to operate and master increasingly sophisticated computer systems and databases while maintaining a high level of operational security Management of the systems that keep an eye on everything is also crucial The State Bank must follow and abide by the principles of payment system monitoring, such as those pertaining to: Transparency and uniformity in terms of monitoring criteria; Pursuing and implementing in accordance with international common standards on monitoring the payment system, such as the CPSS norms for financial market infrastructures (PFMIs) in 2012 or the IOSCO principles of monitoring the securities transaction payment system; Ensuring close coordination and rapid response between the State Bank and relevant agencie
Fourth, the State Bank should adopt policies to encourage the commercial banking industry to work together to advance banking technology In light of the ongoing process of competition between local and foreign banks, it is clear that advancements in banking technology are crucial in the sphere of currency security Take full use of your successes
Fifth, improve individuals' access to and responsible use of financial services by increasing their training in financial skills and ability Simultaneously, the government must steer and create laws to undertake action programs on financial
74 education to reach diverse audiences including schools and campaigns to enhance financial literacy for the general public Publicizing financial education and information helps raise consciousness about the need for greater financial literacy among the general public in order to increase their usage of financial services
Firstly, when applying Fintech into operation, banks need to ensure that they build a controlled environment and apply adequate and comprehensive risk management processes The safety, soundness and stability of the bank can be enhanced by implementing monitoring programs to ensure that the bank has an effective governance structure and clear risk management processes, thereby appropriately managing and monitoring the risks arising related to Fintech
Secondly, banks should maintain controls on outsourced services to the same standards as those undertaken internally and implement appropriate risk management policies for any operations outsourced or assisted by third parties, such as fintech businesses
Finally, banks should focus on upgrading technology infrastructure, improving technology security systems to ensure safety and security for the system
As mentioned above, cybersecurity risks are always associated with technology products and services, so financial institutions need to pay special attention to building a data backup system to backup and restore data after possible incidents and disasters; strengthen cooperation with technology solution providers and Fintech companies on a mutually beneficial basis
1 Main results of the study
The purpose of the study is to quantitatively analyze the influence of fintech development on default risk of commercial banks by examining factors representing the development of Fintech In addition, the study offers recommendations for Vietnam in developing digital transformation to take advantage of the benefits, and limit the harm that Fintech brings
The project used data of 33 commercial banks in the period of 2017 – 2022, a total of 198 observations in the study sample, to calculate the internal variables of banks, then empirically calculated the default risk of Vietnamese commercial banks using the Z-score index The results show that the Z-score of Vietnamese commercial banks in general tends to increase year by year in the period of 2017-2022 In addition, the study also examines trends and differences in default risk of both commercial banking system and banking groups according to the following criteria: capital, no state capital, and foreign; listed and unlisted on the stock exchange Specifically, the group of private banks has lower average default risk than state- owned commercial banks, while the group of banks that have not been listed on the stock exchange is higher than the group of banks that have been listed and all tend to increase
A quantitative research approach has been implemented to determine the extent to which Fintech influences default risk The first step after running the VIF test, the author discovered that the model has a multicollinearity phenomenon that can affect the results of the study, so the variables depth of financial institution (DFI), gross domestic product per capita (GDPC), the innovation index (INNOV), and the number of automated teller machines per 100,000 (ATMS) were excluded from the model The researchers then conducted a panel data analysis and found that the random impact model matched a 6-year panel data set from 33 commercial banks
Bank total asset (BSIZE), asset growth (AGROW), non-interest income ratio (NIIC), diversification index (DIVI), and access to financial institution (AFI) are statistically non-significant indicators, meaning they have no impact on default risk Number of Fintech companies established in a year (FINC), deposit over total asset
(DTA), debt to capital (LEV) are statistically significant indices and they have a negative impact on Z-score, while indices such as value of digital transaction (FINT), outstanding to deposit ratio (OBTD), net interest margin (NIM) are statistically significant indices and they have a negative impact on default risk The results are of great importance for policymakers, especially when Vietnam is in the process of digital transformation because the results show that Fintech development has negative impact on commercial banks’ default risk
2 Limitations of the research and proposed research direction
The number of samples in the study is limited, including 198 observations in the period from 2017-2022 This number of observations is generally not large enough and the 6-year period is not enough of an economic cycle, so the study of the influence of Fintech factors on default risk of commercial banks is not really accurate