Credit risk management case study of BIDV

MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECNOMICS HOCHIMINH CITY  - 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 understanding managers, about credit allowed risk me to management get in deeper 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 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-performingloan 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 nonperforming-loan ratio in BIDV These techniques and tools help Page ii 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 .4 Figure 1.3: Method of secondary data Figure 1.4: Population and sampling .8 Figure 1.5: Quota sampling method .9 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 Page 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 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 Total Valid Valid Valid Valid Valid Impact of credit information 100 100 100 Normal Agree Strongly Total Reward policy Frequenc Percen Valid y t Percent 11 11 11 57 57 57 32 32 32 100 100 100 Disagree Normal Agree Strongly Total Impact of loan policy Frequenc Percen Valid y t Percent 5 10 10 10 64 64 64 21 21 21 100 100 100 Disagree Normal Agree Strongly Total Well-defined loan policy Valid Frequenc Percen y t Percent 2 15 15 15 56 56 56 27 27 27 100 100 100 Disagree Normal Agree Strongly Total Well-communicated loan policy Frequenc Percen Valid Cumulative y t Percent Percent 3 3 16 16 16 19 53 53 53 72 28 28 28 100 100 100 100 Disagree Normal Agree Strongly Frequency of amendment Frequenc Percen Valid y t Percent 2 15 15 15 67 67 67 16 16 16 Cumulative Percent Cumulative Percent Cumulative Percent Cumulative Percent 11 68 100 15 79 100 17 73 100 17 84 100 Impact of credit information Total 100 100 100 Case Processing Summary Cases Valid Exclude N % 10 0 100 .0 10 100 da a Listwise deletion based on all variables in the procedure Reliability Statistics Cronbach's Alpha N of Item 703 Item Statistics Mean Impact of credit Source of credit information Credit information selecting and Credit information sharing Credit information checking 4.270 3.940 4.030 4.250 3.940 N 10 10 10 10 10 6789 Item-Total Statistics Scale Mean if Impact of credit Source of credit information Credit information selecting and Credit information sharing Credit information checking Std Deviati on 6789 6583 6571 16.16 16.49 00 16.40 00 16.18 00 16.49 Scale Varianc e if Item 3.65 3.52 3.96 3.74 3.74 00 Summary Case Processing Correcte d ItemTotal Cronbach' s Alpha if Item 406 562 681 611 392 681 488 643 461 654 Impact of credit information N Cases Valid % 100 100 0 Exclud e da 100 100 Total a Listwise deletion based on all variables in the procedure Reliability Statistics Cronbach's N of Alpha 817 Item Statistics Mean Technology operation Credit staff competence and Frequency of facility Technology investment & BIDV's growth progress matching Modern technology Frequency of facility Technology investment & BIDV's growth progress N 4.110 100 3.890 6650 100 3.920 6305 100 4.060 5829 3.900 6113 Item-Total Statistics Scale Mean if Technology Credit staff competence and Std Devia ti on 100 100 Scale Correcte Variance if d ItemItem Total Cronbach' s Alpha if Item 15.77 3.81 585 790 15.99 00 3.70 678 760 15.96 00 3.97 600 784 15.82 00 4.06 628 777 Impact of credit information 15.98 00 Modern technology RELIABILITY 4.12 /VARIABLES=CS1 CS2 CS3 CS4 CS5 /SCALE('ALL VARIABLES') ALL /MODEL=ALPHA /STATISTICS=DESCRIPTIVE SCALE /SUMMARY=TOTAL Case Processing Summary N Cases Valid % 10 0 Exclude 100 .0 10 100 da a Listwise deletion based on all variables in the procedure Reliability Statistics Cronbach' s Alpha N of 819 Item Statistics Mean Characteristic of credit staffs Comprehensive training course Proper supervision system Important role of board of directors Reward policy Std Deviati N 4.0 744 10 3.9 748 10 4.0 667 10 4.0 764 10 4.2 624 10 Item-Total Statistics0 560 795 Case Processing Summary N Cases Valid Exclude da % 10 0 100 10 100 0 Scale Scale Mean Variance if if Item Item Characteristic of credit staffs Comprehensive training course Proper supervision system Important role of board of directors RELIABILITY 4.72 638 776 16.3 4.83 592 790 16.2 5.10 597 788 16.1 4.71 616 783 /SCALE('ALL VARIABLES') ALL /MODEL=ALPHA /STATISTICS=DESCRIPTIVE SCALE /SUMMARY=TOTAL Case Processing Summary Cases Valid Exclude % 10 0 100 .0 10 100 da a Listwise deletion based on all variables in the procedure Reliability Statistics Cronbac h's N of Cronbach' s Alpha if Item 16.1 /VARIABLES=LP1 LP2 LP3 LP4 N Correcte d ItemTotal .851 Case Processing Summary N Cases Valid 10 0 Exclude da % 100 .0 10 100 Item Statistics Mean Impact of loan Well-defined loan policy Well-communicated loan policy Frequency amendment of N 4.0 718 10 4.0 706 10 4.0 750 10 3.9 627 10 Item-Total Statistics Scale Correcte Cronbach' Scale Mean Variance if d Item- s Alpha if if Item Item Item Total 12.1 808 3.14 697 Impact of loan Well-defined loan policy Well-communicated loan policy Frequency amendment RELIABILITY Std Deviati of 12.0 3.10 736 791 12.0 3.10 670 821 12.1 3.48 669 821 /VARIABLES=CI TECH CS LP /SCALE('ALL VARIABLES') ALL /MODEL=ALPHA /STATISTICS=DESCRIPTIVE SCALE /SUMMARY=TOTAL Case Processing Summary N Cases Valid Exclude da % 100 100.0 0 100 100.0 a Listwise deletion based on all variables in the procedure Reliability Statistics Cronbach' s Alpha N of 787 Item Statistics Mean Credit informati Technology Credit staff Loan policy Std Deviation N 4.086 3.976 4.044 46559 100 48516 100 54204 100 4.030 58310 100 Item-Total Statistics Scale Mean Scale if Item Variance if Credit information Technology Credit staff Loan policy Cronbach's Alpha if Item Corrected Item- Total 12.05 00 12.16 00 12.09 1.692 611 730 1.726 539 761 1.557 590 737 20 12.10 60 1.408 651 705 ... 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. 4Credit 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