Statement of the problem and rationale for the study
Banks are crucial to the financial systems of countries worldwide, serving as the backbone of national economies, particularly in bank-based economies like Vietnam However, banking activities are constantly exposed to potential risks stemming from both macroeconomic and microeconomic factors.
The Covid-19 pandemic has significantly impacted income levels, leading to a surge in loan demand while many businesses face stagnation This situation has diminished the debt repayment capacity of both individuals and companies, resulting in a higher ratio of non-performing loans in banks According to Messai and Jouini (2013), during economic growth, loans can be approved regardless of customer reputation, whereas in recessionary periods, non-performing loans tend to rise sharply Can Van Luc (2021) emphasizes this trend, highlighting the challenges faced in the latter half of the year.
As of June 30, 2022, the expiration of Circular 14/2021/TT-NHNN may lead to a clearer representation of potential non-performing loans on banks' balance sheets, increasing the associated risks within the banking industry Additionally, the impending expiration of Resolution 42/2017/QH14 on August 15, 2022, will conclude the pilot mechanism designed for addressing non-performing loans, further complicating the management of these financial challenges.
Non-performing loans remain a significant issue for the Vietnamese banking industry, serving as a crucial indicator of the economy's health Various domestic and foreign studies have explored the factors influencing non-performing loans in commercial banks across different countries and time periods However, these determinants can vary based on the specific economic context, research methodologies, and operational characteristics of the banks involved.
The examination of bank-specific and macroeconomic factors influencing non-performing loans (NPLs) is crucial for effective management and reduction of this ratio In 2020, Techcombank, known for having one of the lowest NPL rates in Vietnam's commercial banking sector, faced a significant challenge in 2021 as its NPLs surged by 77% due to the adverse effects of Covid-19 Addressing these NPLs, in line with the directives from the Governor of the State Bank of Vietnam, highlights the importance of identifying their determinants Consequently, my graduation thesis focuses on "The Determinants of Non-Performing Loans at Vietnam Technological and Commercial Joint Stock Bank," aiming to yield insights that could enhance Techcombank's loan portfolio quality and contribute positively to the broader banking system.
Literature reviews
Foreign research
In her 2014 study, Bruna Skarica examined the factors affecting non-performing loan (NPL) ratios in several emerging European markets, focusing on seven Central and Eastern European countries: Bulgaria, Croatia, the Czech Republic, Hungary, Latvia, Romania, and Slovakia, during the period from Q3 2007 to Q3 2012 Utilizing a fixed effects estimator on a panel dataset, this research is notable as the first empirical analysis of its kind in the CEE region, drawing on aggregate country-level data regarding problem loans The findings revealed that the economic slowdown significantly impacts NPL ratios, with substantial coefficients linked to GDP, unemployment, and inflation rates.
Roland Beck, Petr Jakubik and Anamaria Piloiu (2015) investigated the macroeconomic causes of non-performing loans (NPLs) across 75 countries from
2005 - 2014 The study’s result indicated that the real GDP growth, stock prices,
The currency rate and lending interest rate significantly influence non-performing loan (NPL) ratios, particularly in nations with pegged or controlled exchange rates, where the extent of foreign exchange lending to unhedged borrowers plays a crucial role Additionally, in countries with a substantial stock market relative to GDP, fluctuations in share prices have a more pronounced impact on NPL ratios Notably, alternative econometric specifications do not alter these findings.
A study conducted by Laxmi Koju, Ram Koju, and Shouyang Wang (2017) examined the determinants of non-performing loans in Nepalese commercial banks from 2003 to 2013, analyzing data from 30 banks with 7 bank-specific and 5 macroeconomic variables The findings revealed that the GDP growth rate, capital adequacy, and inflation rate negatively impacted non-performing loans, while the export to import ratio, inefficiency, and asset size exhibited a positive relationship with non-performing loans.
Jordan Kjosevski, Mihail Petkovski, and Elena Naumovska (2018) conducted a decade-long study on the bank-specific and macroeconomic determinants of non-performing loans in the Republic of Macedonia, covering the period from 2003 to 2014 Utilizing an autoregressive distributed lag modeling approach and a co-integration model with quarterly time series data, the research found that return on assets (ROA), loan growth, and GDP growth positively influence non-performing loans, whereas inflation has a negative and statistically significant effect on them.
Junkyu Lee et al (2019) carried out the research on “Non-performing loans in
A study titled "Asia: Determinants and Macrofinancial Linkages" analyzed 165 commercial banks in Asia from 1995 to 2014, employing a dynamic panel model to identify the determinants of non-performing loans (NPLs) The findings revealed that credit growth and excessive bank lending significantly increase NPLs, while factors such as GDP growth rate, credit supply, and unemployment rate also contribute to a higher NPL ratio Although the variables examined are generally consistent with previous studies, this research introduced additional variables like ETA, ROE, and LDR ratio Importantly, the study highlighted that the financial crisis in Asia played a crucial role in the rise of non-performing loans among emerging Asian banks.
Domestic research
In his 2017 study, "Analysis of Factors Affecting Non-Performing Loans of Joint Stock Commercial Bank for Foreign Trade of Vietnam - Hue Branch," Nguyen Phuoc Thien employed the OLS model and found that the ratio of non-performing loans negatively correlates with economic growth, while return on assets (ROA) shows a positive correlation with the non-performing loans ratio from the previous period This research highlights the key determinants influencing non-performing loans in Vietnamese commercial banks.
Between 2005 and 2011, a study by Do Quynh Anh and Nguyen Duc Hung (2013) identified key factors influencing non-performing loans in commercial banks The research revealed that inflation, the GDP growth rate, and the previous year's non-performing loans significantly affect current non-performing loans Additionally, it was found that the size of the bank has a positive correlation with non-performing loans.
Between 2008 and 2017, Nguyen Thi Trang (2018) examined the factors influencing non-performing loans at 29 Vietnamese commercial banks The study revealed that both macroeconomic and bank-specific factors significantly affect the non-performing loans ratio Notably, indicators such as economic growth, unemployment rate, and bank size are inversely related to non-performing loans, whereas inflation rates and the previous year's non-performing loans ratio exhibit a positive correlation with current non-performing loans.
Nguyen Thi Nhu Quynh et al (2018) conducted a study on the factors affecting non-performing loans (NPLs) in 25 Vietnamese commercial banks from 2006 to 2016 Utilizing models such as Pooled OLS, FEM, and REM, the study identified FEM as the most suitable model To address heteroskedasticity, the Feasible Generalized Least Squares regression model was applied The findings revealed that economic growth rate, bank credit growth, and unemployment rate negatively influenced the NPLs ratio, while inflation and the previous year's NPLs ratio had positive effects However, no significant relationship was found between bank-specific determinants, including bank size and return on equity ratio, and the NPLs ratio.
Le Minh Nhat (2015) conducted an empirical analysis of the determinants of non-performing loans (NPLs) at 11 Vietnamese commercial banks from 2006 to 2014, revealing that credit growth rate and bank income negatively impacted NPLs, while loan loss reserve ratio, previous year NPLs, and inflation had positive correlations Similarly, Tran Thi Phuong Hoa (2016) examined NPLs at 25 Vietnamese commercial banks during the 2006-2015 period, finding that the loan loss reserve to NPL ratio and previous year NPLs exhibited similar effects, while credit growth rate, return on assets (ROA), and consumer price index (CPI) were positively associated with NPLs.
Objectives
This study aims to assess the factors influencing non-performing loans (NPLs) at Techcombank, with the ultimate goal of providing recommendations to authorities to effectively reduce the NPL ratio.
To achieve this goal, the thesis includes 4 tasks First, systematization the theoretical framework related to non - performing loans and the determinants of non
This article focuses on the analysis of performing loans and the evaluation of non-performing loans (NPLs) at Vietnam Technological and Commercial Joint Stock Bank (Techcombank) It aims to measure the factors influencing the NPL ratio at Techcombank and provide solutions and recommendations to enhance the management of non-performing loans.
Scope of the study
This thesis will concentrate on measuring and evaluating the determinantas of non - performing loans of Vietnam Technological and Commercial Joint Stock Bank in the period 2014 - 2021.
Research Methodology
Thesis uses both qualitative and quantitative methods as below:
Qualitative method: From financial statements, annual reports of Vietnam
Between 2014 and 2021, an analysis of non-performing loans at Techcombank was conducted using various methods, including comparison, synthesis, data analysis, and statistical evaluation This assessment incorporates detailed tables and graphs to illustrate the current status of non-performing loans within the bank.
This thesis employs a quantitative approach, analyzing Techcombank's quarterly financial statements from 2014 to 2021, alongside macroeconomic data sourced from the IMF, ADB, WB, and reports from Vietnam's General Statistics Office and State Bank The estimation technique utilized in this study is Ordinary Least Squares regression (OLS).
Structure of the thesis
The thesis is structured into four chapters and includes essential components such as a table of contents, an introduction, a conclusion, a list of acronyms, a list of tables, a list of figures, and a list of references.
Chapter 1: Theoretical framework of non-performing loans and the determinants of non-performing loans
Chapter 2: Situation of non-performing loans at Techcombank
Chapter 3: Measuring and evaluating the determinants of non-performing loans at Techcombank
Chapter 4: Solutions and recommendations to improve the quality of non - performing loans management at Techcombank
THEORETICAL FRAMEWORK OF DETERMINANTS OF
Theoretical framework about non- performing loans
1.1.1 Concept and classification of non - performing loans
1.1.1.1 Concept of non - performing loans
International financial institutions provide specific guidelines for identifying non-performing loans The European Central Bank (ECB) defines non-performing loans as those that are 90 days past due, regardless of whether they are in default or impaired Additionally, loans are considered impaired based on U.S GAAP and International Financial Reporting Standards (IFRS) criteria, as well as those in default according to the Capital Requirements Regulation.
Non-performing loans, as defined by the International Monetary Fund (IMF), are loans where borrowers have failed to make interest or principal payments for at least 90 days This includes situations where interest payments have been capitalized, refinanced, or postponed by mutual agreement for 90 days or more Additionally, loans with delayed payments of less than 90 days may also be classified as non-performing if there is significant uncertainty regarding the debtor's future ability to make payments.
The Basel Committee on Banking Supervision (BCBS) does not specify a timeline for evaluating non-performing loans However, in its guidelines on credit risk management, BCBS defines non-performing loans as overdue debts for which borrowers are unable to repay if banks do not take action to recover the amounts owed.
The State Bank of Vietnam's consolidated document 01/VBHN-NHNN outlines the regulations for asset classification, risk provision methods, and the management of provisions in credit institutions and foreign bank branches It defines non-performing loans as debts categorized as group 3 (substandard debt), group 4 (doubtful debt), and group 5 (potential loss of capital) This classification highlights the critical nature of non-performing loans within the financial system.
Non-performing loans (NPLs) typically arise when a customer is significantly overdue on a primary payment or unable to fulfill their debt obligations to a financial institution This article will utilize the State Bank of Vietnam's definition to explore the factors influencing the occurrence of non-performing loans.
1.1.1.2 Classification of loans and non - performing loans
Loan classification is a critical process used by banks to evaluate their loan portfolios by grouping them based on similar characteristics and risk levels The World Bank categorizes loans into five distinct groups: current loans, special mention loans, sub-standard loans, doubtful loans, and loss loans Current loans are fully secured by cash or equivalents, with overdue periods of less than 90 days and borrowers capable of repayment Special mention loans exhibit potential weaknesses that could hinder repayment, also overdue for less than 90 days Sub-standard loans demonstrate credit weaknesses and are either renegotiated or overdue between 90 and 180 days Doubtful loans are unlikely to be recovered, overdue from 180 to 360 days, while loss loans are deemed unrecoverable, overdue for more than 360 days Non-performing loans fall into the categories of sub-standard, doubtful, and loss loans.
The State Bank of Vietnam's Circular No 02/2013/TT-NHNN outlines the classification of assets, risk provision levels, and methods for managing credit risk provisions in banking It mandates that credit institutions and foreign bank branches categorize debts—excluding off-balance sheet commitments—using a quantitative method divided into five distinct groups.
Group 1 (standard debts) includes current debts that being assessed as fully and timely recoverable, both principals and interests; debts which are overdue for a period of less than 10 days and being assessed as fully recoverable, both overdue
9 principals and interests, and fully and timely recoverable, both remaining principals and interests; Other debts which are classified to the Group 1 in accordance with provisions in clause 2, this Article
Group 2 (debts which need attention) includes: Debts which are overdue for a period of between 10 days and 90 days; Debts which are restructured repayment term for the first time; Other debts which are classified to the Group 2 in accordance with provisions in clause 2 and clause 3 this Article
Group 3 (sub – standard debts) includes: Debts which are overdue for a period of between 91 days and 180 days; Debts which are extended repayment term for the first time; Debts which are exempted or reduced interests because customers are not sufficient capability to pay all interests under credit contracts; Debts which are recovered under inspection conclusions; Other debts which are classified to the Group 3 in accordance with provisions in clause 2 and clause 3 this Article; Debts which are fallen in one of the following cases:
Debts of customers or the guarantee party being organizations, individuals who are not subject to be extended credit by credit institutions, foreign banks’ branches as prescribed by law
Debts secured by the stocks of credit institutions or their subsidiaries involve loans used to inject capital into another credit institution In this arrangement, the lending credit institution receives security assets in the form of stocks from the institution that receives the contributed capital.
Unsecured debts refer to loans or credit extended without collateral, particularly those with preferential terms or exceeding 5% of a credit institution's own capital This applies to foreign bank branches when credit extension to customers is subject to legal restrictions.
Debts provided to subsidiary and associate companies of credit institutions, as well as enterprises under their control, must not exceed the legal limit set by regulations These debts are significant as they hold a value that surpasses the prescribed thresholds.
10 exceeding limits of credit extension, unless being allowed to exceed limit, as prescribed by law
Debts which violated provisions of law on credit extension, foreign exchange management and rates of safety assurance for credit institutions, foreign banks’ branches
Debts which violated internal regulations on credit extension, loan management and policy on risk provisions of credit institutions, foreign banks’ branches
Group 4 (doubtful debts) includes : Debts which are overdue for a period of between 181 days and 360 days; Debts which are restructured repayment term for the first time but still overdue for a period of less than 90 days under that restructured repayment term; Debts which are restructured repayment term for the second time; Debts which are specified in point c (iv) clause 1 this Article and overdue for a period of between 30 days and 60 days after decisions on recovery have been issued; Debts which must be recovered under inspection conclusions but fail to be repaid although recovery term was overdue from 60 days ago; Other debts which are classified to the Group 4 in accordance with provisions in clause 2 and clause 3 this Article
Group 5 (potentially irrecoverable debts) includes: Debts which are overdue for a period of more than 360 days; Debts which are restructured repayment term for the first time but still overdue for a period of 90 days or more than under that first restructured repayment term; Debts which are restructured repayment term for the second time but still overdue under that second restructured repayment term; Debts which are restructured repayment term for the third time or later, whether debts are overdue or not; Debts which are specified in point c (iv) clause 1 this Article and overdue for a period of more than 60 days after decisions on recovery have been issued; Debts which must be recovered under inspection conclusions but fail to be repaid although recovery term was overdue for more than 60 days; Debts of customers being credit institutions which are announced by the State bank to place in special control status, or foreign banks’ branches of which capital and
11 assets are blockaded; Other debts which are classified to the Group 5 in accordance with provisions in clause 3, this Article
Moreover, under quanlitative method, credit institutions and foreign banks’ branches may classify debts, off-balance sheet commitments according to 5 groups as follow:
The determinants of non - performing loans at commercial banks
1.2.1.1 The return on assets ratio
Return on Assets (ROA) is a key indicator of a bank's profitability, showcasing its ability to generate profits relative to total assets This ratio facilitates performance comparisons among banks with similar risk profiles, as it accounts for variations in tax policies and financial leverage (Kupiec & Lee, 2012; Messai & Jouini, 2013).
1.2.1.2 The equity to assets ratio
Equity to total assets ratio indicates the percentage of a bank's assets are owned by investors and not leveraged and therefore this low ratio shows that the
High financial leverage in banks can increase potential risks and lower profits, especially when borrowing costs rise Nguyen Thi Hong Vinh (2015) utilized estimation models such as REM, FEM, and GMM to analyze factors influencing non-performing loans (NPLs) at commercial banks from 2007 to 2014, finding that equity negatively impacts the NPL ratio Conversely, Shrieves and Dahl (1991) conducted an empirical study of nearly 1,800 U.S banks between 1984 and 1986, concluding that the equity-to-total-assets ratio correlates similarly with NPLs This relationship is attributed to government regulations on capital ratios and risk levels, indicating that higher capital typically allows for greater risk exposure.
1.2.1.3 The size of the bank
The size of a bank, as indicated by its total assets, is crucial in determining its equity requirements and credit granting capacity, according to the State Bank of Vietnam regulations Larger commercial banks typically engage with reputable and stable enterprises, leading to more effective asset and liability management compared to smaller banks However, the relationship between bank size and non-performing loans (NPL) ratios can vary; while Salas and Saurina (2002) suggest that larger banks may experience higher NPL ratios due to increased diversification opportunities, Misra and Dhal (2010) found that smaller banks often maintain stricter management practices, resulting in lower NPL ratios This indicates that the impact of bank size on NPL ratios is complex and influenced by asset management strategies.
1.2.1.4 The loan loss reserve ratio
The loan loss reserve ratio is a key metric used by banks to indicate the percentage of reserves set aside to cover potential losses from defaulted loans An increase in lending typically leads to a rise in the loan loss reserve The relationship between the loan loss reserve ratio and the non-performing loans (NPLs) ratio can be either positive or negative, depending on the quality of the credit extended Research by Ahmad and Taqadus Bashir (2013) and Tran Thi Phuong Hoa (2016) suggests that the loan loss reserve ratio can have similar effects on the NPLs ratio.
Non-performing loans are not only affected by the bank's specific factors but also affected by macro - economic factors
The relationship between GDP and non-performing loans (NPLs) is influenced by economic conditions, as highlighted by Louzis et al (2011), who noted that economic crises lead to increased NPL ratios due to financial difficulties faced by companies and households Conversely, during periods of strong economic growth, NPL ratios tend to decline as incomes rise Salas and Saurina (2002) further emphasized the negative correlation between GDP growth and NPLs, demonstrating the swift impact of macroeconomic factors on the lending capabilities of financial institutions Additionally, Khemraj and Pasha (2009) conducted research on Guyana's NPLs from 1994 to 2004, confirming the inverse relationship between GDP growth and NPLs, consistent with earlier findings.
Inflation significantly influences a bank's capacity to pay interest and manage loan repayments through various payment methods Its impact on non-performing loans can vary, potentially moving in the same or opposite direction Research indicates that high inflation may enhance borrowers' ability to repay their debts, highlighting a complex relationship between inflation and loan performance.
Inflation can significantly impact the real value of loans, particularly when lending rates are fixed, as banks are unable to adjust interest rates in response to changing inflation This situation can diminish customers' real income, making it more challenging for them to repay their debts Conversely, with floating lending rates, banks may raise interest rates to preserve real returns, which can further strain borrowers' repayment capabilities and lead to a higher ratio of non-performing loans Consequently, the relationship between inflation and non-performing loans can vary, potentially moving in the same or opposite directions.
This article provides a comprehensive overview of non-performing loans (NPLs), including their definition, classification, and impact on both banks and the broader economy It emphasizes the critical need for banks to identify and mitigate NPLs promptly to avoid severe consequences The author summarizes key factors influencing NPLs based on domestic and international research, presenting various research models utilized in the analysis This foundation aids in selecting the most appropriate model for studying the factors affecting NPLs in banks The following chapter will assess the current state of business operations and NPLs at the Vietnam Technological and Commercial Joint Stock Bank.
SITUATION OF NON - PERFORMING LOANS AT
Performance of Techcombank during 2014-2021 period
2.1.1 Overview of Vietnam Technological and Commercial Joint Stock Bank
Vietnamese full name: Ngân hàng Thương mại cổ phần Kỹ thương Việt Nam
English name: Vietnam Technological and Commercial Joint Stock Bank
Address: Techcombank Tower, 191 Ba Trieu, Hai Ba Trung District, Hanoi
2.1.1.1 History and development process of Techcombank a History of Techcombank
Founded in 1993, Techcombank has established itself as one of Vietnam's largest private banks, holding around 4% of the market share as of 2021 The bank primarily operates in three segments: retail banking (46.6% of gross loan portfolio), SME banking (18.4%), and wholesale banking (35%) With a robust customer base of over 170,000 SMEs and corporate clients, alongside nearly 9.5 million individual customers—representing about 10% of Vietnam’s population—Techcombank boasts an extensive distribution network that includes 2 representative offices and 307 branches across 45 provinces, complemented by a comprehensive e-banking platform.
Techcombank is a leader in technological innovation within Vietnam's banking sector, having introduced the first core banking system in 2001 and launched a mobile banking solution in 2015 The bank further strengthened its infrastructure by investing in cloud technology and cybersecurity in 2020 As of 2021, Techcombank's brand was valued at USD 945 million by Brand Finance Additionally, since its listing on the Ho Chi Minh City Stock Exchange in 2018, Techcombank has achieved a market capitalization of USD 7.9 billion as of February 23, 2022.
22 b The development process of Techcombank
Period 1 (1993-2003): Techcombank was established with charter capital of VND 20 billion in Hanoi Techcombank not only focus on establishing many branchs, sub-branches in Hanoi and Ho Chi Minh City but also focused on enhancing the new banking software by signing contract with a leading software supplier in the market-Temenos Holding NV
Period 2 (2004-2013): Techcombank had launched the new brand identity, became a member of Smart Link and Bank Net At this time HSBC became a strategic shareholder of Techcombank This bank also kept expanding many projects on technological modernization to upgrade the core banking system, connecting ATM system with strategic partner HSBC, opening free call Centre (24/7) The bank at the forefront of bringing a sector-leading digital technology experience through cradles ATM transaction service In addition, Techcombank had moved Head office to Techcombank Tower in Hanoi and launched new head office at grade A building located at the center of Ho Chi Minh City
Period 3 (2014-2017): Techcombank had refreshed 5- strategy for 2016-2020 period to become the No.1 Bank in Vietnam This period witnessed Techcombank bought back HSBC’s shareholding and signed 15-year exclusive partnership agreement with leading insurance firm – Manulife Another outstanding point at that time is that Techcombank had written – off all VAMC loans
Period 4 (2018 – 2021): Techcombank has been listed on Ho Chi Minh City Stock Exchange (HOSE), and became the top 3 biggest IPO deals in Southeast Asia, the first Vietnamese bank to report under IFRS 9 (2018) Moreover, Techcombank officially complied with Basel II and received many awards from VISA organization and other organizations like Global Banking and Finance Review, The Asian Banker, Finance Asia, Euromoney c Organization structure of Techcombank
Figure 2.1: Organization structure of Techcombank
Source: Techcombank analyst presentation 2021 2.1.2 Performance of Techcombank during 2014-2021 period
2.1.2.1 Status of assets and sources of capital of Techcombank during 2014-
Table 2.1: Status of Techcombank’s assets and sources of captial
2014 2015 2016 2017 2018 2019 2020 2021 Total assets 175902 191994 235363 269392 320989 383699 439603 568729 Liabilities 160916 175536 215777 242462 269206 321627 364988 475687 Total equity 14986 16458 19586 26930 51783 62072 74615 93042
The total assets of Techcombank had gone up dramatically during the 2014-
In 2021, Techcombank experienced a remarkable 300% increase in earnings despite the challenges posed by Covid-19 During this period, earning assets constituted 91% of the bank's total assets, while non-earning assets made up only 9% This strategic asset allocation enables Techcombank to maximize capital efficiency effectively.
As of December 31, 2021, liabilities surged to VND 475,687 billion, nearly tripling since 2014, primarily driven by a significant rise in customer deposits Funding sources also included the interbank market at 24%, valuable papers at 7%, and other sources at 3%.
In 2021, Techcombank's expanding deposit franchise and diverse funding sources played a crucial role in its healthy asset growth The bank's shareholders’ equity reached VND 93,042 billion, marking a sixfold increase since 2014 With strong capital and funding support from investors, Techcombank achieved the top position among major Vietnamese banks, boasting a solid capital adequacy ratio of 15.0% as of December 2021, well above the regulatory minimum of 8%.
2.1.2.2 Capital mobilization of Techcombank during 2014-2021 period
Table 2.2: Deposits of Techcombank during
Techcombank has experienced substantial growth in deposits over the past eight years, particularly notable in 2016 and 2020 Despite the challenges posed by Covid-19, the bank's deposit values increased by VND 30,000 to 40,000 billion annually, showcasing a strong average upward trend.
In 2016, Techcombank saw a significant 21.9% increase in deposits as it launched its 5-year strategy (2016-2020), focusing on raising borrowing interest rates and enhancing service quality and digital experiences By 2020, deposits continued to grow by 20%, showcasing the dedication of the bank's staff in mobilizing resources, particularly during the challenging times of the Covid-19 pandemic.
Analyzing the deposits portfolio by ownership
Figure 2.2: Techcombank deposits portfolio by ownership
From 2014 to 2021, customer deposits consistently represented the largest share of total deposits, starting at approximately 64-67% in the initial three years and rising to 70% or more from 2017 onward This increase can be attributed to Techcombank's proactive approach in providing customized banking services, enhanced by advanced mobile technology and attractive interest rate policies, alongside the expansion of branches and transaction offices for greater user convenience.
YearsDeposits from organizations Deposits from individuals
Analyzing by category of Techcombank’s deposit
Figure 2.3: Techcombank deposits portfolio by category
The bar chart illustrates the significant role of term deposits in Techcombank's capital mobilization from 2014 to 2021, highlighting a notable shift in the deposit structure during this period In 2014, term deposits comprised 83% of the total, vastly outnumbering current accounts at 15% However, by 2021, this gap had narrowed to just 3%, primarily due to changing customer payment behaviors The COVID-19 pandemic spurred a surge in e-commerce and online shopping, leading to an increased reliance on non-cash payments among customers This shift not only reduced capital costs but also enhanced profit margins for the bank.
2.1.2.3 Loans to customer of Techcombank during 2014-2021 period
Over the past eight years, Techcombank has experienced significant growth in lending activities, with an impressive average annual growth rate of 22.87% Although there was a slight decline in total outstanding loans in 2018, the bank rebounded with three consecutive years of growth, reaching VND 347,341 billion in 2021, which marks an approximate increase of 333% compared to 2014.
Current accounts Term deposits Marginal deposits
Figure 2.4: Loans to customers of Techcombank
Analyzing loan portfolio by ownership
Figure 2.5: Techcombank’s loan portfolio by ownership
YEARSLoans to organizations Loans to indiduals
Over an 8-year period, loans to individuals showed an upward trend, while loans to organizations generally declined In 2014, there was a significant 24% gap between the two, but by 2021, this difference had narrowed to just 6% Notably, in 2020, individual loans dropped to 40%, whereas loans to organizations rose to 60% This shift can be attributed to the impact of Covid-19, which led individual customers to scale back their businesses, while organizations increased borrowing to cover unexpected expenses arising from macroeconomic factors.
Analyzing loan portfolio by term
Figure 2.6: Techcombank’s loans portfolio by term
The mechanism of controlling non - performing loans at Techcombank
Techcombank utilizes quantitative methods in accordance with Article 10 of Circular 11/2021/TT-NHNN to classify customer loans, identifying non-performing loans both individually and across the entire portfolio The bank evaluates these loans using its internal credit rating system aligned with Basel II guidelines, employing two distinct assessment models for individual and corporate clients.
When a customer has multiple debts with a bank and one is classified as high risk, the bank must categorize all remaining debts from that customer into the high-risk group In the context of syndicated loans, the bank aligns its risk classification with the leading bank's assessment If the bank assigns a lower risk group to a customer than that indicated by the Credit Information Center (CIC), it is obligated to adjust the customer's classification to match the CIC's risk group.
Situation of non - performing loans at Techcombank
2.3.1 Classification of loans by group
Over the past eight years, Group 1 loans have consistently represented the majority of Techcombank's gross loan portfolio, maintaining a range of 96% to 98% In 2021, Group 1 loans surged to VND 342,903 billion, marking a remarkable 348% increase from VND 76,479 billion in 2014 Non-performing loans, categorized as Group 3, Group 4, and Group 5, contribute to the overall loan performance metrics.
From 2014 to 2021, the value of non-performing loans (NPLs) at Techcombank increased from VND 1,913 billion to VND 2,294 billion; however, the NPL ratio significantly decreased from 2.38% to 0.66% During this period, Techcombank effectively reduced and controlled loans classified as group 3 and 4, leading to only a slight increase in their absolute value while their proportion of the gross loan portfolio declined Notably, group 5 loans experienced a substantial reduction, falling from VND 1,055 billion in 2014 to VND 755 billion in 2021.
Table 2.3: Classification of loans by group of Techcombank 2014 – 2021
Source: Techcombank financial statement 2.3.2 Analyzing the basic indicators reflect the situation of non - performing loans
Between 2014 and 2019, the absolute value of non-performing loans (NPLs) increased from VND 1,913 billion to VND 3,078 billion, before dropping significantly to VND 1,295 billion in 2020 However, the Covid-19 pandemic led to a resurgence in NPLs, which rose by nearly VND 1,000 billion in 2021, reaching VND 2,294 billion—a 77% increase compared to 2020.
Table 2.4: NPLs ratio of Techcombank 2014 – 2021
Techcombank has achieved a significant reduction in its non-performing loans (NPLs) ratio, decreasing from 2.38% in 2014 to just 0.66% in 2021 Notably, this ratio has remained below 1% for the past two years, positioning Techcombank as one of the banks with the lowest NPLs ratio in Vietnam In contrast, the overall NPLs ratio for the Vietnamese banking sector was 1.7% in 2021, excluding the Vietnam Asset Management Company (VAMC) Furthermore, Techcombank has successfully eliminated its VAMC loans since 2020, becoming one of the first banks to resolve these issues.
The structure of Techcombank non-performing loans by group
The bar chart illustrates that Group 5 loans experienced significant growth from 2014 to 2019, representing approximately 55-61% of the total loans during 2014-2018 Notably, in 2019, Group 5 loans surged to 83% of Techcombank's non-performing loans.
In 2021, Techcombank demonstrated effective management of non-performing loans by provisioning all outstanding loans for restructuring ahead of schedule, two years prior to the timeline set by the State Bank of Vietnam (SBV), to support customers impacted by Covid-19.
Figure 2.8: Techcombank’s non - performing loans by group 2014 – 2021
Source: Techcombank financial statement 2.3.2.2 Loan loss reserve to gross loan portfolio
Table 2.5: Loan loss reserve to gross loan portfolio ratio
Between 2014 and 2021, Techcombank's loan loss reserve rose significantly from VND 960 billion to VND 3,736 billion However, the loan loss reserve to gross loan portfolio (LLR/GLP) ratio experienced a slight decline, decreasing from 1.20% in 2014 to 1.08% in 2021.
In 2020, Techcombank experienced a record low loan loss reserve ratio of 0.8% of its gross loan portfolio, attributed to a decrease in non-performing loans, allowing the bank to significantly lower its loan loss reserves.
2.3.2.3 Loan loss reserve to non - performing loans
Over the past eight years, Techcombank has significantly increased its loan loss reserve, reflecting its commitment to enhancing credit risk management Prior to the Covid-19 pandemic, from 2014 to 2019, the bank's reserve coverage ratio remained below 100% However, in response to the prolonged impacts of Covid-19 and a rise in customer loans, Techcombank improved its reserve coverage ratio to 171% in 2020 and 163% in 2021 The bank actively identifies and addresses non-performing loans and non-earning assets by diligently collecting and allocating loan loss reserves.
Table 2.6: Loan loss reserve to non - performing loans ratio
Overall assessment of Techcombank performance and its non - performing
Techcombank stands out as one of Vietnam's leading commercial banks, demonstrating remarkable performance both before and during the pandemic Between 2014 and 2021, the bank garnered numerous prestigious international and domestic awards, highlighting its achievements During this period, Techcombank's total assets grew over threefold, while liabilities nearly tripled, and equity surged an impressive 15 times compared to 2014 figures Notably, the bank experienced a 239% increase in capital mobilization, particularly benefiting from a rise in low-cost funding sources, including current accounts.
Techcombank has experienced significant growth, particularly in 2016, when deposits surged due to the implementation of a zero-fee policy for its online banking services This strategic move contributed to a remarkable increase in customer deposits, marking a pivotal moment in the bank's financial performance.
The pandemic in 2020 accelerated online banking activities, leading customers to maintain higher account balances for payment purposes, which resulted in an increase in current and savings accounts In 2021, Techcombank saw a remarkable 433% rise in loans to customers compared to 2014 The bank successfully balanced its short-term and long-term loans while ensuring a more equitable distribution between individual and organizational borrowers Additionally, Techcombank's income in 2021 was five times greater than in 2014, and its earnings before tax and interest were 15 times higher than in 2014, showcasing effective expense management over the past eight years.
Techcombank's loan portfolio is predominantly composed of Group 1 loans, which consistently represent 96% to 98% of the total Over an eight-year period, the bank has significantly reduced its non-performing loans (NPLs) ratio, establishing itself as a leader in NPL management among Vietnamese commercial banks The loan loss reserve to NPL (LLR/NPL) ratio remained robust, peaking at 163% in 2021, indicating strong coverage for potential losses Additionally, the LLR to gross loan portfolio (LLR/GLP) ratio showed a positive trend, declining from 2014 to 2021 as Techcombank effectively addressed legacy NPLs and focused on improving credit quality, contributing to the overall decrease in both NPL and LLR/GLP ratios.
2.4.2 Drawbacks of Techcombank and the reasons of the drawbacks
Between 2014 and 2021, Techcombank demonstrated strong overall performance, yet faced notable challenges Specifically, from 2014 to 2017, the bank's total expenses consistently exceeded its EBIT, highlighting financial inefficiencies during this period.
Techcombank faces a reduction in annual profits due to the requirement of a 20% provision for VAMC bonds resulting from the sale of non-performing loans to VAMC This phenomenon is influenced by multiple factors, with the bank's financial strategy significantly impacting its profitability.
Between 2014 and 2017, significant investments in technology were made, alongside the introduction of customer-attracting policies like a zero-fee structure for online banking services, which impacted the bank's profitability Additionally, the lingering effects of the 2008 financial crisis continued to influence the Vietnamese economy during this period, with Techcombank also feeling the repercussions.
Another noticeable drawback of Techcombank is that the NPLs ratio in 2014
The period from 2014 to 2017 saw significant fluctuations in the non-performing loans (NPLs) ratio in Vietnam, peaking at 2.38% in 2014 due to political instability and lingering effects of the global financial crisis While the NPLs ratio declined from 2014 to 2016, it rose again in 2017 and 2018, primarily due to two factors First, Techcombank's acquisition of non-performing loans from the Vietnam Asset Management Company (VAMC) in 2017 contributed to the increased NPLs ratio Second, Techcombank's strategic focus on both short-term loans for individuals and medium to long-term loans for organizations during the 2016-2020 plan heightened credit risk, further elevating the NPLs ratio.
An analysis of Vietnam Technological and Commercial Joint Stock Bank (Techcombank) from 2014 to 2021 reveals effective asset and capital management practices The bank has successfully mobilized capital and developed a robust loan portfolio, indicating promising future prospects Additionally, Techcombank has demonstrated strong credit risk management, particularly in maintaining a low ratio of non-performing loans.
The Covid-19 pandemic has significantly affected the economy and created volatility in the global market, posing major challenges for Techcombank To address these issues, the author will develop a model to identify the determinants of non-performing loans at the bank, enabling Techcombank to enhance its credit management and implement effective strategies to reduce non-performing loans.
EVALUATING THE DETERMINANTS OF NON -
Research model
This article analyzes the determinants of non-performing loans at the Vietnam Technological and Commercial Joint Stock Bank, focusing on macro-economic and bank-specific factors Utilizing quantitative models derived from previous research and the thesis outlined in Chapter 1, key variables will be examined to understand their impact on loan performance.
Macro - economic determinants: Economic growth (GDP), Inflation rate (CPI)
Bank – specific determinants: Return on assets ratio (ROA), Equity to assets (ETA), Size of the bank (SIZE), Loan loss reserve (LLR)
This study develops a research model for analyzing non-performing loans (NPL) at commercial banks, specifically Techcombank, by referencing previous works, including studies by Jordan Kjosevski (2018), Junkyu Lee et al (2019), Le Nhat Minh (2015), and Chan Thi Phuong Hoa (2016) The model includes NPL as the dependent variable and six independent variables: economic growth rate, inflation rate, return on assets ratio, equity to assets ratio, loan loss reserve ratio, and bank size.
NPL t = 𝜷 0 + 𝜷 1 ROA t + 𝜷 2 ETA t + 𝜷 3 LnSIZE t + 𝜷 4 LLR t + 𝜷 5 GDP t + 𝜷 6 CPI t + 𝒖 it
In which, t= 1,2,3…t with t is the quarters in the research period 2014 – 2021
NPL t : The non-performing loans ratio at Techcombank at t period
ROA t : The return on assets ratio of Techcombank at t period
ETA t : The Equity to Assets ratio of Techcombank at t period
LnSIZE t : The logarit of Techcombank’s size at t period
LLR t : The loans loss reserve for credit risks of Techcombank at t period
GDP t : The economic growth rate of Vietnam at t period
CPI t : The inflation rate of Vietnam at t period
𝒖 : The random errors at t period
Table 3.1: Variables used in research model
Variables Denotation Calculation Expectation Previous studies
Fofack(2005), Junkyu Lee et al
Laxmi et al (2017), Nguyen Thi Nhu Quynh et al (2018)
Earning after tax/ Total assets
Jordan Kjosevskia et al (2019), Fawad Ahmad et al (2013)
Equity to assets ratio 𝐸𝑇𝐴 𝑡 Equity/Total assets -
Le Minh Nhat (2015), Tran Thi Phuong Hoa
Size of bank Ln𝑆𝐼𝑍𝐸 𝑡 Log (total assets) -
Loan loss reserve/Gross loan portfolio
Research data and research methods
Data used for research includes the quarterly GDP growth rate and inflation rate, return on assets, equity-to-assets ratio, loan loss reserve and the size of Techcombank from 2014 – 2021
This study employs Ordinary Least Squares (OLS) methodology and utilizes Stata software version 14 for analyzing the research model The data analysis procedures are systematically outlined to ensure accurate results.
Step 1 : Descriptive analysis of data The author starts analyzing the descriptive data through Stata software The received result includes number of observations (Obs), the average value (Mean), standard deviation (Std.Dev), the maximum value (Max) and the minimum value (Min) of the variables
Step 2: Checking the stationarity of the data by using Augmented Dickey – Fuller test and carrying out the correlations test for all the variables Analyzing the correlation of variables will demonstrate the relationship among the variables in the research model
Step 3: The regression of the model by Ordinary Least Squares regression (OLS) method
Step 4: Hypothesis testing the model The error checking of multicollinearity will be implemented by using the Variance Inflation Factor (VIF), Breusch-Pagan/Cook- Weisberg test is used to find the heteroskedasticity Finally, this study carries out an autocorrelation test for the time series data.
Descriptive statistics
The summary descriptive statistics for the variables in this thesis indicate stable values, with standard deviations lower than the mean The non-performing loans (NPLs) ratio ranged from a minimum of 0.36% to a maximum of 4.60% throughout the research period Notably, one quarter experienced negative GDP growth, with values ranging from -6.02% to 7.65% A comparable trend was observed in the Consumer Price Index (CPI).
The study revealed that the value of ROA fluctuated between 0.09% and 0.97%, with a standard deviation of 0.28% The Equity to Assets ratio varied from 8.32% to 17.59%, exhibiting a standard deviation of 3.87% Additionally, the Loan Loss Reserve (LLR) showed a minor range, with minimum and maximum values around 0.8% and 1.7% The lnSIZE values were observed to range from 12.08 to 13.25, with a standard deviation of 12.53 Overall, these metrics highlight the financial performance and stability within the examined entities.
Table 3.2: The descriptive statistics of selected variables
Variable Obs Mean Std Dev Min Max
Source: Result from Stata software
Unit root test for stationarity
Testing the stationarity of data is crucial for obtaining reliable estimation results in time series analysis When the data is stationary, it allows for the progression to regression model analysis Conversely, if the data is non-stationary, it is essential to examine the differences between variables to ensure they become stationary The results of the stationarity testing are summarized in the table below.
Table 3.3: Result of stationarity testing
Variables ADF Test Statistic Test Critical value at 5% *P - value
The research utilized an Augmented Dickey-Fuller (ADF) test with a critical value of 5 percent, revealing that the absolute ADF t-statistic for all variables exceeded this threshold (-2.983) Consequently, all variables rejected the null hypothesis, indicating the absence of unit roots Additionally, the p-values for all variables were below 5 percent, confirming that none exhibited unit roots and that they formed a stationary series.
Correlation analysis
The analysis reveals that independent variables such as GDPt, ROAt, ETAt, and LLRt positively influence the dependent variable NPLt, while CPIt and LnSIZEt exhibit a negative impact on non-performing loans Additionally, the small correlation coefficients among the independent variables indicate minimal multicollinearity concerns in this study.
Table 3.4: Result of correlations analysis
NPL GDP CPI ROA ETA LLR LnSIZE
Source : Result from Stata software
Analysis of regression model
Table 3.5: The result of regression OLS model
Variable Coefficient Standard Deviation P > |t| NPL
Source: Result from Stata software
**Correlation is significant at the 0.05 level (2-tailed)
***Correlation is significant at the 0.01 level (2-tailed)
With the P-value 0.0000 < 0.05, the model can be presented as below:
NPL t =0.206 - 0.57 GDP t +0.452 ROA t + 2.249 LLR t - 0.017LnSIZE t
Table 3.6: Result of the goodness-of-fit checks
Source: Result from Stata software
The model demonstrates strong reliability with an F value of 51.22 and a Prob>F of 0.0000 Additionally, the R-squared and Adjusted R-squared values are 92.48% and 90.67%, respectively, indicating that the regression model is well-fitted to the data, as both values exceed 50%.
Hypothesis testing research model
This study used Variance Inflation Factor (VIF) method to measure multicollinearity in the set of multiple regression variables According to Hair et al
In 1995, it was established that a Variance Inflation Factor (VIF) exceeding 10 indicates serious multicollinearity in regression analysis However, as indicated in the table above, all variables in this model have a VIF below 10, suggesting the absence of multicollinearity and reinforcing the reliability of the regression results.
Table 3.7: Result of multicollinearity test
Source: result from Stata software
This study used Variance Inflation Factor (VIF) method to measure multicollinearity in the set of multiple regression variables According to Hair et al
In 1995, it was established that a Variance Inflation Factor (VIF) exceeding 10 indicates significant multicollinearity in regression analysis However, the data presented in the table shows that all VIF values for the variables are below 10, suggesting that this model is free from multicollinearity issues.
Table 3.8: Result of heteroskedasticity tests
Breusch-Pagan/Cook-Weisberg test for heteroskedasticity
Variables: fitted values of NPL
Source: result from Stata software
The Breusch-Pagan/Cook-Weisberg test is employed to detect heteroscedastic errors in regression models At a significance level of 5%, the results indicate the presence of heteroskedasticity To address this issue, the study utilized Robust Standard Errors to adjust for the heteroscedasticity within the model.
Table 3.9: Result of autocorrelation test
Lag(p) Chi2 Df Prob>Chi2
Source: Result from Stata software
The analysis reveals that the null hypothesis, which posits the absence of serial correlation, is accepted since the Chi-squared value is 0.3554, exceeding the 5% significance level Consequently, this indicates that there is no serial correlation present among the residuals in the model.
Result of the regression model
The analysis reveals that, after addressing heteroskedasticity, bank size negatively impacts non-performing loans (NPLs) at Techcombank, while return on assets (ROA) and loan loss reserves (LLR) exhibit a positive correlation with NPLs Additionally, this study did not identify a significant relationship between the NPL ratio and the independent variables GDP, CPI, and ETA, as their p-values exceeded the 5% significance threshold.
The relationship between the NPLs ratio and ROA at Techcombank is positively correlated, indicating that an increase in ROA significantly impacts NPLs Specifically, a 1% rise in the return on assets results in a 0.425% increase in the NPLs ratio, aligning with the author's initial expectations and corroborated by findings from Fawas Ahmad and Taqadus Bashir.
(2013), Nguyen Phuoc Thien (2017) This phenomenon is true with the situation of
Techcombank faces increased credit risks due to its strategy of targeting a diverse customer base, including both high-income and mid-income segments While the mid-income group is emerging as a potential market in Vietnam, it also presents financial risks, especially during unstable market conditions As the financial stability of mass affluent customers fluctuates, the likelihood of non-performing loans rises, impacting the bank's overall profitability.
Techcombank has recently increased medium and long-term loans for SMEs, particularly during the Covid pandemic, which can enhance profitability but also heightens risk for the bank The ongoing complexities of the Covid-19 situation in Vietnam and globally, coupled with the political instability between Russia and Ukraine, pose significant challenges for many Vietnamese SMEs Consequently, Techcombank must exercise caution when evaluating the industry and business plans of potential borrowers This careful assessment explains the negative correlation between ROA and NPL ratios at Techcombank.
The loan loss reserve at Techcombank shows a positive correlation with non-performing loans (NPLs), aligning with previous studies by Fawad Ahmad and Taqadus Bashir (2013) and Tran Thi Phuong Hoa (2016) A 1% increase in the loan loss reserve results in a 2.249% rise in the NPL ratio The Covid-19 pandemic has adversely affected customers' incomes, diminishing their ability to repay debts and consequently increasing NPLs In response, Techcombank has had to allocate more funds to loan loss reserves Furthermore, the implementation of Circular No 14/2021/TT-NHNN, which provides guidelines for debt rescheduling and relief for borrowers impacted by the pandemic, may lead to a rise in future NPLs, necessitating further increases in the bank's loan loss reserves.
Techcombank exhibits a negative relationship between its size and non-performing loans (NPLs), indicating that as the bank's size increases, the NPL ratio decreases This aligns with previous studies by Le Minh Nhat (2015), Tran Thi Phuong Hoa (2016), and Laxmi Koju, Ram Koju, and Shouyang Wang (2017) The growth in Techcombank's assets primarily results from an increase in customer loans, reflecting the bank's strong performance and reputation, which attract more clients Consequently, this allows Techcombank to better assess customers' financial capabilities, thereby mitigating credit risk and lowering NPLs Additionally, the bank enhances its loan value and diversifies its loan portfolio by varying terms and ownership characteristics, further balancing its portfolio and reducing the NPL ratio.
Limitation of the study
This study focused on key variables such as return on assets, equity to asset ratio, bank size, loan loss reserves, economic growth rate, and inflation rate However, it is important to note that other significant factors influencing non-performing loans, including technology, credit growth rate, capital adequacy, unemployment rate, and interest rates, were not examined Additionally, the analysis was limited to data from 2014 to 2021, as Techcombank did not fully disclose its quarterly financial statements prior to this period.
This study used the OLS model to analyze the determinants of non - performing loans at Vietnam Technological and Commercial Joint Stock Bank from
Between 2014 and 2021, research revealed that macroeconomic factors did not affect the non-performing loans (NPLs) ratio at Techcombank, while bank-specific determinants had both positive and negative effects Specifically, the return on assets (ROA) and loan loss reserves (LLR) showed a positive correlation with the NPLs ratio, whereas the size of the bank had a negative relationship with non-performing loans In the following chapter, I will present solutions and recommendations aimed at assisting Techcombank in managing its non-performing loans in the upcoming years.
SOLUTIONS AND RECOMMENDATIONS TO IMPROVE
Solutions to limit non - performing loans at Techcombank
To mitigate credit risk, Techcombank should prioritize the diversification of its loan portfolio Research indicates that non-performing loans tend to decrease as bank size increases, highlighting the need for careful lending decisions By balancing its loan portfolio through diversification across customer segments, business sectors, lending maturities, and currencies, Techcombank can proactively manage risks and enhance its financial stability.
Techcombank must enhance its equity capital to strengthen its financial stability The tier 1 capital serves as a crucial buffer during temporary liquidity shortages and mitigates credit risks Both Techcombank and other commercial joint stock banks in Vietnam face increasing pressure to boost capital levels to ensure operational safety Currently, the implementation of Basel III standards in Vietnam is in its early stages, with capital adequacy being a key criterion that needs to be addressed.
52 ratio (car) Therefore, to ensure a minimum capital adequacy ratio, the bank must simultaneously increase the owner's ownership and increase the capital mobilization rate
Establishing an appropriate loan loss provision is essential for Techcombank, as it must align with both internal regulations and legal requirements from the government and the State Bank of Vietnam The study indicates a direct correlation between the ratio of non-performing loans and the loan loss reserves ratio; as the latter increases, so do the non-performing loans This trend necessitates a higher allocation of the bank's budget for provisions, impacting overall financial management.
To enhance its credit rating information system and reduce non-performing loans, Techcombank should prioritize the meticulous monitoring of credit risk management Regular reviews and stringent control of the loan portfolio are essential to prevent potential credit losses Additionally, maintaining accurate and up-to-date customer information, alongside the integration of human resources and technology in credit assessment, will significantly improve risk management and lower the incidence of non-performing loans.
Techcombank must enhance the skills and professionalism of its credit officers, who play a crucial role in assessing and supervising customer situations To achieve this, the bank should implement regular training programs and elevate management standards within the credit services team Continuous evaluations to identify weaknesses and develop targeted solutions will help reduce errors among credit staff Additionally, fostering strong ethical standards among credit officers is vital; establishing clear regulations regarding their responsibilities with loans is essential Regular internal audits will enable the bank to promptly identify and address any violations, ensuring a robust and effective credit management system.
Recommendation with the Government and the State Bank of Vietnam
To help Vietnam Technological and Commercial Joint Stock Bank addresses non - performing loans more effectively, the author makes several recommendations to the Government and the State Bank as follows
4.3.1.1 Maintaining the defined economic growth rate
Stable economic growth fosters a favorable environment for enterprises to expand their manufacturing and business activities, enhancing their ability to repay bank loans and reducing the non-performing loans (NPLs) ratio To mitigate NPLs, the government should implement targeted investment policies that prioritize industrial parks, construction, and services Additionally, providing capital support and creating favorable policy conditions for businesses, especially during the ongoing challenges posed by the COVID-19 pandemic, are crucial steps the government can take.
To stabilize society, the government must enhance social security initiatives to improve residents' lives while actively implementing measures to address natural disasters and epidemics Additionally, creating a conducive legal framework and procedural conditions for startups is essential, alongside opening the economy to attract increased foreign investment.
4.3.1.2 Keeping the inflation rate under 3%
To stabilize inflation and promote economic development, the government and the State Bank of Vietnam must implement flexible monetary policies Maintaining inflation at a reasonable level is crucial for reducing non-performing loan ratios Additionally, strict market surveillance is essential, along with tightening control over the money supply The government should focus on reducing national budget expenditures while increasing revenues to alleviate the pressure of money printing.
4.3.1.3 Improving the optimal legal framework for the resolution of non- performing loans
The government must enhance the legal framework for addressing non-performing loans in banks by collaborating with the State Bank of Vietnam to develop a robust trading market that attracts investors, particularly foreign financial companies This market should be supported by adequate infrastructure and transparent information dissemination Additionally, allowing foreign investors to increase their stakes in commercial banks, along with utilizing state-owned funds to bolster the capital of these banks, is essential for effectively resolving non-performing loans.
4.3.2 Recommendation to the State Bank of Vietnam
4.3.2.1 Provide flexible and appropriate economic policies
The State Bank of Vietnam must effectively forecast potential economic risks and implement appropriate policies to foster bank development while reducing non-performing loans Additionally, it is crucial to manage credit growth, as rapid increases in lending by commercial banks can heighten credit risks, particularly through a rising ratio of non-performing loans.
The State Bank of Vietnam must oversee commercial banks in adhering to regulations regarding loan classification and credit risk allowances, ensuring both profitability and safety Regular monitoring is essential to swiftly identify non-performing loans within these banks, enabling the proposal of effective solutions to address and resolve these issues promptly.
4.3.2.3 Improving the quality of VAMC debt trading market
VAMC has achieved many significant achievements in resolving non- performing loans, in recent years when implementing resolution 42/2017/QH14;
The implementation of resolution 42/2017/QH14 has faced significant challenges due to inconsistencies among localities in assessing collaterals and navigating real estate business laws To enhance the effectiveness of the Vietnam Asset Management Company (VAMC), the State Bank of Vietnam must clarify the roles of local authorities in aiding banks, particularly in evaluating non-performing loans Additionally, it is crucial for the State Bank to develop policies that bolster VAMC's financial, human, and technological resources This support will enable VAMC to better address non-performing loans Furthermore, easing the restrictions on debt purchases by VAMC and providing legal support will facilitate smoother operations, especially in collaboration with banks.
The competitive landscape of Vietnam's banking sector necessitates effective resolution of non-performing loans for sustainable growth This study evaluates credit risk management practices at Techcombank and across the sector, proposing strategies to improve the resolution quality of non-performing loans while maximizing recoverable value and minimizing operational costs Achieving these goals requires alignment among the policies and regulations set forth by the State Bank, the Government, and the banks themselves.
Banking involves significant risks, and the author aims to implement solutions that will lower the non-performing loans ratio and enhance credit risk management quality at Vietnam Technological and Commercial Joint Stock Bank.
Analyzing the determinants of non-performing loans is essential for banks to mitigate credit risks and lower operational costs, ultimately improving overall performance This study focuses on four key aspects related to management practices at Techcombank.
This study used both qualitative and quantitative methods with the data from
From 2014 to 2021, a study was conducted to assess the non-performing loans (NPLs) at Techcombank, examining six key variables: NPLs ratio, bank size, equity to assets ratio, return on assets (ROA) ratio, loan loss reserve (LLR) ratio, economic growth rate, and inflation rate The findings revealed a positive correlation between ROA and LLR ratios with the NPLs ratio, while larger bank size negatively affected the NPLs ratio However, no significant relationship was found between macroeconomic factors and the NPLs ratio Notably, Techcombank maintained one of the lowest NPLs ratios and demonstrated strong performance during this period Despite this, the adverse effects of the pandemic and global political instability pose potential risks to the bank's performance, highlighting the importance of analyzing NPLs to develop effective future strategies.
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2 Do Quynh Anh and Nguyen Duc Hung (2013), “Empirical analysis the determinants of non - performing loans at Vietnamese commercial banks”,
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3 Nguyen Thi Nhu Quynh et al (2018), “The determinants of non - performing loans at Vietnamese commercial banks”, Ho Chi Minh City Open University Journal of Science, Vol 13, No 3, pp 261-274
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5 Le Minh Nhat (2015), “ Empirical analysis the determinants of non - performing loans at Vietnamese commercial banks”, Master Thesis, University of Economics Ho Chi Minh City
6 Tran Thi Phuong Hoa (2016), “The determinants of non - performing loans at Vietnamese commercial banks”, Master thesis, University of Economics Ho Chi Minh City
7 State Bank of Vietnam, “Intergated Document No 01/VBHN-NHNN of the State
The Bank of Vietnam has consolidated its Circulars regarding the classification of assets, the ratios and methods for establishing provisions for credit losses, as well as the utilization of these provisions within the banking operations of credit institutions and branches of foreign banks, as of March 31, 2014.
8 State Bank of Vietnam, “Circular No 02/2013/TT-NHNN on providing on classification of assets, levels and method of setting up of risk provisions, and use of provisions against credit risks in the banking activity of credit institutions, foreign banks’ branches” , issued date January 21, 2013
9 The National Assembly (2014), Resolution No42/2017/QH14 on pilot settlement of bad debts of credit institutions, issued date June 21, 2017
10 State Bank of Vietnam (2021), Circular No14/2021/TT-NHNN on amendments to Circular no 01/2020/TT-NHNN providing instructions for credit institutions and foreign branch banks (FBB) on debt rescheduling, exemption or reduction of interest and fees, retention of debt category to assist borrowers affected by Covid-19 pandemic, issued date September 07, 2021
11 The Government (2021), Resolution No.01/NQ-CP on main tasks and solutions for implementation of socio-economic development plan and state budget estimate of 2021, issued date January 01,2021