MINISTRY OF EDUCATION AND TRAINING THE STATE BANK OF VIETNAM BANKING UNIVERSITY OF HO CHI MINH CITY GRADUATION THESIS THE IMPACT OF DIGITAL TECHNOLOGY ON CREDIT MANAGEMENT EFFECTIVENESS
INTRODUCTION TO THE RESEARCH TOPIC
REASON FOR CHOOSING TOPIC
Commercial banks serve as the backbone of the economy by acting as financial intermediaries that channel idle capital from individuals and organizations to meet the funding needs of others for profit As competition intensifies within the banking sector, these institutions must leverage unique opportunities to enhance their brand competitiveness The Fourth Industrial Revolution is a crucial catalyst for integrating technology into business practices, with advancements in high-speed internet, cloud computing, and blockchain technology reshaping operations and influencing consumer behavior Digital transformation signifies a shift in how individuals and businesses utilize digital technology to improve operations, enhance personal experiences, and develop new business models This transformation can significantly impact organizational practices through the adoption of innovative technologies, prompting companies to adapt and expand their operational strategies.
The digital transformation in commercial banks has become a focal point for researchers, policymakers, and industry experts, as it signifies the full integration of digital technologies into banking operations This transformation is fundamentally changing how banks function and provide value to their customers Key innovations, including financial software, digital banking platforms, mobile applications, and fintech solutions, are being created to meet customer expectations concerning interest rate flexibility, big data utilization, mobile finance, risk management, and online financial services.
2 relationship management Additionally, this transformation signals a cultural change within the banking sector, requiring institutions to continuously adapt, experiment, and learn from both their achievements and challenges
The COVID-19 pandemic in 2020 presented significant challenges for the global banking and financial sector, simultaneously accelerating digital transformation within these industries The transition to remote work necessitated the adoption of innovative operational methods to fulfill the rising demand for digital services, including swift access to financial assistance In response, the financial sector embraced advanced technologies such as robotic process automation (RPA), machine learning for anti-money laundering (AML), and know your customer (KYC) protocols Additionally, regulatory technology (RegTech) has been implemented to mitigate compliance risks This digital transformation has shifted the focus of financial services towards a customer-centric approach, aiming to meet evolving expectations for new services, enhanced market transparency, and diverse customer needs.
Military Commercial Joint Stock Bank (MB Bank) is one of Vietnam's largest and most respected commercial banks, where effective credit management is crucial for its financial performance and stability In the first half of 2024, MB Bank reported a 10.3% increase in its customer loan portfolio compared to the previous year, exceeding the industry's average growth rate, while maintaining a low non-performing loan (NPL) ratio of 1.43% To achieve these results, MB Bank has adopted digital transformation within its operations; however, this shift has introduced various challenges in credit management that require careful reassessment to further enhance financial efficiency.
As a result, the author has selected the topic “ The Impact of Digital
Technology on the Effectiveness of Credit Management in Military Commercial
Joint Stock Bank ” as my graduation thesis.
RESEARCH OBJECTIVES
This research aims to identify and highlight the key factors influencing the effectiveness of credit management It will analyze and suggest solutions to improve the operational efficiency of Military Commercial Joint Stock Bank in credit management, which is crucial for fostering sustainable development within the banking sector.
1) Identify the specific factors influenced by digital technology that impact the efficiency of credit management in Military Commercial Joint Stock Bank
2) Evaluate the degree of impact of integrating digital technology into credit management at Military Commercial Joint Stock Bank
3) Suggest actionable recommendations for Military Commercial Joint Stock Bank to improve their credit management processes using digital technology.
RESEARCH QUESTIONS
We can identify the key questions that must be answered in accordance with the research objectives:
Question 1: Which digital technologies have been implemented in the credit management processes at Military Commercial Joint Stock Bank, and how do they specifically enhance operational efficiency?
Question 2: What is the extent of influence that digital transformation has on the credit management practices at Military Commercial Joint Stock Bank?
Question 3: What recommendations can be proposed for Military Commercial
Joint Stock Bank to enhance their credit management processes through the adoption of digital technology?
SUBJECTS AND THE SCOPE OF THE STUDY
1.4.1 The subjects of the study
The impact of digital technology on the Effectiveness of Credit Management in Military Commercial Joint Stock Bank
1.4.2 The scopes of the study
Space: The research focuses on Military Commercial Joint Stock Bank and various digital transformation to identify the impact and the connection between them
Time: Data will be collected from 2013 to 2023 on a quarterly basis.
METHODLOGY
This study employs a combination of qualitative and quantitative methods, facilitating a comprehensive analysis of operational aspects within the study subject
Descriptive statistical techniques and data collection methods are employed to gather and analyze information from the Vietnamese financial market, focusing on companies in the digital technology sector and the consolidated reports of Military Commercial Joint Stock Bank.
A time-series comparison strategy enables the detection of changes in Military Commercial Joint Stock Bank's operations, particularly in its credit provision, driven by digital transformation.
This study utilizes analytical methodologies to investigate the impact of digital technology on credit management, specifically focusing on AI applications By employing the Python programming language for data collection and analysis, the research enhances the accuracy and effectiveness of its findings The Multiple Linear Regression algorithm will be utilized to evaluate the influence of various factors on credit risk at MB Bank The results will be analyzed and presented, culminating in the development of several management implications.
RESEARCH CONTRIBUTION
Establishing a framework that connects digital technology to effective credit management enhances our understanding of their interaction and highlights specific factors influenced by technological advancements Key elements include the automation of approval processes, the use of big data analytics for risk assessment, and improved client communication This framework enriches theoretical discussions on critical aspects such as risk assessment, loan processing times, and customer satisfaction, thereby offering a solid theoretical foundation for future research in credit management.
The research expands on digital transformation theories in the banking sector by demonstrating how technological advancements, such as chatbots and machine learning, can revolutionize traditional practices at Military Commercial Joint Stock Bank It emphasizes that these technologies not only optimize the lending process but also provide new avenues for improving risk management and enhancing customer experiences These findings are crucial for both theoretical development and for anticipating future banking trends in the digital transformation era.
This research provides actionable recommendations for banks to integrate digital technology into their credit management processes, significantly improving operational efficiency By adopting Robotic Process Automation (RPA), banks can streamline loan application processing, resulting in quicker approval rates and a reduction in human error.
Digital technology significantly enhances risk management, as demonstrated by MB Bank's use of big data analytics to accurately assess risk This enables the bank to implement effective strategies to mitigate credit risk and reduce non-performing loans, thereby safeguarding profitability and fostering a safer lending environment for customers.
The research findings indicate that MB Bank can enhance customer experience by integrating digital technology, which will improve interaction and service delivery This, in turn, is likely to boost customer satisfaction and loyalty, essential for fostering long-term relationships and promoting sustainable development for both the bank and the economy.
The research highlights the critical role of training staff on new technologies to optimize their usage and improve credit management skills in banks These recommendations are essential for enhancing internal processes and promoting the overall growth of the banking sector amid digital transformation.
DISSERTATION STRUCTURE
In Chapter 1, the author highlights the significance of analyzing the factors influencing credit risk at Military Commercial Joint Stock Bank, setting the stage for the study's objectives and research questions This chapter provides a detailed overview of the thesis, including its goals, contributions, and structure Employing both qualitative and quantitative research methods, with an emphasis on quantitative analysis, the author references previous studies from various contexts to identify new risk factors impacting credit management today Additionally, the chapter outlines the thesis organization, which consists of five chapters.
This research seeks to consolidate existing theories on credit management while examining relevant theoretical frameworks It will analyze insights from pertinent scientific studies, both domestically and internationally, to pinpoint the most applicable theories in today's context Additionally, the study aims to address previously overlooked factors and identify gaps in current research Through this comprehensive analysis, it will provide valuable insights into the field of credit management.
7 will choose the most fitting model to guide the research
Based on the research findings from Chapter 2, the author will develop a comprehensive model and outline a series of research hypotheses The subsequent analysis will investigate the different factors affecting these hypotheses and examine their potential implications in depth.
This article assesses the current credit management practices at MB Bank and analyzes results obtained from a regression model By utilizing Python programming and relevant algorithms, it investigates the influence of various factors on credit management outcomes The discussion delves into the insights gained from these findings, exploring their broader implications for the bank's credit strategies.
In conclusion, this study highlights the crucial impact of digital technology on credit management effectiveness at the Military Commercial Joint Stock Bank The research findings reveal that digital tools significantly enhance risk assessment accuracy, accelerate processing times, and improve customer engagement, ultimately leading to better decision-making and stronger client relationships By optimizing credit management through digital solutions, financial accessibility can be increased, promoting growth in both the banking sector and Vietnam's economy Additionally, it is important to recognize the study's limitations and consider future research directions.
CONCLUSION
Chapter 1 provides an overview of the research topic which focuses on the context and importance of applying digital technology in credit management at Military Commercial Joint Stock Bank
The chapter emphasizes the continuous development of the Vietnamese
Commercial banks play a crucial role in stimulating economic activities by providing essential credit However, the rise in credit can lead to increased credit risks, particularly concerning bad debt, which poses a threat to the stability and sustainability of both the banking system and the broader economy.
This chapter outlines key research methods, focusing on quantitative approaches such as regression models and data analysis techniques, which lay the groundwork for more in-depth analyses in later sections The primary aim is to identify factors influencing credit management and to suggest solutions for improving its effectiveness through the integration of digital technology at Military Commercial Joint Stock Bank.
THEORETICAL BASIS AND LITERATURE REVIEW
THEORETICAL BACKGROUND
2.1.1 The concept of Credit Management
Credit is a financial arrangement that allows borrowers to access money or goods with the expectation of future repayment, often including interest This system relies on trust in the borrower's ability to repay and encompasses various instruments such as loans, mortgages, and trade credit "Credit extension" allows individuals or organizations to access a specified amount of money under defined repayment terms through means like loans and bank guarantees Both lenders and borrowers face risks; lenders risk default while borrowers risk over-leveraging, which can lead to financial challenges Key factors influencing credit terms include accessibility, interest rates, creditworthiness, and collateral Bank credit, a subset of credit, focuses on financial services provided by banks, including loans and international transactions Banks are vital financial intermediaries that facilitate economic activity by providing credit, enabling businesses and households to save, invest, and grow.
10 expenditures, ultimately contributing to economic growth
Credit management is essential for companies involved in credit-related activities, as it encompasses strategies to collect and control payments from clients Effective credit management ensures timely payment for services rendered and involves practices such as credit analysis, rating, classification, and reporting It is crucial for organizations to manage their credit sales efficiently to mitigate credit risk This is particularly significant for banks, where robust credit management is vital for long-term sustainability and financial stability By implementing strong credit management strategies, banks can protect themselves from loan default risks, which can threaten their operational continuity and overall financial health Therefore, maintaining effective credit management is necessary for the resilience and success of banks in a competitive market.
2.1.2 The concept of Digital Technology
The Fourth Industrial Revolution is driving the integration of technology into business operations, leading to a significant shift towards digital transformation Traditional businesses are evolving into online platforms, influenced by rapid advancements in high-speed internet, cloud computing, and blockchain technology, which are fundamentally reshaping operational processes.
Digital transformation is a comprehensive process that integrates digital technologies into all facets of a business, fundamentally altering organizational operations and customer value delivery This shift affects not only operational processes but also enhances customer engagement and service delivery As noted by George Westerman et al (2014), successful digital transformation requires more than just adopting new technologies; it demands a cultural shift within the organization, supported by strong leadership and commitment from all levels.
Digital transformation is revolutionizing business processes in the banking sector, particularly in credit management By automating tasks such as data collection, analysis, and credit approval, banks can save time and reduce costs, as highlighted by Erik Brynjolfsson and Andrew McAfee.
Digital technology has the potential to enhance organizational workflows, leading to increased efficiency and fewer errors By embracing digital transformation, financial institutions can gather and analyze extensive data from diverse sources, such as transaction records, social media, and credit histories.
"Competing on Analytics: The New Science of Winning" highlights the importance of data analytics in enhancing decision-making processes for credit approvals Digital transformation empowers financial institutions to adapt their credit policies and approval workflows more efficiently According to Emory University et al (2013), adopting these technologies allows banks to identify early signs of potential bad debt, enabling proactive measures Additionally, digital transformation strengthens information security and ensures compliance with regulatory standards The integration of blockchain technology provides a secure and transparent framework for tracking credit transactions, significantly reducing the risk of fraud, as discussed in "Blockchain Revolution: How the Technology Behind Bitcoin Is Changing Money, Business, and the World."
Don Tapscott and Alex Tapscott (2014) emphasize that blockchain technology enhances the security and reliability of financial systems Furthermore, automating credit management processes can lower operational costs significantly By adopting automation in areas like debt collection and customer record management, financial institutions can realize substantial cost savings.
2.1.3 Credit Management Measurement Indicators v Cost to income ratio (CIR)
The Cost-to-Income Ratio (CIR) is a key financial metric that evaluates the operational efficiency of banks by comparing their operating expenses to operating income A lower CIR signifies greater efficiency, enabling banks to better manage credit resources Operating expenses encompass interest costs, labor, and depreciation, while operating income includes interest, fees, and commissions Efficient banks can enhance their risk assessment tools and credit evaluation processes, leading to lower default rates Conversely, banks that do not improve operational efficiency risk losing their competitive edge in fund mobilization and allocation Thus, the relationship between CIR and credit management is vital, as it significantly influences lending practices and overall financial performance.
Credit risk is a major concern in the banking sector, primarily evident through non-performing loans (NPLs), which pose a risk of non-payment for both principal and interest (Yurttadur et al., 2019) Effective credit management relies heavily on the categorization of loans, which in Vietnam is organized into five groups based on repayment punctuality and debt restructuring.
Loan Category Name Timeline Provisioning Rate
1 Standard Loans Less then 10 day 0%
2 Watchlist Loans From 10 to 30 days 5%
3 Substandard Loan From 30 to 90 days 20%
4 Doubtful Loans From 90 to 180 days 50%
According to Clause 8 of Article 3 of Circular 11/2021/TT-NHNN
Clause 6 Article 2 Chapter 1 of Decition on the issuance of regulation on the debts classification said “Bad debts” as known as Non-performing loans (NPL) is sum of group 3,4 and 5 debts Non-performance loan ratio is a ratio to estimate credit quality of credit institutions According to (Allen N Berger & Robert DeYoung,
Research indicates that troubled banks often exhibit a high ratio of non-performing loans prior to failure, highlighting the importance of asset quality as a key predictor of insolvency (Berger & DeYoung, 1997) Effective management of credit risk is crucial, as banks can only withstand a certain level of loan losses A rise in non-performing loans diminishes banks' lending capacity, resulting in stricter credit conditions that may hinder economic growth Therefore, implementing robust credit management practices is vital for maintaining a healthy loan portfolio and ensuring the long-term sustainability of financial institutions.
The Technology Investment Cost (CDS) is a vital metric for evaluating the financial dedication of commercial banks to technological advancements, which play a significant role in improving credit management performance Digital transformation has fundamentally changed the operational processes of these banks, strengthening relationships with customers and stakeholders while enhancing overall business efficiency.
Model innovation is crucial for banks to create client value while reducing costs, labor, and capital (Do et al 2022) A well-calculated Cost-Driven Strategy (CDS) enables banks to assess how effectively their technology investments enhance operational efficiency and customer service By investing in technology, banks can streamline processes and introduce innovative products and services, which boosts customer satisfaction and loyalty Those that strategically manage technology costs can secure a competitive advantage in the digital marketplace, maintaining their relevance in an evolving environment Understanding the connection between Technology Investment Costs (TIC) and overall bank performance is vital for promoting growth and sustainability in the financial sector.
The Loan to Deposit Ratio (LDR) is a vital financial indicator of a bank's liquidity and credit management efficiency A high LDR can enhance profitability by indicating that more deposits are being used for loans, which generates interest income; however, it may also raise concerns about potential liquidity challenges Conversely, a low LDR suggests inefficient use of deposits To achieve an optimal LDR, banks must implement effective credit management practices that maximize lending capabilities while minimizing risks associated with defaults and non-performing loans Thus, maintaining an appropriate Loan to Deposit Ratio is essential for sound credit management and the long-term viability of financial institutions.
EMPIRICAL RESEARCH OVERVIEW
Numerous studies have explored the impact of digital technology on credit management and the effectiveness of credit lending practices by commercial banks both in Vietnam and globally.
By employing the pooled OLS, fixed effect model (FEM), and random effect
The research conducted by Nguyen Bich Ngan and Nguyen Duc Hien (2024) explores the "Impacts of Digital Transformation and Basel III Implementation on the Credit Risk Level of Vietnamese Commercial Banks" using 16 model (REM) methods The authors find that while investing in technology for digital transformation and implementing Basel III may initially increase credit risk, factors such as digital transformation, ICT readiness, and bank profitability ultimately contribute to reducing credit risk levels Additionally, the study reveals a positive correlation between GDP growth, inflation, the loan-to-deposit ratio (LTD), and the capital adequacy ratio (CAR) with the non-performing loan (NPL) ratio The authors provide recommendations for commercial banks to enhance sustainability, safety, and improve credit risk management practices.
The research paper “Financial Technology and Bank Credit in Vietnam” by Lê Thị Thúy Hằng et al (2024) utilizes the VECM model for data regression, highlighting the importance of enhancing credit management through digital technology to ensure stability in the financial and banking sectors The authors advocate for strengthening legal frameworks to reduce risks linked to the use of digital technology in credit provision.
A study titled "The Impact of Digital Transformation on Credit Risk in Commercial Banks: An Empirical Study in Vietnam" by Nguyễn Thị Thiều Quang (2023) utilized FEM and REM models to analyze the effects of digitalization on credit risk in Vietnamese commercial banks from 2010 onwards.
In 2021, research revealed that digitalization in banking does not decrease credit risk; instead, it often exacerbates it due to increased lending volumes As the banking sector is still navigating the early stages of digital transformation, it is essential for banks to focus on improving credit risk management while accelerating their digitalization processes to strengthen overall risk management effectiveness.
The study conducted by Nguyễn Văn Thuỷ (2023) titled “Tác động của chuyển
The article "17 Digital Transformation and the Competitiveness of Vietnamese Commercial Banks" examines the impact of digital transformation on the competitiveness of these banks By analyzing data from 20 Vietnamese commercial banks between 2008 and 2020 and employing the ICT readiness index, the study demonstrates a positive correlation between digital transformation and bank competitiveness Furthermore, the research offers actionable recommendations for banks to enhance their competitiveness during the digital transformation process.
The article "The Impact of Digital Transformation on Performance: Evidence from Vietnamese Commercial Banks" by Do et al (2022) investigates how digital transformation influences the performance of commercial banks in Vietnam, focusing on variations among different bank sizes Utilizing a quantitative research approach and the System Generalized Method of Moments (SGMM) framework, the study analyzes data from 13 joint-stock commercial banks from 2011 to 2019 The results reveal that digital transformation positively impacts the performance of these banks, with larger institutions benefiting more significantly Additionally, factors such as liquidity risk, loan balance to total assets, and inflation negatively impact performance, while years of operation, GDP, and digital transformation efforts enhance overall performance.
In the study "Exploring Fintech Innovations and Their Potential to Transform the Future of Financial Services and Banking" by Temitope Oluwafunmike Sanyaolu et al (2024), the authors examine the transformative impact of fintech innovations on financial services and banking The research highlights how technologies like blockchain, artificial intelligence, machine learning, and digital payment systems are disrupting traditional financial models, fostering financial inclusion, improving operational efficiency, and enhancing customer experiences.
Fintech is transforming customer expectations and increasing competitive pressures, resulting in the creation of personalized financial products for a wider audience, including the unbanked Traditional financial institutions encounter major challenges in adapting to these changes, particularly in terms of technological capabilities, regulatory compliance, and earning consumer trust The study highlights that although fintech innovations can revolutionize the financial services sector, successful integration necessitates careful management of regulatory, technological, and ethical challenges.
As a result of the research conducted by Victor Olufunsho Hambolu et al
A 2022 study titled "The Impact of Credit Risk on the Profitability of Commercial Banks in Nigeria" examines the connection between credit risk and profitability in Nigerian deposit money banks using Fixed Effects Model (FEM) and Random Effects Model (REM) The research reveals that the ratio of non-performing loans to total assets (NPL) and the capital adequacy ratio (CAR) have a negative impact on bank profitability, while the ratio of loan loss provisions to total assets (LLP) shows a positive correlation Although the size of the bank has a positive but insignificant relationship with profitability, the ratio of loans and advances to total deposits (LTD) displays a negative and insignificant effect These findings highlight the importance of effective credit risk management for larger banks to enhance profitability, emphasizing the need for Nigerian banks to improve their credit risk control policies to mitigate default risks and maintain competitiveness.
The study "The Effect of Digital Channel Migration, Automation and Centralization on the Efficiency of Operational Staff of Bank Branches" by Ortakửy & ệzsỹrỹnỗ (2019) highlights a significant transition from traditional physical banking locations to digital channels Key factors such as staff numbers, training duration, and customer adaptability during digital transformation have greatly influenced banking operations, especially in the realm of credit management.
The study "Financial Technology in Banking Industry: Challenges and Opportunities" by Ahmed T Al Ajlouni and Monir Al-Hakim (2018) explores the significant role of digital technology in the financial and banking sectors It provides an overview of the global fintech market, highlighting the impact of digital transformation on the banking industry, as well as the necessary responses to these changes The research examines future scenarios, opportunities, and challenges associated with the integration of digital technology, while also proposing directions for further research This includes an analysis of the anticipated effects of technology on banks in Arab countries, their readiness to enter the fintech era, and customer willingness to adopt technological innovations in finance and banking.
According to the research “Bank-related, Industry-related and Macroeconomic Factors Affecting Bank Profitability: A Case of the United Kingdom.” by(Saeed,
A study utilizing FEM and REM models indicates that internal factors such as capital, loans, bank size, deposits, and liquidity positively influence profitability metrics like return on assets (ROA) and return on equity (ROE) Conversely, while interest rates boost bank profitability, GDP and inflation exert negative effects This research underscores the importance of analyzing these determinants to improve banks' financial performance during economic fluctuations, providing valuable insights for strengthening the resilience of financial institutions in a changing economic environment.
SUMMARY TABLE OF RESEARCH STUDIES
Table 2 2 Summary of Research Studies related to Credit Management Effectiveness
Year Research Title Country Variables Result
The Impact of Digital Transformation on Performance:
Evidence from Vietnamese Commercial Banks
Liquidity risk (TK), Loan balance to total assets (TD), INF, Years of operation (NHD), GDP, and Digital transformation (CDS)
TK, TD, and INF negatively affect performance, while factors like NHD, GDP, and CDS contribute positively to the overall performance
Tác động của chuyển đổi số tới năng lực cạnh tranh của các ngân hàng thương mại Việt Nam
Bank size (Size), Equity ratio (ETA), Loan to assets (LTA), Loan to deposit (LTD), ICT Index
All variables have a positive impact on the competitiveness of banks during the digital transformation period
Tác động của chuyển đổi số đến rủi ro tín dụng của ngân hàng thương mại: nghiên cứu thực nghiệm tại Việt Nam hàng thương mại Việt Nam
Return on assets (ROA) Loan Loss Provision (LLP), Bank size (Size), Loan to deposit (LTD), GDP
ROA, Size, and GDP affect positively on credit risk of the bank while LLP shows opposite effect
Công nghệ tài chính và tín dụng ngân hàng tại Việt
Number of electronic device users (PUE); Number of commercial
All variables have a positive impact on financial technology in
21 bank branche (BBA); Number of ATM (ATM) the banking credit
Impacts of Digital Transformation and Basel III Implementation on the Credit Risk Level of Vietnamese Commercial Bank
ICT Index, Basel III, Return on assets (ROA), Loan to depoits (LTD), INF, GDP
ICT Index, Basel III, ROA shows a negative relationship with Non-Performing Loan (NPL) whereas LTD, INF and GDP have a positive result
Bank-related, Industry-related and Macroeconomic Factors Affecting Bank Profitability:
A Case of the United Kingdom
Interest rates Loans, Deposits, Liquidity and Bank size, GDP, INF
Interest rates Loans, deposits, liquidity and bank size are positively correlated ROA while GDP and INF have a negative effect
Financial Technology in Banking Industry:
Integrating digital technology plays a vital role in financial and banking sectors
The Effect of Digital Channel Migration, Automation and
Number of staff, training duration, and
All variables have a positive impact on bank’s
Centralization on the Efficiency of Operational Staff of Bank Branche customer adaptability credit management
The Impact of Credit Risk on the Profitability of Commercial Banks in Nigeria
Non- performing loans (NPL), Loan to deposit (LTD), Loan loss provisions (LLP)
NPL, LTD negatively affect bank profitability, and LLP demonstrates a positive correlation
Exploring Fintech Innovations and Their Potential to Transform the Future of Financial Services and Banking
Blockchain, Artificial intelligence, Machine learning, and Digital payment systems
Technology has a positive impact on the banking and financial services sector
CONCLUSION
Chapter 2 provides a comprehensive analysis of credit management efficiency, exploring the effects of digital technology and other influencing factors on credit governance It synthesizes findings from various studies conducted by financial institutions, with a particular emphasis on commercial banks globally and locally Each research contribution offers critical insights into the credit management practices at the Military Commercial Joint Stock Bank, highlighting the diverse elements shaped by the distinct variables of each investigation.
In this research, the performance of credit management (PCM) is designated as the dependent variable within the model To analyze the factors affecting bank
In the study, the author integrates both internal and external elements as independent variables to ensure a comprehensive analysis This method prevents the exclusion of critical components and improves the overall understanding of their impact on the research model These foundational insights will be further explored in Chapter 3, which will concentrate on variable selection and model development.
RESEARCH MODEL AND METHODOLOGY
RESEARCH PROGRESS
Source: Synthesized by the author
Step 1: Identify the issues, subjects, scope, and duration of the study related to the factors affecting credit management in connection with digital transformation at the Military Commercial Joint Stock Bank
Step 2: Investigate the relevant concepts and theoretical frameworks associated with the topic of the study combining and reviewing previous researchs both domestically and internationally in general as well as banks in Vietnam and the Military Commercial Joint Stock Bank in particular
Step 3: Develop the model by collecting data from the quarterly financial reports of
Step 1 • Identify the research statement
Step 2 • Integrate theory and review literature
Step 3 • Construct the research model
Step 5 • Present and discuss research findings
From 2013 to 2023, the Military Commercial Joint Stock Bank has undergone significant changes This study aims to conduct a descriptive statistical analysis to provide a comprehensive overview of both independent and dependent variables Additionally, the research will formulate hypotheses to establish a robust research model.
Step 4: Carry out regression analyses by using the Python programming framework and the Multiple Linear Regression algorithm
Step 5: Present and discuss the research findings obtained from the model testing results and then comparing them with the findings from previous foundational studies
Step 6: Summarize the findings and offer recommendations for enhancing credit management influenced by digital technology at the Military Commercial Joint Stock Bank
To ensure scientific rigor, the research is conducted in two phases: a preliminary study and a formal study
The research follows a defined theoretical framework, utilizing audited quarterly financial reports from the Military Commercial Joint Stock Bank over a decade, from 2013 to 2023, to ensure accuracy and relevance The author references and selects previous research models while applying suitable data analysis methods pertinent to the thesis topic To facilitate formal research, the independent variables are adjusted to reflect real-world conditions and meet data collection requirements.
The formal study will commence after the collection and selection of data derived from previous analyses relevant to the research topic Following the gathering and filtering of this data, its completeness and accuracy will be evaluated Finally, the cleaned data will be prepared for input into the model using Python.
Statistical analysis will be performed using the Python programming environment, focusing on the Multiple Linear Regression algorithm This process will involve examining and constructing a multivariate regression model to yield accurate and realistic results.
RESEARCH MODEL
The authors build upon prior research to explore how digital transformation affects credit management effectiveness in the banking sector, considering both domestic and international contexts over different timeframes This model analyzes micro and macro factors that impact credit management effectiveness, clearly illustrating the connection between digital transformation and credit management capabilities.
PCMit = 𝛼 + 𝛽1CIRit + 𝛽2NPLit + 𝛽3CDSit + 𝛽4LTDit + 𝛽5LLPit + 𝛽6GDPt
PCM: Performance of Credit Management of the commercial banks (i) at time (t)
CIR: Cost to Income ratio of the commercial banks (i) at time (t)
NPL: Non-Performing Loan ratio of the commercial banks (i) at time (t)
CDS: Cost of Technology Investment of the commercial banks (i) at time (t)
LTD: Loan to Deposit ratio of the commercial banks (i) at time (t)
LLP: Loan Impairment Charges to Total Outstanding Loans ratio of the commercial banks (i) at time (t)
GDP: Economic Growth rate of Viet Nam at the time (t)
Among the factors affecting credit risk prevention can be divided into two groups:
Group 1: Group of factors reflecting the attributes of banks including Cost to
Income ratio (CIR), Non-Performing Loan ratio (NPL),Cost of Technology
Investmen(CDS) ,Loan to Deposit ratio(LTD),Loan Impairment Charges to Total Outstanding Loans ratio (LLP)
Group 2: Group represents macro factor including Economic Growth rate (GDP)
RESEARCH DATA
From 2013 to 2023, quarterly research data was collected using reliable sources, including the Military Commercial Joint Stock Bank's financial statements and annual reports from its official website Macroeconomic data was obtained from the General Statistics Office of Vietnam, ensuring high accuracy and reliability These carefully selected data sources enhance the research model's quality and provide valuable insights into the bank's financial situation and operational performance throughout the study period The data used in this research is illustrated in Table 3.1 below.
Table 3 1 Variables in the research model
PCM Performance of Credit Management
Reflecting the results of credit management activities
(Nhân, 2023; Paraskevi Katsiampa et al., 2022; Nguyen Kim Chi et al.,
The ratio of operating expenses to operating income -
NPL Non- performance loans ratio
The rate of loans that are in default of the bank per year
& Đặng Tiến Đạt, 2024; Nguyen Bich Ngan & Nguyen Duc Hien, 2024;
Victor Olufunsho Hambolu et al., 2022; Nguyen Kim Chi et al.,
CDS Cost of technology investment
The expenses associated with investing in software technology
LTD Loan to Total The proportion + (Đào Mỹ Hằng et
29 deposit ratio of loans issued compared to total deposits al., 2024; Phan Minh Anh & Đặng Tiến Đạt, 2024; Victor Olufunsho Hambolu et al., 2022;
The ratio of loan impairment charges to total outstanding loans
(Annor & Obeng, 2018; Paraskevi Katsiampa et al.,
(Đào Mỹ Hằng et al., 2024; Nhân, 2023; Saeed,
Note: + stands for posive impact, - stands for negative impact
Source: Synthesized by the author
3.3.2 Measurement of research variables and research hypothesis
To achieve the objectives of the study, the author has formulated the following research hypotheses:
Performance of Credit Management (PCM)
The main source of revenue for banks comes from lending activities Thus, the
The profit-to-asset ratio, set at 30, serves as a key indicator for assessing the efficiency of credit management, with PCM identified as the dependent variable in the research model This calculation method was established by Nhân (2023), Paraskevi Katsiampa et al (2022), and Nguyen Kim Chi et al (2021) A higher profit-to-asset ratio indicates that a bank is effectively managing its credit.
Cost to Income ratio (CIR)
The cost-to-income ratio, calculated by dividing operating expenses by operating income, is a crucial metric for commercial banks, reflecting their efficiency and directly influencing credit management performance Research by Phan Minh Anh & Đặng Tiến Đạt (2024), Nhân (2023), and RAJINDRA et al (2021) indicates that a higher cost-to-income ratio correlates with lower bank returns Consequently, this study posits that the cost-to-income ratio (CIR) negatively affects the performance of credit management in commercial banks.
H 1 : The cost to income ratio (CIR) has a negative impact on performance of credit management of MB Bank
Non-Performing Loan ratio (NPL)
The non-performing loan (NPL) ratio is a critical concern for commercial banks, as it significantly affects their credit management practices Research indicates that a high NPL ratio is a vital indicator of asset quality and directly impacts profitability When a loan becomes non-performing, defined as missing scheduled payments for at least 90 days, banks must allocate provisions for potentially unrecoverable loans Consequently, an increase in the NPL ratio requires banks to set aside larger reserves, highlighting the importance of effective loan management strategies.
A higher non-performing loan (NPL) ratio negatively impacts the profitability and financial stability of commercial banks by increasing credit risk This suggests that an elevated NPL ratio will adversely affect the credit management performance of these banks.
NPL = Total Non−performance Loans
H2: The non-performing loan ratio (NPL) has a negative impact on performance of credit management of MB Bank
Cost of Technology Investment (CDS)
According to research by Do et al (2022), investment in technology, specifically through Credit Decision Systems (CDS), significantly enhances credit management performance in commercial joint stock banks By boosting their digital technology investments, these banks can improve the efficiency of credit management practices, resulting in better decision-making and more precise risk assessments.
As banks allocate greater resources to technological advancements, they are likely to experience heightened effectiveness and efficiency in their credit management operations
H 3 : The cost of technology investment (CDS) has a positive impact on performance of credit management of MB Bank
Loan to Deposit ratio (LTD)
A high loan-to-deposit ratio boosts a bank's profitability and indicates confidence in borrowers' repayment capabilities, as supported by research from Đào Mỹ Hằng et al (2024) and Phan Minh Anh & Đặng Tiến Đạt.
Research by Victor Olufunsho Hambolu et al (2022) and RAJINDRA et al (2021) highlights a positive correlation between the loan-to-deposit ratio and effective credit management This leads to the hypothesis that enhancing the loan-to-deposit ratio is likely to boost the efficiency of credit management practices.
H 4 : The loan to deposite ratio (LTD) has a positive impact on performance of credit management of MB Bank
Loan Impairment Charges to Total Outstandng Loans (LLP)
Research by Paraskevi Katsiampa et al (2022) indicates that Loan Loss Provisions negatively impact bank efficiency, as evidenced by a decrease in Profitability Cost Management (PCM) An increase in credit risk requires banks to allocate larger provisions, which restricts working capital and ultimately reduces commercial banks' profitability Excessive provisions for credit risk signal poor credit management and can lead to lower credit quality Furthermore, Victor Olufunsho Hambolu et al (2022) also highlight a negative correlation between loan loss provisions (LLP) and profitability.
H 5 : The loan impairment charges to total outstandng loans ratio (LLP) has a egative impact on performance of credit management of MB Bank
The anticipated rise in GDP is expected to improve the financial stability of borrowers, thereby increasing their creditworthiness As a result, financial institutions are likely to implement more effective credit management practices and become more willing to extend credit under favorable conditions This correlation between GDP growth and enhanced credit management performance is supported by previous studies (Đào Mỹ Hằng et al., 2024; Nhân, 2023; Saeed, 2014).
H 6 : The economic growth rate (GDP) has a positive impact on performance of
33 credit management of MB Bank
3.4 Introduction to the Python Programming Language Platform for Data Analysis
3.4.1 Introduction to the Python Programming Language Platform
Python is a high-level, interpreted programming language created by Guido Van Rossum in the late 1980s, focusing on readability and clear syntax Officially released in 1994, it has gained popularity for its flexibility and extensive library support, making it suitable for both small and large projects.
Python's simple syntax makes it beginner-friendly and accessible for those new to programming It supports multiple programming paradigms such as structured, object-oriented, and functional programming, making it suitable for diverse projects Python's extensive libraries for scientific computing contribute to its popularity in artificial intelligence (AI) and natural language processing Furthermore, Python is equipped with features that facilitate data analysis and visualization, providing numerous libraries and APIs for effective data presentation.
+ Python is a language with a clear format, structured organization, and concise syntax
+ It is available on all operating system platforms
+ Python has a vast ecosystem of libraries such as Scikit-learn and Pandas, supporting various fields including data science, artificial intelligence, web development, and automation
+ Python can integrate with other languages, such as C++ and Java, allowing the use of libraries that are not directly supported by Python
No product is ever completely perfect, and Python is no exception Although it is a popular programming language, Python does have certain limitations, including the following:
+ Python tends to be slower than compiled languages like C or C++ because it is an interpreted language, which can affect the execution speed of applications
Python tends to consume more memory than some other programming languages Additionally, it encounters difficulties with multithreading because of the Global Interpreter Lock (GIL), which can affect the performance of applications that need to execute multiple threads concurrently.
Python is highly suitable for finance and banking topics, making it an ideal choice for the author's research Consequently, the author utilized Python version 3.9.13 for data analysis in this study.
Pandas is a highly versatile Python library that offers efficient and flexible data structures for data analysis It is considered one of the top tools for data analysis across programming languages This study employs Pandas to conduct descriptive statistics on value series, facilitating the examination of variables and the resolution of issues like Null values and invalid data.
CONCLUSION
In Chapter 3, the author outlines a quantitative research method aimed at assessing how digital technology influences the effectiveness of credit management at Military Commercial Joint Stock Bank, utilizing quarterly data for analysis.
MB Bank during the period 2010 – 2023 has been utilized to construct a model measuring the influence of digital transformation on credit management
This chapter outlines the research techniques, model construction, and the integration of various factors, detailing the stages of the research process, including descriptive statistics, model selection, and testing procedures It establishes a solid foundation for analyzing the impact of independent variables on the dependent variable by clearly defining data collection methods and sampling techniques Additionally, validation methods such as correlation analysis and multiple regression are employed to clarify the relationships among the variables in the research model.
RESEARCH RESULT AND DISCUSSION
OVERVIEW OF THE DEVELOPMENT PROCESS AND DIGITAL TECHNOLOGY
Military Commercial Joint Stock Bank was established on November 14,
In 1994, a bank was established under the Ministry of Defense with an initial charter capital of 20 billion VND, backed by four main shareholders: Viettel, State Capital Investment Corporation, Vietnam Helicopter Corporation, and Saigon Newport Corporation The bank received approval to be listed on the Ho Chi Minh City Stock Exchange (HOSE) beginning in November.
In its initial ten years, MB concentrated on crafting a robust business strategy, setting operational standards, and solidifying its brand identity A pivotal moment came in 2003 with major reforms in its systems and workforce By 2004, MB distinguished itself as a pioneer in Vietnam by becoming the first bank to issue shares through public auctions.
During a pivotal phase, MB focused on innovation and growth by investing significantly in technology, resulting in the successful launch of the T24 core banking system and the introduction of MIC shares in 2007 To boost operational efficiency, MB established clear distinctions between management and business operations at its headquarters and branches, while also enhancing customer segmentation and development strategies.
In this phase, MB strengthened its domestic presence while also expanding internationally by establishing a branch in Laos On November 1, 2011, the bank took a significant step by officially listing its shares on the Ho Chi Minh City Stock Exchange.
MB has upgraded its CoreT24 system from R5 to R10, emphasizing its commitment to technological advancements By 2016, the bank diversified its offerings by expanding into insurance and consumer finance, reinforcing its identity as a comprehensive financial institution.
Since 2017, MB has taken the lead in Vietnam's digital transformation initiatives, with the goal of becoming a paperless bank In collaboration with IBM in
2018, the bank has launched various digitization projects and high-quality platform initiatives As of now, MB has established itself as a leader in banking technology, demonstrating sustainable and robust growth
+ Outstanding Green Credit Bank Award by Vietnam Banking Association (2020) +Best Digital Banking Solution for Green Credit by Vietnam Fintech Day (2021) + Best Innovation in Financial Services by The Asian Banker (2022)
+ Excellent Customer Service by Asia Pacific Customer Service Consortium (2022) + Top 1 favorite banking app on the App Store in Viet Nam (from 2020 to 2022)
+Outstanding Digital Transformation in Credit Services by Vietnam Digital Awards
+ Best Digital Branch Project & Best Mobile Banking Application by The Asset
MB Bank's recent technology awards underscore the profound impact of digital transformation on credit management By implementing innovative digital solutions, the bank has significantly enhanced its credit assessment processes, leading to improved efficiency and accuracy This recognition highlights the effectiveness of these strategies in credit management and their role in bolstering the bank's overall health and competitiveness Ultimately, the journey of digital transformation has been crucial in refining credit practices at MB Bank.
40 management, helping MB Bank maintain a notable position within the financial industry.
RESULTING THE RESEARCH
Null values indicate the absence of data for a variable, making it crucial to identify both null and non-null variables when working with data in the Pandas library As illustrated in Picture 4.1, the presence of only non-null dependent and independent variables signifies that all variables hold significance in the regression model.
Picture 4 1 Results of reading and processing data
Source: Data processing results in Python
Table 4 1 Descriptive statistics of variables in the research sample
PCM CIR NPL CDS LTD LLP GDP count 36 36 36 36 36 36 36 mean 0.004420 0.376184 0.015296 264986.527778 0.641686 0.017667 0.060533 std 0.001514 0.055742 0.004941 239773.144852 0.113409 0.004484 0.011096 min 0.000950 0.276330 0.007613 32101.000000 0.079887 0.011541 0.034100
Source: Summary statistics in Python
To understand the essential characteristics of the research variables, such as their maximum and minimum values, mean, and the discrepancies between the mean and actual values, conducting a descriptive statistical analysis of the research sample is essential The findings of this analysis are detailed in Table 4.1.
The Performance of Credit Management (PCM) has an average value of 0.004420 and a standard deviation of 0.001514, with recorded values ranging from a minimum of 0.000950 to a maximum of 0.007067 This metric reflects the effectiveness of credit management in banks utilizing digital technology The low average suggests that banks may encounter challenges in their credit management processes However, since 2018, the increasing focus on digital transformation has led to a positive trend in PCM values, with most values surpassing the average and nearing the maximum level, indicating that digital technology adoption is enhancing credit management effectiveness.
The Cost to Income Ratio (CIR) averages 0.376, with a standard deviation of 0.056, indicating effective cost management by banks The CIR ranges from a minimum of 0.276 to a maximum of 0.511, reflecting an average of approximately 38%, which suggests strong financial performance in controlling expenses relative to income.
The Non-Performing Loan (NPL) Ratio averages at 0.015296, indicating effective credit risk management by the bank, with a standard deviation of 0.004941 The NPL ratio ranges from a low of 0.007613 to a high of 0.026444, reflecting a healthy proportion of non-performing loans.
The average cost of technology investment (CDS) among banks is approximately 264,986, with a standard deviation of 239,773, indicating a wide range of expenses from 32,101 to 761,254 This investment reflects the funds banks dedicate to technology software, highlighting their efforts to modernize and improve operational efficiency and competitiveness The considerable variability in these costs points to significant differences in technological strategies adopted by banks, especially in recent years.
The Loan to Deposit Ratio (LTD) averages 0.641686, with a standard deviation of 0.113490, indicating that banks generally use about 64% of customer deposits for lending purposes This ratio, which ranges from a minimum of 0.079887 to a maximum of 0.740628, highlights the primary business strategy of banks in utilizing customer deposits to support loan activities.
The Loan Impairment Charges to Total Outstanding Loans (LLP) ratio averages 0.641686, with a standard deviation of 0.004484 and values ranging from 0.011541 to 0.026636 This metric assesses the proportion of provisions for non-performing loans relative to total outstanding loans The relatively low average suggests that banks are effectively managing credit risk by maintaining adequate provisions, indicating a prudent approach to risk management.
Economic Growth Rate (GDP) has a mean value of 0.060533, with a standard deviation of 0.011096 The minimum recorded value is 0.034100 and the maximum
The GDP, currently at 0.077200 with an average growth rate of around 6%, signifies a stable economic environment conducive to banking expansion and improved performance However, fluctuations in various factors can influence economic stability and, consequently, banks' credit management Thus, it is crucial for banks to monitor macroeconomic indicators, especially GDP, to mitigate risks and enhance operational efficiency.
Picture 4 2 Results of outliers detection
Source: Data processing results in Python
An initial analysis revealed several outliers within the dataset, prompting the author to filter and clean the data, as illustrated in Figure 4.2 After the first round of outlier removal, the number of observations decreased from 44 to 36 Although NPL and LTD remained as outliers, they did not significantly affect the regression results While removing outliers can sometimes lead to errors and loss of contextual relevance, it may be justified if an outlier is exceptionally rare and does not represent the general population, as it could introduce substantial inaccuracies into the model Conversely, frequent outliers may still provide valuable insights and should be considered in the analysis.
In his 2020 study, Jochen Wilhelm emphasized the importance of retaining all variables from the dataset, noting that excluding any data points could diminish the model's overall value Consequently, the author decided to keep all 36 observations following the initial data cleaning process.
Source: Heat.corr in Python
Figure 4.1 presents a Heatmap that highlights the correlation coefficient matrix among the model's variables, with coefficients ranging from -0.73 to 1.00 The dependent variable, PCM, demonstrates a strong relationship with various independent variables, reflecting a mix of positive and negative correlations Notably, PCM exhibits a significant correlation with CIR (-0.73) and CDS (0.73), indicating that credit management performance is positively linked to the bank's investment in software technology.
The analysis reveals that PCM exhibits an inverse relationship with usage costs, while showing a positive correlation with LTD (0.5) and LLP (0.47) In contrast, PCM negatively correlates with NPL and GDP, registering values of -0.29 and -0.27, respectively Additionally, the Heatmap indicates that the correlation coefficients among the variables remain below 0.8, alleviating concerns about multicollinearity within the model.
Source: Linear Regressor in Python
The regression model demonstrates an R-Square (R²) value of 0.7640, indicating that the six independent variables—Cost to Income Ratio (CIR), Non-Performing Loan Ratio (NPL), Cost of Technology Investment (CDS), Loan to Deposit Ratio (LTD), Loan Impairment Charges to Total Outstanding Loans (LLP), and Economic Growth Rate (GDP)—explain 76.40% of the variability in the Performance of Credit Management (PCM) Consequently, 23.60% of the variability remains attributable to factors outside the scope of this analysis.
The analysis reveals that all independent variables—CIR, NPL, CDS, LTD, LLP, and GDP—have a significant impact on the dependent variable, PCM, of the Military Commercial Joint Stock Bank This relationship can be expressed through a regression equation.
PCM= 0.00997 - 0.0172*CIR - 0.0701*NPL+ 1.711*10 -9* CDS+ 0.0022*LTD
- 0.0186*LLP + 0.00798*GDP This result explains that:
- Under the condition that all other factors remain constant, when the Cost to Income Ratio (CIR) increases by 1%, the Performance of Credit Management (PCM) will decrease by 0.427%
- Under the condition that all other factors remain constant, when the Non-Performing Loan Ratio (NPL) increases by 1%, the Performance of Credit Management (PCM) will decrease by 0.0701%
- Under the condition that all other factors remain constant, when the Cost of Technology Investment (CDS) increases by 1 million VND, the Performance of Credit Management (PCM) will increase by 1.711*10 -9 %
- Under the condition that all other factors remain constant, when the Loan to Deposit Ratio (LTD) increases by 1%, the Performance of Credit Management (PCM) will increase by 0.0022%