Managing non performing loans at the joint stock commercial bank for foreign trade of vietnam vcb factors and recommendations

Lunghwa University of Science and Technology Department Of Business Administration Thesis for a Master’s Degree Managing Non-Performing loans at The Joint Stock Commercial Bank for Foreign Trade of Vietnam (VCB) – Factors and Recommendations Researcher: Nguyen Nam Chinh Supervisor 1: Dr Nguyen Phu Hung Supervisor 2: Associate Professor Hsin-Fu Chen November, 2018 Lunghwa University of Science and Technology Approval Certificate of Master's Degree Examination Board This is to certify that the Master’s Degree Examination Board has approved the thesis “Managing Non-Performing Loans at The Joint Stock Commercial Bank for Foreign Trade of Vietnam (VCB) – Factors and Recommendations”published by Mr Nguyen Nam Chinh in the Master Program of Graduate School of Department of Business Administration Master’s Degree Examination Board Board Members: Prof PhD Tsan Eric Dr Dao Tung Dr Nguyen Thi Hong Hanh Advisor: Dr Nguyen Phu Hung Assoc Prof PhD Hsin-Fu Chen Chair: Prof, PhD Tsan Eric Date： 2018／11 ／24 ABSTRACT Thesis Title: Managing Non-Performing Loans at The Joint Stock Commercial Bank for Foreign Trade of Vietnam (VCB) – Factors and Recommendations Page:68 University: Lunghwa University of Science and Technology Graduate School: Department of Business Administration Date: November, 2018 Degree: Master Researcher: Nguyen Nam Chinh Advisor: Dr Nguyen Phu Hung Keywords: Bad debt, Non-PeformingLoans (NPLs), Vietcombank, the factors, The Objective is the study of bad debt and the factors affecting the management of bad debts in the period 2005 – 2016 at The Joint Stock Commercial Bank for Foreign Trade of Vietnam (Vietcombank) After studying overview of bad debt, bad debt management, research topics focused on the relationships and the impact of macro factors and micro ones to bad loans at Vietcombank, which analyzed, evaluated the effectiveness of bad debts in the system during the past Vietcombank.Although the surface achieved, the difficulties and problems encountered still in the management of bad debts in Vietcombank This thesis focuses on factors affecting credit growth in the period 2005 to 2016 to propose and propose solutions to limit bad debt at Vietcombank The survey of data on the financial statement for the period 2005-2016 and the results of the regression model test showed that NPLs at Vietcombank are influenced by factors such as size of bank, credit growth, efficiency Banking and Debt / Total Assets ratio The results of the regression model of the factors affecting bad debt of Vietcombank in the period from 2005 to 2016 show that the factors affecting bad debt in Vietcombank are: Bank size, Credit growth, Profitability Equity and Total Debt / Total iii Assets Ratio The results show that NPLs at Vietcombank are heavily influenced by factors related to credit (growth rate, credit quality) By examining data on Vietcombank's 2005-2016 financial statements and the results of the regression testing, the author has proposed some solutions and recommendations to limit bad debt ratios at Vietcombank The author wishes solutions and recommendations can help Vietcombank have policies to direct and business activities suitable for sustainable development in the future iv ACKNOWLEDGEMENT I receive the support from many people in completing this thesis Their invaluable assistance and encouragement are particularly acknowledged throughout the preparation of this thesis First and foremost, I would like to express my sincere thanks to all the helpful Teachers in the International School – Viet Nam National University, and all my colleagues in Vietcombank who have contributed their comments and created favorable conditions for the completion of this dissertation; kindly helped teach, inspire and guide me during the past years, now and in the time coming But most of all, I have been the grateful beneficiary of the help of Dr Nguyen Phu Hung, who patiently gave me her rare time and attention as well as her useful guidance and comments over the long gestation of this thesis I am also grateful to Debt Handling Department of Vietcombank for kindly sharing their own data and their experience I especially appreciate the help of my friends for their unwavering supports And finally, I am truly indebted in a more literal sense to all my family for their constant supporting and encouragement during the long journey of graduate school, especially to help me fulfill this thesis successfully I am likewise responsible for the information, facts and figures in my thesis And I also commit that the thesis is completely the genuine result of my efforts in study and research, accepts in some cases other's works are used with proper acknowledgement Thank you! Author Nguyen Nam Chinh v TABLES OF CONTENTS ABSTRACT iii ACKNOWLEDGEMENT v TABLES OF CONTENTS vi LIST OF TABLES x LIST OF FIGURES xi INTRODUCTION 1.1 The rationale of study 1.2 Objectives of the study 1.3 Scope of the study 1.4 Research methodology 1.5 Structure of the study LITERATURE REVIEW ON RISK MANAGEMENT AND BAD DEBT IN BANK 2.1 Credit risks of commercial banks 2.1.1 Risk categories and risk levels at banks 2.1.2 Credit risk and non-performing debts 2.1.3 Causes and Impacts of credit risks to commercial banks, economy, and customers 2.1.4 Factors driving credit exposure from within the banks 11 2.1.5 Factors driving credit from customers of the banks 12 2.1.6 Other external reasons 12 2.2 Credit Risk Management in commercial banks 13 2.2.1 Risk management process 13 vi 2.2.2 Classification of bank credit to have proper controls over each category 14 2.2.3 Tools 15 2.2.3.1 Questionnaires 15 2.2.3.2 Data-Input Tables 15 2.2.3.3 Credit scoring system 16 2.2.3.3.1 Moody's and Standard & Poor's rating models 16 2.2.3.3.2 Model scores Z 17 2.2.3.4 Capital cushion 17 2.2.3.5 Risk quantification 18 2.2.4 Measuring Credit Risk 18 2.2.4.1 The concept of bad debt 18 2.2.4.2 Sources driving bad debt 23 2.2.4.3 Measure of bad debt 25 2.2.4.4 Implementation Challenges 25 2.2.5 Measures to Control risk 26 2.2.5.1 Collateralized versus uncollateralized netting sets 26 2.2.5.2 Valuation 27 2.2.5.3 Types of collateral 27 2.2.5.4 Coverage of collateralization 28 2.2.5.5 Disputes and reconciliations 28 2.3 Bad debt management practices 29 2.4 Analytical Framework 31 2.5 Conclusion 32 OVERVIEW OF CREDIT AND BAD DEBT AT VIETCOMBANK33 3.1 General introduction on Vietcombank and business activities over the last time 33 3.1.1 History 33 3.1.2 Business performance 34 vii 3.2 Overview of Bad debts situation and Bad Debts handling at Vietcombank 36 3.2.1 Credit situation at Vietcombank 36 3.2.2 Bad debt situation at Vietcombank 38 3.2.3 Measures taken by Vietcombank to handle bad debts 40 3.2.3.1 Group management measures 40 3.2.3.2 Group of debt handling measures 40 3.2.4 Results of handling bad debt in Vietcombank over time 44 3.2.4.1 Results of bad debt handling 44 3.2.4.2 Classification of results of dealing with bad debts according to the implementation method 45 3.2.4.3 Classification of treatment results by region 47 RESEARCH MODEL, RESULTS AND FINDINGS 49 4.1 Research Process 49 4.2 Research models and research hypotheses 49 4.3 Source of data 51 4.3.1 Secondary research data 51 4.3.2 Processing of research data 51 4.4 Regression Model 51 4.5 Description of data 52 4.6 Statistics Results 52 4.6.1 Regression results 52 4.6.2 The correlation between variables 53 4.7 Findings and discussions 54 4.8 Conclusion 57 RECOMMENDATIONS AND SOLUTIONS 59 5.1 Summary of research results 59 5.2 Solutions to reduce NPL in Vietcombank 59 viii 5.2.1 Credit growth is associated with credit quality 60 5.2.2 Improve the quality of credit appraisal and analysis 60 5.2.3 Manage and monitor the disbursement and post-lending process 61 5.2.4 Develop a system to report bad debt transferability 63 5.3 Limit research models and future research directions 64 5.4 Conclusions 65 REFERENCES 66 ix LIST OF TABLES Table 2-1: The causes and effects of credit risk Table 3-1: Financial status of Vietcombank for the period 2011 - 2015 35 Table 3-2: Credit balance of Vietcombank in the period 2012 – 2015 36 Table 3-3: Bad debt situation and risk provision credit in the whole system of Vietcombank for the period 2012 – 2015 38 Table 3-4: Debt classification results under The Circulars No 02/2013/TT-NHNN at 2015.12.31 39 Table 3-5: Bad debt ratio and bad debt recovery period 2011-2015 45 Table 3-6: Recovery of NPLs by measures taken in the period 2011 - 2015 45 Table 4-1: Variables of the model 50 Table 4-2:Statistics table describing the observed variables 52 Table 4-3: The multivariate regression relationship of each variable with lnNPL 52 Table 4-4: The correlation between variables 53 Table 4-5: The regression relationship of each variable with lnNPL 53 Table 4-6: Data 55 x Intercept lnNPL(t-1) SIZE_t lnLOANS_t INEF_t ROE_t L_A_t lnCPI_t 12.42 -0.17 -0.93 0.27 3.52 -2.93 -0.79 -0.08 5.41 0.38 0.36 0.71 2.50 1.91 0.37 0.10 2.29 -0.46 -2.59 0.38 1.41 -1.53 -2.11 -0.83 0.11 0.67 0.08 0.73 0.25 0.22 0.12 0.47 -4.81 -1.37 -2.07 -1.98 -4.44 -9.00 -1.97 -0.38 29.65 1.03 0.21 2.52 11.47 3.15 0.40 0.23 -4.81 -1.37 -2.07 -1.98 -4.44 -9.00 -1.97 -0.38 Upper 95.0% Lower 95.0% Upper 95% Lower 95% t Stat Standard Error 0.919753866 P-value 10 Coefficient s Total 29.65 1.03 0.21 2.52 11.47 3.15 0.40 0.23 4.6.2 The correlation between variables The correlation relationship between variables is provided in the table below: Table 4-4: The correlation between variables lnNPL_t lnNPL(t-1) SIZE_t LOANS_t INEF_t ROE_t L_A_t lnCPI_t lnNPL_t 1.00 0.47 -0.86 0.08 -0.54 0.08 0.59 0.59 lnNPL(t-1) 1.00 -0.54 0.09 -0.44 0.58 0.19 0.27 SIZE_t LOANS_t INEF_t 1.00 -0.08 0.27 -0.26 0.10 1.00 -0.52 -0.15 -0.29 1.00 0.07 0.64 -0.14 -0.80 -0.63 ROE_t 1.00 -0.39 0.15 lnL_A_t lnCPI_t 1.00 0.43 1.00 The table below provides the regression relationship of each variable to lnNPL The coefficientsshow that Size, Inefficiency, loans/asset, and CPI have positive relationship to NPL The volume of NPL of previous year and loan have negative relationship to overall annual NPL levels Table 4-5: The regression relationship of each variable with lnNPL Regression lnNPL_t lnNPL(t-1) 0.551 * lnNPL_t + (-1.685) -0.264 * lnNPL_t + (1.475) 53 SIZE_t LOANS_t INEF_t ROE_t L_A_t lnCPI_t 4.7 0.306 * lnNPL_t + (-3.725) -2.763 * lnNPL_t + (-2.668) 0.428 * lnNPL_t + (-3.729) 0.266 * lnNPL_t + (-3.557) 0.185 * lnNPL_t + (-3.173) 7.9 * lnNPL_t + (-4.182) Findings and discussions  R2 = 90%, meaning that this model provide a good fit  The NPL is decreasing (coefficient is -0.17) That means the NPL is going down, or VCB is working well to contain NPL  As the Size of the Bank (as measured by the Total Asset) increases, the NPL decreases That may mean VCB has higher power to control loans negotiation to get more favorable conditions, and the VCB may have invested in management mechanism and human resources to increase the credit risk management performance  Loans_t coefficient is 0.27 That can be interpreted that higher magnitude of loans, higher NPL ratio That means the efficiency is reducing when you expand the scale of operations Maybe the credit staffs get overloaded, thus workforce need to be enlarged  The ROE goes in opposite direction to that of NPL The NPL becoming lower makes the ROE become higher  The Loan/Asset has negative relationship (-0.79) to NPL.That means recent larger size of loan does not necessarily lead to higher profit or profit margin 54 Table 4-6: Data lnNPL_t lnNPL(t-1) SIZE_t LOANS_t INEF_t ROE_t L_A_t lnCPI_t SLOPE 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 - 3.373 - 3.595 -3.392 - 3.039 -3.667 -3.532 -3.870 -3.707 - 3.579 -3.747 -3.971 -4.193 - 3.37 -3.59 -3.39 -3.04 -3.67 - 3.53 -3.87 - 3.71 - 3.58 - 3.75 -3.97 0.551 18.93 19.10 16.91 19.36 19.54 19.40 19.84 19.97 20.17 20.33 20.49 - 0.264 0.110 0.409 0.138 0.261 0.249 0.193 0.156 0.136 0.181 0.197 0.20 0.306 0.23 0.28 0.30 0.38 0.40 0.38 0.40 0.40 0.40 0.39 0.40 - 2.763 0.26 0.18 0.11 0.23 0.20 0.15 0.11 0.10 0.11 0.12 0.14 0.428 - 0.90 -0.73 1.59 -0.62 -0.59 -0.27 - 0.56 - 0.56 -0.60 - 0.58 - 0.55 0.266 - 2.72 -2.07 -1.47 -2.68 -2.14 - 1.68 -2.38 - 2.72 -3.20 -5.07 -3.00 0.185 55 Check with model results: First, there is a positive relationship between the size of Vietcombank’s total assets and the bad debt ratio at this bank Specifically, if the total assets of Vietcombank increased, the ratio of bad debt increased This can be explained by the following: As the size of the bank expands, the increase in total assets, which means expanding the network as well as increasing the outstanding loans for credit growth while the resources, management, review and warning of short-term risks of the bank are often unchanged, which results in the quality of the bank's credit is affected, resulting in increased NPLs Second, there is a negative relationship between credit growth and NPL ratio at Vietcombank Particularly, when the credit growth rate of Vietcombank increased, the bad debt ratio decreased and vice versa Therefore, accelerating credit growth for Vietcombank may be a short-term solution to reduce the bad debt ratio The bad debt ratio is calculated by the formula = (Bad debt) / Total outstanding loans Therefore, in the short term, when the new loans have not yet arisen, total outstanding loans will reduce the NPL ratio according to the formula above Third, there is a negative relationship between business performance and bad debt ratio Specifically, when profitability on equity (ROE) increased, bad debt increased Thus, if Vietcombank has good business results, high efficiency, bad debt ratio will be improved Fourth, there is a negative relationship between the ratio of total outstanding loans / total assets and bad debt ratio If this ratio increased, the bad debt ratio decreasedand vice versa This is similar to the relationship between credit growth and non-performing loans As Vietcombank increased credit (thereby increasing total assets), NPL ratio in the short term will tend to decrease Fifth, the credit growth rate and the ratio of total outstanding loans / total assets have the opposite relationship with the ratio of non-performing loans That means, if Vietcombank increases credit growth, NPL ratios will improve However, this cannot be regarded as a solution for Vietcombank to limit credit, as bad debt ratio will be effective in the short term, when new loans have not incurred bad debts Without paying attention to the quality of credit, in the long term, the increase in credit and the ratio of credit to 56 total assets are more likely to be bad debt Therefore, Vietcombank should take measures to improve the quality of credit, limit the risks that lead to bad debt increases Sixthly, profitability is negatively correlated with bad debt ratio As a result, bank performance, low management cost, and low non-performing loans, operating profit will be improved This is the reason for the decrease in bank profitability As a result, Vietcombank needs to revise its operating expenses to reduce unnecessary expenses, especially the credit risk profile, to ensure business efficiency as well as to reduce the bad debt ratio in the bank Based on the analysis of bad debt situation, Vietcombank’s bad debt treatment results in Chapter as well as the analysis and exchange of models of factors affecting bad debt of Vietcombank, can draw an integer leading to the shortcomings in the management and handling of bad debts in Vietcombank as follows: - Firstly, the quality of credit appraisal and analysis has been paid attention, however, it has not yet been effective in reviewing the risks before borrowing - Secondly, the management and monitoring of disbursement process and use of loans after loans haven’t been paid much attention There are no detailed procedures and sanctions for violations - Thirdly, Vietcombank does not have a problem debt warning system, bad debt transfer ability At Vietcombank, the list and report of problem debt customers is made and sent to Head Office for consolidation at the beginning of the month However, the evolution of bad debt of customers not only occurs at the time of reporting, but the transfer of bad debt is a process, since business customers encounter difficulties / customers have signs of fraud, resulting in no source of repayment, bad loans are transferred and banks are completely passive, there are no plans and measures before the loan to transfer bad debt, and also to set up risk provisions, affecting business results 4.8 Conclusion Based on the results of regression models of factors affecting bad debt and bad debt situation in Vietcombank, the thesis has pointed out some causes leading to the 57 existence and limitation of management and debt settlement Like bad debt ratio of Vietcombank In particular, the main reason focuses mainly on the effectiveness of the assessment, analysis of customers before, during and after the loan Based on that, the thesis will propose solutions to limit bad debt in Vietcombank in Chapter 58 RECOMMENDATIONS AND SOLUTIONS 5.1 Summary of research results The bad news is that commercial banks in Vietnam have recently become increasingly prone to blood clots in the economy as well as the government’s target, the State Bank of Vietnam Commercial banks in general and Vietcombank in particular, this thesis focuses on factors affecting credit growth in the period 2005 to 2016 to propose and propose solutions to limit bad debt at Vietcombank The survey of data on the financial statement for the period 2005-2016 and the results of the regression model test showed that NPLs at Vietcombank are influenced by factors such as size of bank, credit growth, efficiency Banking and Debt / Total Assets ratio The results also show that the macroeconomic variables proposed are Inflation (measured by CPI) and Economic growth (measured by GDP) which is not statistically significant in the model This conclusion is different from previous studies, which may be due to the fact that macroeconomic variables have a common impact on the commercial banks system, in the context of a particular bank, the impact factor Major bad debt comes mainly from internal factors inside the bank The results of the regression model test are the basis for the authors to propose and propose solutions to improve the effectiveness of NPL management as well as limit NPLs at Vietcombank in the coming time 5.2 Solutions to reduce NPL in Vietcombank The results of the regression model of the factors affecting bad debt of Vietcombank in the period from 2005 to 2016 show that the factors affecting bad debt in Vietcombank are: Bank size, Credit growth, Profitability Equity and Total Debt / Total Assets Ratio The results show that NPLs at Vietcombank are heavily influenced by factors related to credit (growth rate, credit quality) In this chapter, the author will propose solutions to limit and prevent bad debts for Vietcombank: 59 5.2.1 Credit growth is associated with credit quality The results show that bank growth, credit growth and total asset growth may reduce NPL ratios at Vietcombank However, as discussed in the discussion, if the focus is on total asset and credit growth without paying attention to credit quality and risk control, bad debt ratios will change in the short run but there is much risk of bad debt in the long term Therefore, the Board of Directors of Vietcombank needs to determine the strategy and business direction to ensure credit growth must be linked to the quality of credit, through which communication and regulation to the entire Branch, rolling the staff of the system to implement The combination of quality and quantity will help Vietcombank develop sustainably in the future 5.2.2 Improve the quality of credit appraisal and analysis One of the most important and effective solutions to dealing with bad debt is to control the risk of bad debt from the beginning To this, the quality of credit appraisal, loan use inspection is Solutions Vietcombank needs attention to complete Credit risk begins with imprecise and inaccurate credit analysis and judgment that leads to inaccurate lending decisions This is an extremely important step and will ensure credit risk mitigation Highest efficiency, lowest loss The evaluation process should meet the requirements of quality analysis and decision-making time, ensuring proper precaution on the basis of profit and risk analysis as well as meet the requirements of quality customer service Addressing these requirements requires Vietcombank to: - Perform accurate analysis and evaluation of customers' overall risk by defining credit limits every months or year This will give the bank an overall view of the financial situation, the quality of the business, and the assessment of the development prospects of the business to identify the risks of the business, setting a credit limit It is within the limits of customers' liability for the Vietcombank system (excluding the credit limits of other credit institutions because they cannot control the loans of other credit institutions) However, each customer is not only borrowing from a bank, but can also borrow at different banks and the breakdown of any bank loan will cause a risk and affect the ability to repay of customer Therefore, in addition to setting credit limits, 60 other credit conditions, especially the condition of total outstanding loans and customers' financial structure, should be set in order to ensure the safety of the business In order to implement this requirement, it is necessary to focus on quantitative analysis, quantify the risk level of customers through assessment of data and concurrently with qualitative analysis (macro- the internal environment of the business, the history of credit relations with the bank, etc.) to identify the potential risks and the ability of the bank to control and mitigate those risks In the quantitative analysis, the application and completion of the credit scoring system and customer credit rating (in the first phase should focus only on the business) This system should be regularly adjusted to suit the realities and economic conditions of Vietnam and should not be rigid according to the calculations of countries with similar conditions Through the use of quantitative models, the level of risk will be properly quantified, reflecting more clearly the degree of risk of expected borrowings and developing preventive and restrictive measures Risk before granting credit to customers Efforts to set reasonable credit limits will help the bank stay on the defensive and effectively control credit risk - It is necessary to closely coordinate the credit conditions in credit contracts such as interest rates, equity ratios of projects / projects, security assets to ensure the benefits are adequate with the risk level Based on the basic interest rate issued by the State Bank of Vietnam and the cost of its capital, Vietcombank should only set interest rates for reference and assign branches to actively determine appropriate interest rates for each At the same time, it is necessary to set the interest rate in accordance with the step of using the borrowed money of the enterprise (the outstanding portion of the loan exceeds the reference credit limit but remains within the approved credit limit) higher loans) The lower the credit rating, the higher the level of equity participation, the more secure liquidity, and the more stringent legal conditions in credit contracts It also ensures the benefits of Vietcombank when the risk occurs, while enhancing the responsibility of customers in using loans, limit risks 5.2.3 Manage and monitor the disbursement and post-lending process Make disbursement in accordance with credit approval decisions of the approval level, compare the purpose of the loan, disbursement requirements and the structure of 61 costs in the customer's capital requirements, ensure the use of loan funds.Full proof and valid documents Limit cash disbursements except for specific cases due to business activities of customers such as loans for purchasing agricultural and forestry products of households, pay workers, apply only transfer method To be able to control the use of loans by customers Credit risks arise after lending not only because of the poor business plan itself, borrowers use funds for the wrong purposes but also because Banks not control the cash flow after the end of the business plan, resulting in the customer using this money source for less effective or non-transparent purposes In order to prevent these risks, strict control should be exercised after lending To check the use of loans in accordance with the characteristics of the loans, the quality of customers With each loan, each borrower has a distinct difference, but it is important to develop and select a plan that checks the use of capital adequately to ensure the safety of the bank but also facilitates the business the customer and the relationship between the parties Customer credit ratings should be used as the basis for monthly, quarterly or half yearly loan use checks, in which customers with a high credit rating are reputable The longer the credit term is used, the lower the credit rating is, the greater the densification For customers with bad debts, it is necessary to check and classify debts one time a month to closely monitor the situation of customers, to identify, analyze and correct solutions to limit risks In the examination of capital use, it is necessary to strictly carry out the actual inspection, assess the use of capital, the security assets of customers, timely detect the risks and take measures to handle, avoid carrying out a countervailing test, done on paper There should be an analysis and timely assessment of the signs of risk such as customers having difficulty repayment, changes in business environment, market situation adversely affect business plan, there are signs of breach of law , based on the system of early warning signs of credit risk (this is being done in Vietcombank issued documents on each type of loans in recent times) to Capture the ability to handle active, timely risks are occurring Closely monitor the sources of money of customers based on the development of mechanisms for checking each type of loan (export loans, check the date of export, claims, export documents Basic construction loans need to check the progress of the 62 works, confirm the investor's debt and commit to transfer the entire source of payment to the customer's account opened at the branch Commercial loans need to check inventory and debt monthly and check the use of customer revenues, stipulating that the source of money from the loan plan must repay as soon as the money is collected, whether undue loan 5.2.4 Develop a system to report bad debt transferability The bad debt transfer reporting system will be developed at each level, starting from the Transaction Office, Head Office and Branch Office The main objective of building a detailed debt reporting system at all levels is to be able to handle the debt immediately when the client begins to show signs of financial indebtedness to the client In addition, it is possible to track and manage the loan processing process at each level and over time as well as measures to handle debt The reporting system will focus on two main targets: first, bad debt, debt using risk provisions is applying collection methods; Secondly, it is difficult for customers to meet their obligations to the Bank However, the loans are still in good debt category and not yet in the debt classification period This reporting system should provide the following information: - Detailed information about customers, about the loan - Time, cause of overdue debt - Information on the status of security assets and the legality of property records - Detailed measures and results have been applied to handle the loan - Sources of debt collection from customers and the feasibility of the recovery plan - Proposing measures to handle debt to higher level for decision Proposed forms and synthesis of problematic debt reporting system will be implemented by the Debt and Credit Risk Department at the Head Office, based on the situation of debt customers Issues raised by the branches, thereby proposing measures as well as time and debt recovery plans for approval by the Board 63 Detailed customer reports and status are updated and reported weekly, directly to each credit officer by checking the customer situation, loan utilization as well as payment capacity, principal and interests with banks Each customer report is made immediately when the customer shows signs of not being able to repay the bank instead of updating the customer situation at the end of the month when the credit scoring has been completed and the loan has been transferred to the debt group bad debt, then the debt processing will not take the initiative and detrimental to the Bank when starting to dispose of assets 5.3 Limit research models and future research directions The results of the factor modeling test on bad debt at Vietcombank showed that the data for independent variables are suitable and can be explained for independent variables However, the model and data are still limited: Firstly, the number of observations taken into the model is 120 observations, only the minimum number of observations recommended by Green (2011) and Tabachinick &Fidell (2007) Moreover, the research focus only on one commercial bank, while credit and bad debt are heavily influenced by other macroeconomic factors, so that the regression model gives the main results In fact, it is necessary to extend the scope of the study to increase the number of observations in the survey data before examining the regression model Secondly, the model does not address the qualitative factors that affect bad debt such as policy risk due to the governance of the government, moral hazard factors of bank officials Thirdly, the model does not mention the impact of some recent potential risky loan portfolios such as real estate loan, securities lending Some new research directions may focus on the following:  Research on factors affecting bad debts by groups of commercial banks: Stateowned commercial banks, joint stock commercial banks;  Research on qualitative factors in order to clarify the impact of bad debts of commercial banks; 64  Research on additional factors related to the list of loans with potential risks such as real estate, securities 5.4 Conclusions Bad debt and dealing with bad debt are one of the issues that are affecting the business of each commercial bank in particular and the economy as a whole, so study the issues NPLs are a necessary research direction and carry real meaning This thesis focuses on the factors affecting the bad debt ratio at Vietcombank The results of the study show that, similar to most other studies conducted previously, NPLs at Vietcombank are affected by the factors of total asset size, credit growth and operational efficiency business By examining data on Vietcombank's 2005-2016 financial statements and the results of the regression testing, the author has proposed some solutions and recommendations to limit bad debt ratios at Vietcombank The author wishes solutions and recommendations can help Vietcombank have policies to direct and business activities suitable for sustainable development in the future In addition, identifying constraints and suggesting new research directions is the basis for future research to focus on the factors that affect the NPLs of 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Chair: Prof, PhD Tsan Eric Date： 2018／11 ／24 ABSTRACT Thesis Title: Managing Non- Performing Loans at The Joint Stock Commercial Bank for Foreign Trade of Vietnam (VCB) – Factors and Recommendations. .. Non- Performing Loans at The Joint Stock Commercial Bank for Foreign Trade of Vietnam (VCB) – Factors and Recommendations? ??published by Mr Nguyen Nam Chinh in the Master Program of Graduate School of Department