Assessing the Impact of Digital Transformation on Risk Management in Vietnam's Joint-Stock Commercial Banks

Abstract

The phenomenon of digital transformation is an increasingly significant global trend, particularly evident in contemporary society. The banking sector is at the forefront of this transformation, driven by advancements in science and digital technology. This shift offers a blend of both opportunities and challenges, especially for joint-stock commercial banks. While these institutions are benefiting from the adoption of digital technologies, they are also facing a range of risks. These include credit, liquidity, and information risks, as well as security concerns like unauthorized access to sensitive data for card or electronic bank account usage and challenges in detecting fraudulent transactions. Given this backdrop, assessing the impact of digital transformation on risk management within joint-stock commercial banks is vital. Our study involved a thorough investigation at 18 joint-stock commercial banks, including interviews with 192 participants.

Full text

Migration Letters
Volume: 21, No: S2(2024), pp. 372-384
ISSN: 1741-8984 (Print) ISSN: 1741-8992 (Online)
www.migrationletters.com
Assessing the Impact of Digital Transformation on Risk
Management in Vietnam's Joint-Stock Commercial Banks
Pham Thi Thu Hoai
1
, Nguyen Thanh Vu
2
*
Abstract
The phenomenon of digital transformation is an increasingly significant global trend,
particularly evident in contemporary society. The banking sector is at the forefront of this
transformation, driven by advancements in science and digital technology. This shift
offers a blend of both opportunities and challenges, especially for joint-stock commercial
banks. While these institutions are benefiting from the adoption of digital technologies,
they are also facing a range of risks. These include credit, liquidity, and information
risks, as well as security concerns like unauthorized access to sensitive data for card or
electronic bank account usage and challenges in detecting fraudulent transactions. Given
this backdrop, assessing the impact of digital transformation on risk management within
joint-stock commercial banks is vital. Our study involved a thorough investigation at 18
joint-stock commercial banks, including interviews with 192 participants.
Keywords: Risk management, banking sector, digital transformation, EFA, multiple
regression.
1. Introduction
The development of enterprises is regarded as a pivotal undertaking in Vietnam's strategy
for socio-economic advancement. The promotion of enterprise development is expected
to facilitate economic growth and significantly contribute to the expansion of the gross
national product. Since the onset of the COVID-19 pandemic in 2020, businesses have
encountered numerous challenges in their production, commercial, and export endeavors.
The phenomenon of business recovery is of significant magnitude. Presently, business
operations exhibit a significant dependence on loans, primarily procured from credit
institutions. According to Mr. Dang Duc Thanh, Chairman of the Vietnamese Economists
Club (VEC), the proportion of equity capital in the business amounts to only 20-30%,
with the remaining being credit loans obtained from commercial banks. The current
scenario has resulted in an overwhelming strain on the credit market. It is now compelled
to meet the needs for both immediate and extended-term capital sources for the economy
and businesses. This study demonstrates that the expansion of lending to businesses
presents a favorable prospect for commercial banks to enhance their credit portfolio.
However, it also entails numerous risks for banks, particularly when faced with
challenges in accurately assessing the financial standing of these businesses. The level of
control exerted over corporate customers is not stringent. Indeed, risk management in
banking activities assumes an exceedingly crucial role in the new era. As the financial
landscape evolves with the advent of technological advancements and shifts in global
dynamics, effective risk management becomes indispensable for ensuring the stability,
1
Lecture/Researcher, Institute of Acccounting & Auditing, Thuongmai University
2
Lecture/Researcher, Thai Nguyen University of Economics and Business Administration

373 Assessing the Impact of Digital Transformation on Risk Management in Vietnam's Joint-Stock
Commercial Banks
sustainability, and resilience of banking institutions. Addressing the intricate array of
risks, which encompasses credit risk, market risk, operational risk, liquidity risk,
compliance risk, and emerging digital risks, requires a thorough and forward-thinking
strategy.
In this new era, characterized by rapid technological changes and interconnected financial
systems, banks must continuously refine their risk management strategies. The integration
of digital technologies introduces both opportunities and challenges, demanding a
nuanced understanding of risks associated with cybersecurity, data privacy, and the
evolving regulatory landscape. Successfully navigating these complexities requires robust
risk assessment methodologies, sophisticated analytical tools, and a vigilant leadership
that is attuned to the intricacies of the modern banking environment. In the trend of
concerning the integration of international economies, accompanied by the robust
development of the Fourth Industrial Revolution, digital transformation emerges as a
pivotal goal and an inevitable developmental strategy for the global banking system, with
specific emphasis on Vietnam's joint-stock commercial banks. Effectively managing risks
in the context of digital transformation requires these banks to identify and measure risks
adeptly, with a strong focus on technological innovation for the development and
implementation of risk management tools. However, traditional risk management systems
often concentrate on constructing response measures post-risk occurrence, rendering them
inadequate in addressing sudden risks promptly. Moreover, various barriers hinder the
timely sharing and processing of data. The advent of big data technology is poised to
revolutionize the channels and mechanisms for collecting, analyzing, and applying
information, creating technical conditions conducive to effective risk management.
Utilizing data mining techniques through customer transactions provides banks with
crucial information sources for credit risk management. The big data utilization
technology is expected to enhance the automation of liquidity risk management in banks
and bolster decision support for liquidity risk analysis and measurement. Additionally,
Blockchain technology finds utility in controlling banking risks. Assessing the factors
influencing risk management in banks is a crucial and necessary undertaking. This
evaluation plays a pivotal role in assisting banks in improving the efficiency of their risk
management practices within the context of digital.
This study was undertaken with the primary objective of assessing the effecting of digital
transformation elements on risk management activities within the banking sector. The
intention behind this research is to establish a foundational understanding that can guide
the comprehensive implementation of digitalization initiatives across the entire banking
industry. In the current landscape, where digital transformation is increasingly becoming
a defining feature of the banking sector, it is imperative to scrutinize how these
technological changes intersect with and affect risk management practices. The research
endeavor seeks to deliver insights into the intricate relationship between digitalization
factors and the efficacy of risk management in banks. By doing so, it seeks to lay the
groundwork for a cohesive and synchronized adoption of digital strategies that can
enhance risk management capabilities across the broader banking landscape. Through a
systematic analysis of various digital transformation components and their impact on risk
management, this research endeavors to contribute valuable insights to the banking
industry. The findings are anticipated to serve as a valuable resource for banking
professionals, policymakers, and other stakeholders, fostering informed decision-making
and strategic planning in the realm of digital transformation and risk management.
2. Literature review
As Matt et al, (2015) posit, digital transformation serves as a driving force, offering
solutions to the challenges that banks encounter in the digital era. It involves a cultural
shift, organizational restructuring, and changes in business operations through

Pham Thi Thu Hoai et al. 374
Migration Letters
technology. The comprehensive provision of digital services to customers, minimizing
direct interactions, and introducing information technology products are key facets of this
transformation. Effective collaboration between the marketing and IT departments within
banks is essential for successful digital product offerings (Lugovsky - 2021).
Le Thi Thuy Van (2021) expands the definition of digital transformation as the transition
from a conventional business model to a digital one, facilitated by the adoption of
technologies such as big data, Internet of Things (IoT), and cloud computing. This shift
entails modifications in operational approaches, leadership styles, work processes, and
corporate culture. Le Cam Tu (2021) emphasizes the comprehensive process of reshaping
a bank's business model, strategy, and culture through the integration of digital
technology platforms, including Artificial Intelligence (AI) and Blockchain.
Essentially, the digital revolution in the banking sector represents a complete embrace of
digital technology, encompassing the digitization of all aspects and operations in banking.
This transformation is instrumental in keeping pace with trends and better meeting
customer needs. The evolution of digital transformation in businesses, especially in the
banking sector, generally progresses through three stages: Digitalization of information,
process digitization, and comprehensive digitization.
Khuc et al. (2021) investigated the factors that affect the quality of digital transformation
processes within Vietnamese commercial banks. They identified five critical factors
Digital transformation environment, Digital transformation capacity, senior leaders,
facilities, and employees demonstrating positive correlations. Notably, the digital
transformation environment and senior leadership were deemed to exert the greatest
influence on the process's quality. Building on this, Dr. Nguyen Tu Anh (2022)
highlighted the inevitability of digital transformation across economic sectors,
particularly in banking. Recognizing this trend, he emphasized the necessity for rigorous
control and management of potential risks associated with digital transformation.
According to his research, key risks for the banking industry in the next five years include
climate change, economic recovery levels, the speed and scope of digitalization,
disruption from new technologies, outdated IT systems, data integrity, use of ML/AI,
competition from new entrants, data privacy, and model risks related to ML/WHO.
Notably, climate change emerged as the most critical factor, with 91% of respondents
expressing concerns about its impact on asset loss, factory damage, and reduced
borrowing capacity. Le Hai Trung and colleagues (Banking Academy, 2021) delved into
operational risks at Vietnamese commercial banks, exploring factors including metrics
like the outstanding loans to total assets ratio and non-interest income to total income
ratio, profit to total assets, and the number of branches. Using the Pooled OLS method,
fixed effects, and uncertain effects, their results revealed that the GDPG variable had a
negative impact, aligning with expectations, while the impact of Return on Assets (ROA)
differed from predictions, indicating the complexity of operational risk dynamics in
commercial banking.
The research titled "IOT Impacts and Digital Transformation at Listed Vietnam Banks"
conducted by Nguyen et al. (2021), addresses the imperative for Vietnamese banks to
undergo a transformative shift in Adapting to the vigorous growth of the digital economy
in Vietnam. With the digital transformation wave sweeping across various industries, the
study underscores the necessity for banks to embrace a new business model. This model
advocates for the integration of technology into operations and the digitization of
business processes, emphasizing automation and intelligence. The overarching goal is to
enable banks to deliver products and services on digital platforms while effectively
leveraging data to enhance business resolution and customer engagement. The research
adopts a comprehensive approach, employing a combination of quantitative and
qualitative analytical methods. Notably, the study utilizes OLS regression supported by
Eviews for quantitative analysis. The data collection process involves sourcing
information from reliable internet platforms and websites, encompassing stock prices

375 Assessing the Impact of Digital Transformation on Risk Management in Vietnam's Joint-Stock
Commercial Banks
from HOSE or HNX stock exchanges, rates from the bank system, and economic
indicators such as GDP and CPI from authoritative sources such as the Bureau of
Statistics and the Ministry of Finance. This combined-method research offers valuable
insights into the influence of IoT and digital transformation on listed banks in Vietnam,
providing a nuanced understanding of the changing landscape within the context of the
growing digital economy.
Pham Ngoc Diep, in a 2022 study at Military Bank, emphasized the factors crucial for the
success of a bank's digital transformation. According to Diep, customer considerations,
followed by innovation and the ongoing modernization of infrastructure, operating
models, solution definition, data, and human resources, precede the imperative to grasp
and master technology. However, it's noteworthy that Diep's research was qualitative in
nature, lacking quantitative analysis.
Fortis et al. (2021) conducted an article titled “Digital transformation and strategy in the
banking sector: Evaluating the acceptance rate of E-services.” This study examined the
ongoing digital transformation in the Greek banking sector, focusing on factors such as
serving remote areas, differentiation from competitors, and cost reduction. Utilizing a
Multivariate Regression Analysis based on the Technology Acceptance Model, the study
involved 161 employees from Greek banks. The findings highlighted the perceptions of
bank employees regarding new technologies and offered practical insights for Greek
banking executives to ease the shift into the digital age through targeted educational
programs.
Research by Indra Saputra, Etty Murwaningsari, and Yvonne Augustine in 2023
conducted at Trisakti University, Indonesia, and explored the function of Enterprise Risk
Management and Digital Transformation in maintaining the stability of the banking sector
in Indonesia. Utilizing questionnaires administered to directors, managers, division heads,
and branch heads in the Indonesian banking sector, the research employed descriptive and
verification analyses. The findings demonstrated that both Enterprise Risk Management
and Digital Transformation exert a substantial and positive influence on the sustainability
of the banking sector, underscoring the profound impact of technological advancements
on the industry's resilience.
Hussain (2022) investigated the relationship between employee skills, task quality, and
technical accuracy within the banking sector of England. Employing a quantitative
research approach through a questionnaire survey, the study revealed that task quality acts
as a mediator in the association between employee skills and technical accuracy. This
suggests that improved employee skills lead to enhanced task quality, ultimately resulting
in increased technical accuracy.
In conclusion, a review of domestic and foreign research related to influencing factors on
risk management in banks indicates varying perspectives and a lack of consensus on the
number of influencing factors. The author proposes a further exploration of these factors
and their impact through an integration of both qualitative and quantitative research
approaches, specifically focusing on joint-stock commercial banks in Vietnam.
3. Methodology
This research employs a quantitative approach with the objective of developing
theoretical models and hypotheses to establish a robust relationship between empirical
observations and mathematical expressions. The research population includes all
executive officers, encompassing directors, division heads, department heads, branch
heads, and managers. Questionnaires were distributed to this population through various
channels such as phone calls, Zalo, Google Forms, and direct visits to respondents
facilitated by official contact persons. The author conducted a comprehensive review and
analysis of prior studies on factors influencing risk management at joint-stock

Pham Thi Thu Hoai et al. 376
Migration Letters
commercial banks in Vietnam in the context of digital transformation. Following this, the
author conducted interviews with experts to design a research model, formulate research
hypotheses, and construct a scale of factors affecting risk management at joint-stock
commercial banks in Vietnam concerning digital transformation.
The quantitative research method builds upon the research model established during the
qualitative research phase to test hypotheses and assess the impact levels of identified
factors. Interviews conducted in a semi-structured format to test and screen the variables
of the model, refining scales and completing the survey instrument for the research
variables. Through open-ended questions, experts' opinions on factors impact on risk
management at joint-stock commercial banks in Vietnam in the current digital
transformation context were collected. The author utilized the interview content, either
audio-recorded or transcribed with permission, to evaluate established scales and identify
scales for quantitative research. The preliminary scale, which synthesized research results
by domestic and foreign scientists, was presented to experts for feedback.
The research model, after discussions and consultations with experts, encompasses six
factors influencing the effectiveness of risk management at joint-stock commercial banks
in Vietnam within the context of digital transformation. These factors include the
automation of business processes, upgrading network security, transformation of
corporate culture towards digital transformation, transformation of management and
leadership towards digital transformation, qualifications of employees, and the utilization
of new technology. The interview process concluded after experts provided their
comments, evaluations, and potential additions to the model, ensuring a comprehensive
and well-informed approach to studying risk management in the digital transformation
landscape of joint-stock commercial banks in Vietnam.
The proposed research model entails six hypotheses to be tested, with the dependent
variable being "Risk Management" (RM) and the following independent variables:
Model 1. Research model Factors affecting risk management
The hypotheses are as follows:
(ABP) Automate business processes
(UNS) Upgrade network securities
(TCC) Transform corporate culture towards digital transformation
(TML) Transform management and leadership towards digital transformation
(EOQ) Employees of qualifications
(UNT) Use new technology
H1: Automate business processes has a positive impact on risk management in terms of
digital transformation. This hypothesis posits that automating business processes

377 Assessing the Impact of Digital Transformation on Risk Management in Vietnam's Joint-Stock
Commercial Banks
enhances risk management by improving productivity, compliance, and overall customer
experience. Examples include using automation to improve office administration,
increase processing speed, and identify customers with RPA-enabled tools.
H2: Upgrade network security is positively related to risk management in the context of
digital transformation. This hypothesis asserts that enhancing network security is crucial
in mitigating cybersecurity risks associated with online banking transactions. As hackers
exploit vulnerabilities, robust security measures are essential to prevent system collapse
and financial losses.
H3: Transform corporate culture towards digital transformation is positively related to
risk management in the context of digital transformation. This hypothesis suggests that
cultivating a culture aligned with digital transformation goals fosters a learning
organization, customer-centric thinking, and creativity. This involves changing
perceptions, evaluating values, and communicating new cultural values during digital
transformation.
H4: Transform management and leadership towards digital transformation is positively
related to risk management in the context of digital transformation. This hypothesis
emphasizes the importance of leadership adapting to digital transformation, possessing
technological awareness, systems thinking, long-term strategic vision, and supporting
employees to navigate the transformation effectively.
H5: Employees of qualification have a positive relationship with risk management in the
context of digital transformation. This hypothesis highlights the significance of skilled
and professional staff in minimizing errors during operations and addressing management
and resource development for effective risk management.
H6: Using new technology has a positive relationship with risk management in terms of
argument transfer. This hypothesis suggests that the utilization of new technologies, such
as Artificial Intelligence, contributes to increased efficiency, improved operational
processes, enhanced customer experience, and ultimately aids in managing future risk.
The study centers on risk management within joint-stock commercial banks in Vietnam
during the era of digital transformation, encompassing diverse dimensions of risk,
including credit risk, market risk, liquidity risk, operational risk, legal and compliance
risk, reputation risk, and strategic risk. The scales for these variables have been crafted by
drawing on existing studies and adjusted to align with the specific characteristics of coal
mining enterprises through expert discussions.
The quantitative data collection involved distributing 270 survey questionnaires to 18
joint stock commercial banks in Vietnam, yielding 215 responses with a response rate of
79.63%. After removing invalid votes, 192 responses were qualified for data entry. The
survey instrument included Likert scales for factors affecting risk management, and the
data analysis utilized SPSS software to assess scale quality, evaluate variable correlations,
and conduct linear regression analysis. The expert interview instrument utilized a Likert
scale, with responses ranging from Strongly Disagree (1) to Strongly Agree (5).
4. Results discussion
4.1. Data analysis
Quality of scale
Once the survey data were entered into SPSS 22 software, the author employed the tool to
compute two reliability standards for the scale, namely Cronbach's Alpha and Total
Correlation coefficient (Corrected Item - Total Correlation), to assess the scale's quality
for each factor. The examination of the scale's quality yielded the following results:

Pham Thi Thu Hoai et al. 378
Migration Letters
Table 1. Summary of scale reliability assessment
Scale
Cronbach's Alpha
coefficient
Observed variables
Corrected Item-Total
Correlation
Conclusion
ABP
0.804
ABP1
0.517
Qualified
ABP2
0.592
Qualified
ABP3
0.623
Qualified
ABP4
0.606
Qualified
ABP5
0.623
Qualified
UNS
0.746
UNS1
0.373
Qualified
UNS2
0.601
Qualified
UNS3
0.496
Qualified
UNS4
0.726
Qualified
TCC
0.417
TCC1
0.318
Not
TCC2
0.133
Not
TCC3
0.315
Not
TML
0.739
TML1
0.511
Qualified
TML2
0.504
Qualified
TML3
0.539
Qualified
TML4
0.575
Qualified
EOQ
0.739
EOQ1
0.540
Qualified
EOQ2
0.544
Qualified
EOQ3
0.609
Qualified
UNT
0.722
UNT1
0.413
Qualified
UNT2
0.685
Qualified
UNT3
0.518
Qualified
UNT4
0.449
Qualified
RM
0.772
RM1
0.615
Qualified
RM2
0.186
Not
RM3
0.565
Qualified
RM4
0.566
Qualified
RM5
0.621
Qualified
RM6
0.621
Qualified
Source: own calculation
Based on the above table, it is evident that the Cronbach's Alpha coefficient for 5 factors
is > 0.6, and 1 factor has a Cronbach's Alpha coefficient < 0.6; we eliminate this factor.
Among the 5 factors with Cronbach's Alpha coefficient > 0.6, the observed variable RM2
has a correlation coefficient < 0.3; therefore, we eliminate this variable. The correlation
coefficient of the remaining scales is greater than 0.3. The remaining scales in the
research model are reliable and satisfactory for further in-depth analysis. We proceed to
run Cronbach's Alpha coefficient again and have the following Table 2:
All remaining scales in the research model are reliable and satisfactory for further in-
depth analysis
Exploratory factor analysis
Following the assessment of the scale's quality, the author determined that all observed
variables ensure reliability in measuring the factors they observe. To evaluate the degree
of correlation between variables in explaining the concept of the factor, the author
conducted the KMO test (Kaiser-Meyer-Olkin) and Bartlett's Test of Sphericity. The
results are presented in Table 3.
The KMO (Kaiser-Meyer-Olkin Measure of Sampling Adequacy) coefficient, registering
a value of 0.740, fulfills the criterion of 0.5 ≤ KMO ≤ 1. This suggests the suitability of

379 Assessing the Impact of Digital Transformation on Risk Management in Vietnam's Joint-Stock
Commercial Banks
exploratory factor analysis. The significance coefficient of Bartlett's Test of Sphericity is
0.000, meeting the condition of being less than or equal to 0.05. This robustly supports
the conclusion that the results of exploratory factor analysis (EFA) are highly significant,
confirming that the observed variables are correlated and converge to account for the
factors.
Table 2. Summary of scale reliability assessment
Scale
Cronbach's Alpha
coefficient
Observed variables
Corrected Item-Total
Correlation
Conclusion
ABP
0.804
ABP1
0.517
Qualified
ABP2
0.592
Qualified
ABP3
0.623
Qualified
ABP4
0.606
Qualified
ABP5
0.623
Qualified
UNS
0.746
UNS1
0.373
Qualified
UNS2
0.601
Qualified
UNS3
0.496
Qualified
UNS4
0.726
Qualified
TML
0.739
TML1
0.511
Qualified
TML2
0.504
Qualified
TML3
0.539
Qualified
TML4
0.575
Qualified
EOQ
0.739
EOQ1
0.540
Qualified
EOQ2
0.544
Qualified
EOQ3
0.609
Qualified
UNT
0.722
UNT1
0.413
Qualified
UNT2
0.685
Qualified
UNT3
0.518
Qualified
UNT4
0.449
Qualified
RM
0.824
RM1
0.633
Qualified
RM3
0.617
Qualified
RM4
0.603
Qualified
RM5
0.608
Qualified
RM6
0.634
Qualified
Source: own calculation
Multiple linear regression analysis
The findings presented in Table 4 indicate that all Sig values, representing the Pearson
correlation between independent variables and dependent variables, are <0.05.
Furthermore, a Pearson Correlation coefficient <0.5 suggests the absence of
multicollinearity.
Table 3. KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
.740
Bartlett's Test of
Sphericity
Approx. Chi-Square
1321.150
Df
190
Sig.
.000

Pham Thi Thu Hoai et al. 380
Migration Letters
Table 4. Pearson's Linear Correlation Analysis
Correlations
RM
ABP
UNS
TML
EOQ
UNT
RM
Pearson Correlation
1
.554*
.535**
.513**
.450**
.480**
Sig. (2-tailed)
.000
.000
.000
.000
.000
N
192
192
192
192
192
192
ABP
Pearson Correlation
.554**
1
.143*
.341**
.184*
.178*
Sig. (2-tailed)
.000
.048
.000
.011
.014
N
192
192
192
192
192
192
UNS
Pearson Correlation
.535**
.143*
1
.233**
.223**
.414**
Sig. (2-tailed)
.000
.048
.001
.002
.000
N
192
192
192
192
192
192
TML
Pearson Correlation
.513**
.341**
.233**
1
.240**
.284**
Sig. (2-tailed)
.000
.000
.001
.001
.000
N
192
192
192
192
192
192
EQQ
Pearson Correlation
.450**
.184*
.223**
.240**
1
.315**
Sig. (2-tailed)
.000
.011
.002
.001
.000
N
192
192
192
192
192
192
UNT
Pearson Correlation
.480**
.178*
.414**
.284**
.315**
1
Sig. (2-tailed)
.000
.014
.000
.000
.000
N
192
192
192
192
192
192
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Source: own calculation
Table 5: Table of results of regression coefficient test
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t
Sig.
Collinearity Statistics
B
Std. Error
Beta
Tolerance
VIF
1
(Constant)
-.164
.169
-.969
.334
ABP
.294
.037
.367
7.962
.000
.868
1.152
UNS
.281
.042
.321
6.715
.000
.807
1.239
TML
.158
.034
.219
4.571
.000
.810
1.235
EQQ
.158
.035
.210
4.536
.000
.863
1.159
UNT
.139
.045
.154
3.100
.002
.752
1.330
a. Dependent Variable: RM
Source: own calculation
The author tests the linear relationship between the dependent variable RM and the
independent variables (ABP, UNS, TML, EOQ, UNT) to determine whether the

381 Assessing the Impact of Digital Transformation on Risk Management in Vietnam's Joint-Stock
Commercial Banks
regression model is suitable for the study or not. The results in Table 5 show that there are
5 factors that are statistically significant: ABP, UNS, TML, EOQ, UNT is statistically
significant in the analytical model with sig. < 0.05, and these 5 factors have an impact on
the effectiveness of risk management at joint stock commercial banks in the context of
digital transformation. In addition, the VIF coefficients are all less than 2, so it can be
concluded that there is no multicollinearity between these factors. The regression model
to evaluate the impact of factors on the effectiveness of risk management at joint stock
commercial banks in Vietnam in the context of digital transformation is written as
follows:
RM = -0,164 + 0,294*ABP + 0.281*UNS + 0.158*TML + 0.158*EOQ + 0,139*UNT
The analysis results of the multiple linear regression models show that all 5 factors have a
positive and significant impact on the effectiveness of risk management at joint stock
commercial banks in the context of digital transformation (the regression coefficient is
not standardized with a positive sign).
According to the summary table of the model, the coefficients R² and adjusted R² are
reported as 0.656 and 0.647, respectively. This indicates that the independent variables in
the research model collectively account for 65.6% of the variation in the dependent
variable. The remaining 34.4% is attributed to the influence of variables outside the
model and random error.
Table 6: Evaluation of the fit of the regression model
Model Summaryb
Model
R
R Square
Adjusted R Square
Std. Error of the
Estimate
Durbin-Watson
1
.810a
.656
.647
.23715
1.769
a. Predictors: (Constant), UDT, ABP, EOQ, TML, UNS
b. Dependent Variable: RM
Source: own calculation
4.2. Findings
Research has demonstrated that factors such as “automating business processes”,
“upgrading network securities”, “transforming management and leadership towards
digital transformation” and “utilizing new technology” have substantial impacts on risk
management within the banking sector. This empirical evidence serves as a valuable
resource for managers, enhancing their understanding of the crucial role that digital
technology plays in effective risk management within the banking industry.
Moreover, the term "employee quality" in this study is contextualized within the
application of digital technology, extending beyond conventional employee attributes.
Consequently, this emphasizes the imperative need for promptly elevating staff quality to
align with the ongoing development of digital transformation within the banking sector.
In addition, the study reveals that transforming corporate culture towards digital
transformation does not exhibit a significant relationship with risk management at banks.
In essence, this implies that the corporate culture within banks concerning digital
transformation has yet to be clearly defined.
5. Conclusion
During the Fourth Industrial Revolution era, digital transformation within the banking
industry has become an inevitable trend for those seeking to enhance competitive

Pham Thi Thu Hoai et al. 382
Migration Letters
advantage and fortify risk management. Swift adaptation to this trend positions banks to
be more proactive within the process of digital transformation, fostering sustainable
development in the future.
The scope of risk management in joint-stock commercial banks involves addressing risks
related to credit, market, operations, liquidity, and complianc
To effectively manage these risks, banks must construct a risk management process
tailored to the developmental needs and regulatory requirements of each business activity.
Technical support capabilities and data management frameworks should be enhanced to
ensure efficient risk and capital management. Emphasis on collateral management, risk
identification and measurement, internal assessment, and risk warning is crucial,
involving a thorough review of the enterprise's financial situation and credit information
to assess debt payment capability and timing.
IFRS standards emerge as pivotal tools in bank risk management, particularly in
addressing accounting functions for financial instruments. These standards encompass
decision rules for classification, impairment instruments, valuation, and accounting
instruments, with a focus on prop risk accounting to assess and mitigate risks throughout
the financial system.
While many joint stock commercial banks in Vietnam have embraced new technologies
such as AI, online automatic answering robots, and IoT, the country faces a shortage of
qualified human resources proficient in information technology, particularly in digital
transformation. Some banks resort to outsourcing or partnering with Fintech companies,
potentially compromising the quality of their IT staff and impeding the speed and quality
of digital transformation. Future efforts should concentrate on training IT personnel,
offering additional benefits, and creating attractive working conditions to attract talents in
the field.
Artificial intelligence (AI) plays a pivotal role in identifying unusual activities in
transactions and minimizing fraud risks. Leveraging historical data, AI aids banks in
making flexible and intelligent decisions, contributing to improved customer experiences
through psychographic analysis, behavior understanding, and personalization.
Furthermore, big data proves instrumental in customer understanding, trend prediction,
process optimization, and risk management. Its applications in the banking industry
enhance customer experiences, provide personalized services, detect fraud, manage
investment portfolios, and improve internal processes.
Leadership awareness is deemed an urgent requirement for the digital transformation
process. Leaders in banking must possess knowledge not only of finance and banking but
also of the latest technology in Industry 4.0. Planning resource requirements, setting clear
goals, providing strategic vision, restructuring the bank's organization, and creating
products suitable for the digital transformation process are crucial leadership roles.
Leaders can draw lessons from the achievements of foreign banks in digital
transformation and leverage global competition as motivation for successful digital
transformation within their own institutions.
ACKNOLEGEMENT
First and foremost, I would like to express my heartfelt gratitude to the University of
Commerce for supporting me to carry out this research.
I would like to express my sincere gratitude to the dean of the Faculty of Accounting and
Auditing at the University of Commerce for providing me with the necessary support and
conducive environment that enabled me to successfully complete my research within the
given timeframe.
I would like to express my heartfelt gratitude to the members who kindly assisted me in
conducting this research, particularly Dr. Nguyen Thanh Vu from Thai Nguyen University

383 Assessing the Impact of Digital Transformation on Risk Management in Vietnam's Joint-Stock
Commercial Banks
of Economics and Business Administration. Dr. Vu not only offered valuable guidance
and insights on research methods but also played a significant role in the successful
completion of this study.
CONFLICT OF INTEREST
We would like to assure you that our research is conducted independently and in full
compliance with legal regulations, solely for scientific purposes. We prioritize
safeguarding information security for corporates and banks that engage in interviews. We
kindly assure you that this product is solely the result of our research team's efforts and
has not yet been published in any magazine. We assure you that we will fully accept
responsibility in the event of any conflicts of interest that may arise regarding the
utilization of the findings from this research.
Author 1 – Pham Thi Thu Hoai: Conceptualization; Methodology; Formal Analysis;
Investigation; Writing – Original Draft; Project Administration; Software; Supervision;
Funding Acquisition
Author 2 – Nguyen Thanh Vu: Conceptualization; Methodology, Validation; Data
Curation, Writing – Review & Editing.
We affirm that the data utilized in the research has been thoroughly examined in
accordance with appropriate protocols, thereby ensuring scientific rigor. We are prepared
to furnish the raw data from the study and address any inquiries pertaining to the data.
The findings presented in the article are derived from the research methodologies
employed by the author. The recommendations outlined in the study are intended solely
as a point of reference for individuals with comparable conditions residing in Northern
Vietnam. The utilization of these research findings by individuals and organizations
necessitates careful evaluation of the current conditions and circumstances.
References
[1] Brett, Bank 4.0 (2018).
[2] C. Skinner, Digital Bank. (2014). Strategies for Launching or Becoming a Digital Bank.
Marshall Cavendish Business.
[3] DBS Bank (2017). Digital Transformation Presentation at Invester Day 2017.
[4] Dr. Khuc The Anh, Nguyen Thi Phuong Anh, Ta Thi Minh Hang, Cao Nguyen Ly Ly - (2021),
Research on factors affecting the quality of digital transformation processes at Vietnamese
commercial banks.
[5] Esinath Ndiweni, Mohamed Boulkeroua, Abdelghani Echchabi and Tabani Ndlovu. Digital
technology disruption on bank business models. Int. J. Business Performance Management,
Vol. 21, Nos. 1/2, 2020.
[6] Florian Diener (2021). Digital Transformation in Banking: A Managerial Perspective on
Barriers to Change. Prague University of Economics and Business.
[7] Fortis Kitsios, Ioannis Giatsidis and Maria Kamariotou (2021), Digital transformation and
strategy in the banking sector: Evaluating the acceptance rate of E-services
[8] Fotis Kitsios, Ioannis Giatsidis, Maria Kamariotou (2021) Digital Transformation and Strategy
in the Banking Sector: Evaluating the Acceptance Rate of E-Services Journal of Open
Innovation: Technology, Market, and Complexity, Volume 7, Issue ISSN 2199-8531,
https://doi.org/10.3390/joitmc7030204.
[9] Indra Saputra, Etty Murwaningsari and Yvonne Augustine (2023) (Trisakti University,
Indonesia), The role of enterprise risk management and digital transformation on sustainable
banking in Indonesia

Pham Thi Thu Hoai et al. 384
Migration Letters
[10] Le Hai Trung and al. (2021) "Factors affecting operational risks at Vietnamese
commercial banks"
[11] Matt, Christian & Hess, Thomas & Benlian, Alexander. (2015). Digital Transformation
Strategies. Business & Information Systems Engineering. 57. 339-343. 10.1007/s12599-015-
0401-5.
[12] Mubashar Hussain (2022) Influence of Employee Skills and Task Quality on Technical
Accuracy: Evidence from Digital Transformation in England. (2022). Business Review of
Digital Revolution, 2(2), 36-44. https://doi.org/10.62019/BRDR.02.02.05
[13] Nguyen Thi Hang, Dinh Tran Ngoc Huy, Dinh Thi Hien, and Vu Quynh Nam (2021), IOT
Impacts and Digital Transformation at Listed Vietnam Banks
[14] Richard Baskerville, Francesco Capriglione, Nunzio Casalino (2020). Impacts,
Challenges and Trends of Digital Transformation in the Banking Sector. Law and Economics
Yearly Review Journal.
[15] Saputra, Indra & Murwaningsari, Etty & Augustine, Yvonne. (2023). The Role Of
Enterprise Risk Management And Digital Transformation On Sustainable Banking In
Indonesia. Neo Journal of economy and social humanities. 2. 17-30. 10.56403/nejesh.v2i1.85.
[16] Thomas F. Dapp (2017). Fintech: The Digital Transformation in the Financial Sector.
Springer International Publishing AG.