(Luận văn thạc sĩ) credit risk management case study of BIDV

MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECNOMICS HOCHIMINH CITY K - BÙI NGUYÊN NGỌC CREDIT RISK MANAGEMENT: CASE STUDY OF BIDV MASTES’S THESIS In Banking Ology code: 60.31.12 Supervisor: Dr Nguyễn Văn Phúc Ho Chi Minh City – 2010 ACKNOWLEDGMENT I owe a debt of gratitude to many people who helped me complete this thesis I would like to acknowledge the help of all First of all I would like to express my deepest acknowledgement to my supervisor, Doctor Phóc Nguyễn Văn, for his valuable advice and recommendations Then, I would like to thank my superiors and colleagues who agreed to be interviewed and/or completed the survey questionnaires The information they provided, especially from managers/vice managers, allowed me to get deeper understanding about credit risk management in BIDV and deriving the findings of this study Finally, I want to express my sincere thanks to every member of my family for their encouragement and support during the time I devoted to this dissertation Page i ABSTRACT Credit risk is one of the most popular risks in banks due to their intermediary functions: lending and borrowing An excessive level of credit risk may destroy not only banks’ profitability but also the stability of global banking system Therefore, it is necessary for banks to develop an effective credit risk management strategy not only to protect themselves but also to prevent banking crises In case of BIDV, BIDV is one of four State Banks established when Viet Nam banking system is at a very early stage of development For a long time, BIDV was controlled in allocating loans by government Therefore, credit risk management has been the main challenge facing the board of BIDV managers With the best try of this board, since 2008, BIDV has controlled credit risk that comply with international standard (non-performing-loan ratio was less than 3%) This is the main reason that drove this study to describe credit risk management in BIDV, to know why non-performing loan ratio in BIDV has been sharply reduced from 38.3% in 2004 to 2.82% in 2009 Both secondary data and primary data are needed for this study Collected data is analyzed by Statistical Package for Social Studies version 16.0 (SPSS) Cronbach alpha is used to measure coefficient of reliability and t-test technique is used to test the hypotheses about the four factors influence reduction of non-performing-loan ratio in BIDV These techniques and tools help collected data transform into information that will answer the researcher’s questions Page ii LIST OF FIGURES Figure 1.1: Structure of chapter Figure 1.2: Field of research problem Figure 1.3: Method of secondary data Figure 1.4: Population and sampling Figure 1.5: Quota sampling method Figure 1.6: Structure of the study 12 Figure 3.1: BIDV Organization Chart 40 Figure 3.2: BIDV’s non-performing loans 45 Figure 3.3: BIDV’s loan structure by collateral 46 Figure 3.4: BIDV’s loan structure by economic sector 47 Figure 4.1: Respondents’ position 57 Figure 4.2: Respondents’ working years in BIDV 58 Page iii LIST OF TABLES Table 2.1: Level of specific provision 20 Table 2.2: Example of a loan rating system and bond rating mapping 23 Table 2.3: Strategies for reducing and scoping with portfolio credit risk 26 Table 3.1: BIDV’s key performance indicators 41 Table 3.2: BIDV’s credit indicators 43 Table 3.3: Loan classification in BIDV 49 Table 3.4: Summarize four factors influencing NPL ratio in BIDV 52 Table 4.1: Four variables with different aspects 58 Table 4.2: Level of agreement in survey questionnaire 59 Table 4.3: The overall score of Cronbach’s alpha 60 Table 4.4: The t-test result 61 Table 4.5: Summary of hypotheses testing results 64 LIST OF APPENDICES Appendix A 74 Appendix B 75 Appendix C 79 Page iv TABLE OF CONTENTS Acknowledgment i Abstract ii List of figures .iii List of tables iv List of appendices iv Table of contents v Chapter 1: Introduction 1.1 Introduction 1.2 Rationale of the study 1.3 Statement of the problem and the scope of the study 1.4 Research questions and objectives 1.5 Methodology 1.5.1 Research design 1.5.2 Data collection 1.5.3 Data analysis 10 1.6 Significance of the study 12 1.7 Structure of the study 12 Chapter 2: Literature review 14 2.1 Introduction 14 2.2 Basic functions of banks 14 2.3 Lending business 15 2.3.1 The board of directors’ written loan policy 15 2.3.2 Lending procedure 16 2.4 Credit risk in banks 17 2.4.1 Credit risk 17 2.4.2 Loan classification 19 2.4.3 Loan loss provision 20 Page v 2.4.4 Non-performing loan 21 2.5 Credit risk measurement 21 2.5.1 Traditional approaches 21 2.5.2 Modern approaches 24 2.6 External factors that affect the level of credit risk 27 2.6.1 Financial deregulation 28 2.6.2 Supervision and re-regulation 28 2.6.3 Competition 29 2.6.4 The recent financial crisis 30 2.7 Internal factors that affect the level of credit risk 30 2.7.1 Credit information 30 2.7.2 Technology 32 2.7.3 Credit staffs 33 2.7.4 Loan policy 34 2.8 Summary 35 Chapter 3: Case study of BIDV 37 3.1 Introduction 37 3.2 Overview of BIDV 37 3.2.1 Introduction 37 3.2.2Organization structure 37 3.2.3 BIDV business performance 41 3.3 Lending business 43 3.3.1 Overview 43 3.3.2 Non-performing loans and loan loss provision 44 3.3.3 Loan structure 45 3.4 Internal factors that influence non-performing-loan ratio in BIDV 47 3.4.1 Credit information 47 3.4.2 Technology 48 3.4.3 Credit staff 50 3.4.4 Loan policy 51 Page vi 3.4.5 Suggesting hypotheses 52 3.5 Summary 55 Chapter 4: Data analysis and findings 56 4.1 Introduction 56 4.2 Data collection results 56 4.3 Data analysis 57 4.3.1 Descriptive statistic 57 4.3.2 Measures of reliability 58 4.3.3 Statistical hypotheses testing (t-test) 60 4.4 Comparison and discussion of findings 62 4.4.1 Credit information 62 4.4.2 Technology 62 4.4.3 Credit staffs 63 4.4.4 Loan policy 63 4.5 Result of hypotheses testing 64 4.6 Summary 64 Chapter 5: Recommendation and Conclusion 66 5.1 Introduction 66 5.2 Reviewing research questions 66 5.3 Recommendation for BIDV 66 5.3.1 Credit information 66 5.3.2 Technology 67 5.3.3 Credit staffs 67 5.3.4 Loan policy 68 5.4 Recommendation for other banks 68 5.5 Limitation of the research 69 5.6 Summarizing and concluding the dissertation 69 References 70 Page vii CHAPTER ONE INTRODUCTION 1.1 Introduction: This chapter provides a general introduction to the research study The purpose is to establish foundations for following chapters and the study as a whole, by providing a general picture of the study This chapter is structured into seven sections as presented by figure 1.1 Section 1.1 provides a general introduction to the chapter and section 1.2 examines the research background where the research problem is identified Section 1.3 defines the statement of the problem and scope of the study Section 1.4 which includes two subsections 1.4.1 and 1.4.2 defines the research questions and research objectives Subsection 1.4.1 addresses the research questions that will be respectively answered in chapters of the study Subsection 1.4.2 presents research objectives that the study covers in the process of solving the research problem defined Section 1.5 discusses the aspects of research methodology such as selecting from alternative types of research, research design and research techniques Section 1.6 points out the significance and scope of the study, and finally section 1.7 describes overall structure of the thesis Chapter 1: Introduction Page Section 1: Introduction Section 2: Rationale of the study Section 3: Statement of the problem and scope of the study Section 4: Research questions and objectives Section 5: Methodology Section 6: Significance of the study Section 7: Structure of the study Figure 1.1: Structure of chapter 1.2 Rationale of the study: In today’s world, in order to meet customers’ requirements, there is a need for banks to diversify their business including other activities such as payments, leasing, and investments besides the two traditional functions of lending and borrowing However, lending still plays an important role in banks because banks’ revenue primarily comes from lending revenue which contributes over a half of bank total operating (about 70% in case of BIDV) The traditional way that banks make their profit is to take risk in exchange for an acceptable return to not only cover the cost of funding but also maintain their profitability Thus, the main business of banks is not, as everyone might assume, taking deposits and making loans but minimizing the risk in collecting interest and principle from the loans which is known as managing credit risk (Burton & Lombra 2006) Chapter 1: Introduction Page APPENDIX A MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECONOMICS HOCHIMINH CITY Bïi Nguyªn Ngọc CREDIT RISK MANAGEMENT: CASE STUDY OF BIDV MASTER S THESIS In Banking Ology code: 603112 Supervisor Dr Nguyễn Văn Phúc Ho Chi Minh City - 2010 Page 74 APPENDIX B QUESTIONNAIRE Dear sir/ madam This questionnaire is one part of a research project to understand and improve the credit risk management in BIDV Your responses are important in enabling me to obtain a full understanding of this topical issue The questionnaire may take you about ten minutes to complete Please follow the instruction to answer the questions If you wish to add further comments, please feel free to so The information you provide will be treated in the strictest confidence The finding from your questionnaire and others will be used as the main data set of my dissertation for my master course in Ho Chi Minh City University of Economics (HUE) I hope you will find completing the questionnaire enjoyable Please return the completed questionnaire to the person who delivered it to you/ or just simply reply to this email address Thank you for your help SURVEY OF CREDIT RISK MANAGEMENT IN BIDV SECTION 1: Back ground Instruction: for the following questions please tick your most favorite answer to each question Question 1: How long have you worked in banking industry? [ ] Less than year [ ] 1-3 years [ ] 5-10 years [ ] More than 10 years [ ] 3-5 years Question 2: How long have you worked in BIDV? Page 75 [ ] Less than year [ ] 1-3 years [ ] 5-10 years [ ] More than 10 years [ ] 3-5 years Question 3: What is your current position in your department/bank? [ ] Manager [ ] Supervisor [ ] Staff Question 4: What best describes your background? [ ] Banking [ ] Economics [ ] Technical field SECTION 2: Credit risk management in BIDV (Factors influencing credit risk (non-performing-loan ratio in BIDV) Instruction: Each below question has five choices of answers which are arranged in increasing of agreement level order (Number is Strongly disagree, number is disagree, number is normal, number is agree, number is strongly agree) For the following questions, please circle the most appropriate number to each question Level of agreement No Question Low disagree The quality of credit information has a great Normal High agree 5 5 impact to the reduction of non-performingloan ratio in BIDV What is your viewpoint? The quality of credit information depends on the source that they are collected from? What you think? The quality of credit information depends on the information selecting and systemizing progress of BIDV? What is your viewpoint? If banks are willing to cooperate in sharing credit information, the quality of credit information will be improved significantly What is your opinion? Page 76 The quality of credit information depends on 5 5 5 5 the information updating progress of BIDV? What you think? Effective technology operation has a great impact to BIDV’s level of credit risk Do you agree with this statement? The effectiveness of technology can be improved if BIDV staffs have enough competence to operate and explore technology facility’s functions Do you agree with this statement? The effectiveness depends on the of BIDV frequency technology of facility maintenance How is your view point? The effectiveness of technology can be improved if the technology investment is matching with the growth progress of BIDV What you think? 10 The effectiveness of BIDV technology depends on the frequency of update What is your viewpoint? 11 Characteristic of credit officers play an important role in reducing NPL ratio in BIDV What you think? 12 If BIDV frequently provides comprehensive training courses, the skills of BIDV credit staff can be improve significant Do you agree? 13 The strict and proper supervisor system is needed to prevent the ethic issues of credit Page 77 officers Do you agree? 14 Board of directors plays an important role in 5 5 5 reducing NPL ratio in BIDV What is your opinion? 15 Suitable reward policy plays an important role in improving the quality of credit officers What is your opinion? 16 Loan policy is a critical factor affect to the level of credit risk What you think? 17 A well-defined loan policy is important in reducing the level of credit risk What you think? 18 A well-communicated loan policy is important in reducing the level of credit risk What is your opinion? 19 Loan policy must be frequently amended in order to adept internal and external environment What is your viewpoint? SECTION 3: Suggestion for future Instruction: please state your own idea to answer this question What suggestions would you make to help BIDV continuously reducing nonperforming-loan ratio? Thank you for your help and cooperation! Page 78 APPENDIX C Descriptive statistics Impact of credit information Frequency Percent Valid Percent Valid Strongly disagree Disagree Normal Agree Strongly agree Total 2 2 54 39 100 54 39 100 54 39 100 61 100 Source of credit information Frequency Percent Valid Percent Valid Disagree Normal Agree Strongly agree Total Cumulative Percent 72 14 100 72 14 100 72 14 100 Cumulative Percent 14 86 100 Credit information selecting and systemizing Frequency Percent Valid Percent Cumulative Percent Valid Disagree Normal Agree Strongly agree Total 17 60 22 100 17 60 22 100 17 60 22 100 Credit information sharing Frequency Percent Valid Percent Valid Disagree Normal Agree Strongly agree Total 3 60 34 100 3 60 34 100 3 60 34 100 18 78 100 Cumulative Percent 66 100 Credit information checking Page 79 Impact of credit information Frequency Percent Valid Percent Valid Disagree Normal Agree Strongly agree Total 17 63 17 100 17 63 17 100 17 63 17 100 Technology operation Frequency Percent Valid Percent Valid Strongly disagree Disagree Normal Agree Strongly agree Total Cumulative Percent 20 83 100 Cumulative Percent 1 1 10 62 26 100 10 62 26 100 10 62 26 100 12 74 100 Credit staff competence and technology matching Frequency Percent Valid Percent Cumulative Percent Valid Disagree Normal Agree Strongly agree Total 13 70 12 100 13 70 12 100 13 70 12 100 Frequency of facility maintenance Frequency Percent Valid Percent Valid Disagree Normal Agree Strongly agree Total 18 66 14 100 18 66 14 100 18 66 14 100 18 88 100 Cumulative Percent 20 86 100 Technology investment & BIDV's growth progress matching Frequency Percent Valid Percent Cumulative Percent Valid Disagree Normal Agree Strongly agree 11 69 19 11 69 19 11 69 19 12 81 100 Page 80 Total Impact of credit information 100 100 100 Modern technology Frequency Percent Valid Percent Valid Disagree Normal Agree Strongly agree Total 15 71 11 100 15 71 11 100 15 71 11 100 Characteristic of credit staffs Frequency Percent Valid Percent Valid Normal Agree Strongly agree Total 25 45 30 100 25 45 30 100 25 45 30 100 Comprehensive training course Frequency Percent Valid Percent Valid Disagree Normal Agree Strongly agree Total 26 50 22 100 26 50 22 100 26 50 22 100 Proper supervision system Frequency Percent Valid Percent Valid Disagree Normal Agree Strongly agree Total 16 62 20 100 16 62 20 100 16 62 20 100 Important role of board of directors Frequency Percent Valid Percent Valid Unimportant Normal Important Very important 21 48 29 21 48 29 21 48 29 Cumulative Percent 18 89 100 Cumulative Percent 25 70 100 Cumulative Percent 28 78 100 Cumulative Percent 18 80 100 Cumulative Percent 23 71 100 Page 81 Total Impact of credit information 100 100 100 Reward policy Frequency Percent Valid Percent Valid Normal Agree Strongly agree Total 11 57 32 100 11 57 32 100 11 57 32 100 Impact of loan policy Frequency Percent Valid Percent Valid Disagree Normal Agree Strongly agree Total 10 64 21 100 10 64 21 100 10 64 21 100 Well-defined loan policy Frequency Percent Valid Percent Valid Disagree Normal Agree Strongly agree Total 15 56 27 100 15 56 27 100 15 56 27 100 Well-communicated loan policy Frequency Percent Valid Percent Valid Disagree Normal Agree Strongly agree Total 16 53 28 100 16 53 28 100 16 53 28 100 Frequency of amendment Frequency Percent Valid Percent Valid Disagree Normal Agree Strongly agree 15 67 16 15 67 16 15 67 16 Cumulative Percent 11 68 100 Cumulative Percent 15 79 100 Cumulative Percent 17 73 100 Cumulative Percent 19 72 100 Cumulative Percent 17 84 100 Page 82 Impact of credit information 100 100 100 Total Case Processing Summary Cases N % 100 100.0 0 100 100.0 Valid Excludeda Total a Listwise deletion based on all variables in the procedure Reliability Statistics Cronbach's Alpha N of Items 703 Item Statistics Mean Std Deviation N Impact of credit information 4.2700 76350 100 Source of credit information 3.9400 67898 100 Credit information selecting and systemizing 4.0300 65836 100 Credit information sharing 4.2500 65713 100 Credit information checking 3.9400 67898 100 Item-Total Statistics Scale Variance Scale Mean if if Item Item Deleted Deleted Corrected Item-Total Correlation Cronbach's Alpha if Item Deleted Impact of credit information 16.1600 3.651 406 681 Source of credit information 16.4900 3.525 562 611 Credit information selecting and systemizing 16.4000 3.960 392 681 Credit information sharing 16.1800 3.745 488 643 Credit information checking 16.4900 3.747 461 654 Case Processing Summary Page 83 Impact of credit information N Cases Valid % 100 100.0 0 100 100.0 Exclude da Total a Listwise deletion based on all variables in the procedure Reliability Statistics Cronbach's Alpha N of Items 817 Item Statistics Mean Std Deviati on N Technology operation 4.1100 69479 100 Credit staff competence and technology matching 3.8900 66507 100 Frequency maintenance 3.9200 63054 100 Technology investment & BIDV's growth progress matching 4.0600 58292 100 Modern technology 3.9000 61134 100 of facility Item-Total Statistics Scale Mean if Item Deleted Scale Variance if Item Deleted Corrected Item-Total Correlation Cronbach's Alpha if Item Deleted Technology operation 15.7700 3.815 585 790 Credit staff competence and technology matching 15.9900 3.707 678 760 Frequency of maintenance 15.9600 3.978 600 784 15.8200 4.068 628 777 facility Technology investment & BIDV's growth progress matching Page 84 Impact of credit information Modern technology RELIABILITY 15.9800 4.121 560 795 /VARIABLES=CS1 CS2 CS3 CS4 CS5 /SCALE('ALL VARIABLES') ALL /MODEL=ALPHA /STATISTICS=DESCRIPTIVE SCALE /SUMMARY=TOTAL Case Processing Summary N Cases Valid Excludeda Total % 100 100.0 0 100 100.0 a Listwise deletion based on all variables in the procedure Reliability Statistics Cronbach's Alpha N of Items 819 Item Statistics Mean Std Deviation N Characteristic of credit staffs 4.05 744 100 Comprehensive training course 3.92 748 100 Proper system 4.00 667 100 Important role of board of directors 4.04 764 100 Reward policy 4.21 624 100 supervision Item-Total Statistics Page 85 Case Processing Summary N Cases Valid Excludeda Total % 100 100.0 0 100 100.0 Scale Scale Mean if Variance if Item Deleted Item Deleted Corrected Item-Total Correlation Cronbach's Alpha if Item Deleted Characteristic of credit staffs 16.17 4.728 638 776 Comprehensive training course 16.30 4.838 592 790 Proper system 16.22 5.103 597 788 16.18 4.715 616 783 supervision Important role of board of directors RELIABILITY /VARIABLES=LP1 LP2 LP3 LP4 /SCALE('ALL VARIABLES') ALL /MODEL=ALPHA /STATISTICS=DESCRIPTIVE SCALE /SUMMARY=TOTAL Case Processing Summary N Cases Valid Excludeda Total % 100 100.0 0 100 100.0 a Listwise deletion based on all variables in the procedure Reliability Statistics Cronbach's Alpha 851 N of Items Page 86 Case Processing Summary N Cases Valid Excludeda Total % 100 100.0 0 100 100.0 Item Statistics Std Deviation Mean N Impact of loan policy 4.01 718 100 Well-defined policy 4.08 706 100 4.06 750 100 3.97 627 100 loan Well-communicated loan policy Frequency amendment of Item-Total Statistics Scale Scale Mean if Variance if Item Deleted Item Deleted Corrected Item-Total Correlation Cronbach's Alpha if Item Deleted Impact of loan policy 12.11 3.149 697 808 Well-defined policy 12.04 3.109 736 791 12.06 3.107 670 821 12.15 3.482 669 821 loan Well-communicated loan policy Frequency amendment RELIABILITY of /VARIABLES=CI TECH CS LP /SCALE('ALL VARIABLES') ALL /MODEL=ALPHA /STATISTICS=DESCRIPTIVE SCALE /SUMMARY=TOTAL Case Processing Summary Page 87 N Cases Valid Excludeda Total % 100 100.0 0 100 100.0 a Listwise deletion based on all variables in the procedure Reliability Statistics Cronbach's Alpha 787 N of Items Item Statistics Mean Std Deviation N Credit information 4.0860 46559 100 Technology 3.9760 48516 100 Credit staff 4.0440 54204 100 Loan policy 4.0300 58310 100 Item-Total Statistics Scale Mean if Scale Variance if Item Deleted Item Deleted Corrected ItemTotal Correlation Cronbach's Alpha if Item Deleted Credit information 12.0500 1.692 611 730 Technology 12.1600 1.726 539 761 Credit staff 12.0920 1.557 590 737 Loan policy 12.1060 1.408 651 705 Page 88 ... First is credit risk management background; second is case study of BIDV credit risk management The first part provides some background knowledge about credit risk, credit risk measurements, management. .. discussed Chapter 3: Case study of BIDV: This chapter provides an overview of bank for investment and development of Vietnam (BIDV) and BIDV? ??s credit risk management is the main part of this chapter... charge of as a loss 2.4 Credit risk in banks 2.4.1 Credit risk Banking is the management of risk Banks accept risk in order to earn profits They balance alternative strategies in terms of their risk/ return