Microsoft Word F69703116 doc 28 Chapter4 Research Results and Analysis Contents of Chapter4 includes (1) To analyze the general information of investigated persons; (2) To analyze the causes of credit risk in banks 4 1 To Analyze the General Information of Investigated Persons Table 4 1 The Size of Credit Deposit Frequency Percent Valid Percent Cumulative Percent Valid Less than 100 billions 13 13 0 13 0 13 0 From 100 to 500 billions 36 36 0 36 0 49 0 Great than 500 billions 51 51 0 51 0 100 0 T.
Chapter4 Research Results and Analysis Contents of Chapter4 includes: (1) To analyze the general information of investigated persons; (2) To analyze the causes of credit risk in banks 4.1 To Analyze the General Information of Investigated Persons Table 4-1 The Size of Credit Deposit Valid Cumulative Percent Percent Frequency Percent 13 13.0 13.0 13.0 From 100 to 500 billions 36 36.0 36.0 49.0 Great than 500 billions 51 51.0 51.0 100.0 100 100.0 100.0 Valid Less than 100 billions Total In 100 questions, the total of credit owe surplus under VND 100 billions accounted for 13%, from VND 100 billions to VND 500 billions at 36% and over VND 500 billions at 51% Table 4-2 About Your Working Experience in Bank Valid Cumulative Percent Percent Frequency Percent Valid Less than years 46 46.0 46.0 46.0 From to years 23 23.0 23.0 69.0 Great than years 31 31.0 31.0 100.0 100 100.0 100.0 Total Looking into those 100 questions in bank clerk in credit field working under years reach 46%, under years at 23%, and over years at 31% respectively 28 Table 4-3 Professional Degree Valid Cumulative Percent Percent Frequency Percent 2.0 2.0 2.0 1.0 1.0 3.0 87 87.0 87.0 90.0 10 10.0 10.0 100.0 100 100.0 100.0 Valid Intermediate vocational College University Graduated University Total According to the survey’s results, 87% bank clerks got university degree, 10% got MA degree, 2% graduated from vocational school and only 1% graduated from college Comment: the results show that the surveyed bank clerks in credit field who have less years experience but hold university degrees and work with over VND 500 billion credit owe projects 4.2 To Analyze the Causes of Credit Risk in Banks 4.2.1 The reasons causing credit risk (the outside causes) Table 4-4 The Reasons Causing Credit Risk Std N Mean Deviation 1/ The change of economic 100 2.52 522 2/ Irresistibility causes 100 2.47 643 3/ The Government's policies 100 2.51 595 4/ The regulator modifies regulation 100 2.31 748 5/ The credit information system 100 2.16 838 2.39 669 Average As stated before the factor “The change of economic” has the most value at 2.52 It showed that the cause of credit risk from economics fluctuation is so big In 29 addition, the factor “Irresistibility causes” gained 2.47 to reflect that risks causing from natural disasters in big too, VN yearly suffers from storms and floods so the people lives and their doing business are affected sharply It’s also true for reduced goods consumption, collecting materials, goods shipping, payment, and so on As a result many enterprises can’t pay loans to the banks Moreover, the factor “The government’s policies” and “The regulator modifies regulation” have the value at 2.51 and 2.31 respectively , to see that Vietnamese laws environment are not stable and synchronous to affect the credit activities of the national banks In certain circumstances, per person understands and implements differently from the other leading to difficult implementation Many relating banking activity documents are sometimes suitable at the limited period of time and real situations The mechanism and management policies are left behind the economic development leading to changing policies frequently, and sometimes contrast to make the economy unsteadily In many examples, enterprises have lost their chances leading to loss and difficulty The factor “The credit information system” has the least Mean value at 2.16 to expose the supplying information system for the markets and for businesses is limited and not developed properly 4.2.2 The credit risk be caused from customers Table 4-5 The Credit Risk Be Caused From Customers Std N Mean Deviation 6/ The loan procedure too looses 100 2.30 798 7/ The amount of loan of customer 100 2.42 622 8/ The customers intentionally deceive 100 2.28 740 9/ Customers not perform a credit contract 100 2.27 763 10/ The customers change initial purpose 100 2.40 696 100 2.63 614 2.38 706 11/ Customer offer the truthful financial reports Average 30 According to the analyzed data above, the factor “Customer offers the truthful financial reports” reached the highest value Mean at 2.63 to show that the risk rate about the liable financial reports offered by clients is not so serious In additions, the factors “The loan procedure too looses” and “The amount of loan of customer” have the value of mean at 2.3 and 2.42 respectively to say that the financial abilities of enterprises are weak and the earning profits are low, so they must depend on the loans from the bank to operate, the rate of own capital pouring into business projects is not high As a result, the risk of loss in doing business of enterprises would affect directly to the bank, and the bank will lose the loans if the businesses lost capital or bankruptcy The factor “The customers intentionally deceive”, “Customers not perform a credit contract” and “The customers change initial purpose” also have the high value at 2.28 – 2.27 – 2.4 respectively to review that the risk of credit problems from the customers relating to the above factors are concerned too Many customers have the ideas of cheating, providing false financial information to obtain capital, creating virtual documents, making false contracts, shaking a hand with the sellers to use the loans to deceive the bank or not to implement payment deliberately, and other customers use the loans wrongly and take advantages of withdrawing cash for the fees outside the business strategies registered for the banks before 4.2.3 The credit be caused from the weakness of risk management of bank Table 4-6 The Credit Risk Be Caused From the Weakness of Risk Management of Bank (1) Std N Mean Deviation 12/ The lack of customer's information 100 2.33 682 13/ The lack of information of CIC 100 2.09 889 14/ The difficulty in verifying information 100 2.44 574 100 2.29 795 100 2.12 832 2.25 754 17/ For evaluating and analyzing risk of customer 27/ The records declared on IPCAS system Average 31 Base on the surveyed results, the factors of “The lack of customer’s information”, “The lack of information of CIC”, “The difficulty in verifying information”, “For evaluating and analyzing risk of customer” and “The records declared on IPCAS system” have the value of mean at 2.33 - 2.09 - 2.44 - 2.29 - 2.12 respectively to say that the risk rates from these factors are not so serious The lack of customer’s information to examine for loans happened frequently, the information supplied from CIC only reflected about the credit deals, the financial information is out of date and even having none The information systems of the bank are not strong enough, and there is no information library about economy fields, many businesses related to the systems must rely on their searching information or supplied by their clients In the circumstances, if the clients want to cheat, the bank will face difficulties to check, but the loan tellers usually base on the reports of business results offered by clients And so that, the lack of information and liable one of the customers is the barrier of loan tellers in their tasks and this is the invisible problem to cause the risk of credit in the credit organizations Besides, IPCAS system is not properly liable that is the factor to urge the banks need to invest the information system strongly Table 4-7 The Credit Risk Be Caused From the Weakness of Risk Management of Bank (2) Std N Mean Deviation 18/ The reducing in loan terms 100 2.30 785 19/ The cheating by staff 100 1.98 932 100 2.36 659 23/ The loan of bank 100 2.51 674 30/ The credit information sharing 100 2.38 693 31/ The updated relevant credit information 100 2.36 718 2.32 744 20/ The low qualification and uncompleted training Average 32 Based on the above table, the factor “The reducing in loan terms” and “The low qualification and completed training” have the value of mean at 2.3 - 2.36 to reflect that the banks In order to attract and maintain customer from the competition of other credit organization, the banks also make some mistakes when they cut some terms and conditions of loans At the same time, credit tellers’ experience and skills are also limited They truly believed customers objectively when they decided to issues the credit cards while they didn’t have enough clients’ information such as real and prestige finance, rights finance as well as prestigious borrowers, these cause risk of debtors payment abilities But the agents seldom agree with clients such as “The cheating by staff” has the value at 1.98 Besides the factors “The loan of bank” has the highest value at 2.51, this means that the loan terms of the banks only concentrated on a group of customers, when these customers have difficulties in doing business, bad businesses results, they can’t pay loan on time Loans rate in the banks are strongly increased In addition, the two factors “The credit information sharing” and “The updated relevant credit information” have the low value at 2.38 - 2.36 Meanwhile, the exchanging information among credit organizations are not good and the competition as well as the lack of customers’ collateral information, some firms take advantage of borrowing loans from many banks to work on the same projects This causes difficulty for the banks Table 4-8 The Credit Risk Be Caused From the Weakness of Risk Management of Bank (3) Std N Mean Deviation 15/ Bank regulations 100 2.06 736 16/ The loan procedure of bank 100 2.07 769 24/ The lack of collateral information 100 2.20 791 25/ The difficulty of handling of collateral 100 2.56 608 26/ The inspection of collateral 100 2.24 767 100 2.31 720 2.24 732 29/ The lack of re-checking and supervising Average 33 As showing above, two factors “Bank regulations” and “The loan procedure of bank” has the value mean at 2.06 - 2.07 to show that the terms and regulations of the banks are fairly suitable, but there are some problems which the banks need to correct in order to improve the regulations and procedures and limit the worst things The factor "The lack of collateral information", "The difficulty of handling of collateral", "The inspection of collateral" and "The lack of re-checking and supervising" keep Mean value respectively are 2.2 - 2.56 - 2.24 - 2.31, they show the level of lack of inspection and control after loan, if they had happened they were only formal Inspection records did not complete all the content or implementation not in accordance time after loan There were not recommendations to handle of using loans for improper purposes in time Pursuing achievements so the adjustments of the terms of liability, term of deferment, overdue loans are implemented wrongly to show the unreal credit situation Not considering to the quality of guaranteed properties, not finishing the process to offer loans properly, not performing to evaluate the property on time The guaranty properties haven’t been supervised and closely managed, when the banks offer loans, they haven’t evaluated the true value of properties, so that when the banks solved the credit risk problems, some properties are difficult to handle, and liquidation sales get low value Table 4-9 The Credit Risk Be Caused From the Weakness of Risk Management of Bank (4) Std N Mean Deviation 21/ The credit staff is overload of working 100 2.44 686 22/ The working tools can not compatible 100 2.31 734 28/ For archiving the amount of credit target 100 2.25 783 2.33 734 Average 34 The results say that” the factor: “The credit staff is overload of working”, “The working tools cannot compatible” and “For archiving the amount of credit target” have rather close value Mean at 2.44 – 2.31 – 2.25 These figures show that the volume of work is overloaded The branch of Agribank in Bien Hoa city is the one in which the credit tellers have the highest owe surplus in the Agribank system From this problem, each credit teller has to perform overload work leading to losing control the total and the customers to whom he/she is in charge for Besides that, the continuous changes of the human resources among credit tellers in the bank recently, the work pressure on the current credit tellers is very heavy The recruitment new agents is done slowly, while most of the new agents are graduates and have less experiences, so they are not qualified enough to perform their tasks well As a result, the pressure on finishing planned target is heavy too In addition, the supporting tools such as information system doesn’t work well making the credit quality weaker, the credit risk will be high 35 4.3 Comparisons Analysis 4.3.1 Comparing three groups of causes of credit risk among the size of credit deposit Table 4-10 ONE – WAY ANOVA – Descriptives of the Group of Causes among the Size of Credit Deposit N The reasons causing credit risk The credit risk be caused from customers The credit risk be caused from the weakness of risk management of bank Less than 100 billions From 100 to 500 billions Great than 500 billions Total Less than 100 billions From 100 to 500 billions Great than 500 billions Total Less than 100 billions From 100 to 500 billions Great than 500 billions Total Mean Std Deviation Minimum Maximum 13 1.9692 22871 1.60 2.40 36 2.4833 50228 1.60 3.00 51 2.4392 55716 1.20 3.00 100 2.3940 52912 1.20 3.00 13 2.0000 28054 1.50 2.50 36 2.5046 53376 1.33 3.00 51 2.3954 64890 1.50 3.00 100 2.3833 58961 1.33 3.00 13 1.8962 24955 1.45 2.20 36 2.3681 63225 1.35 3.00 51 2.3157 63761 1.40 3.00 100 2.2800 61402 1.35 3.00 Table 4-11 Test of Homogeneity of Variances – among the Size of Credit Deposit for Each Group of Causes Levene Statistic The reasons causing credit risk The credit risk be caused from customers The credit risk be caused from the weakness of risk management of bank Note: *p< 05; **p< 01 and ***p < 001 36 df1 df2 Sig 9.859 97 000*** 30.295 97 000*** 29.269 97 000*** Table 4-11 shows that the Levene Statistics show that the different the size of credit deposit are not homogeneous for the group of causes They should adopt the Post Hoc statistic test indicated for homogeneity not assumed if the means have significant differences Table 4-12 ANOVA Sum of Squares The reasons causing credit risk Between Groups Within Groups Total The credit risk be Between caused from customers Groups Within Groups Total The credit risk be Between caused from the Groups weakness of risk management of bank Within Groups Total Mean Square df 2.737 1.369 24.979 27.716 97 99 258 2.447 1.224 31.969 34.417 97 99 330 2.259 1.130 35.066 97 362 37.325 99 F Sig 5.314 006** 3.713 028* 3.125 048* Note: *p< 05; **p< 01 and ***p < 001 Table 4-12 displays that, between groups differences for the reasons causing credit risk, the credit risk is caused from customers and the credit risk be caused from the weakness of risk management of bank are significant (p < 0.05) According to SPSS theory, for the group of causes of the reasons causing credit risk, the credit risk is caused from customers and the credit risk be caused from the weakness of risk management of bank using Games – Howell Post Hoc Test The following are the Post Hoc Test for these groups of causes 37 Games-Howell Post Hoc Test (The reasons causing credit risk) Table 4-13 ANOVA The reasons causing credit risk Sum of Squares Between Groups 2.737 Within Groups 24.979 Total 27.716 Note: *p< 05; **p< 01 and ***p < 001 df 97 99 Mean Square 1.369 258 F 5.314 Sig .006** Post Hoc Tests Table 4-14 Multiple Comparisons Dependent Variable: The reasons causing credit risk Games-Howell (I) Credit deposit Less than 100 billions From 100 to 500 billions Great than 500 billions (J) Credit deposit From 100 to 500 billions Great than 500 billions Less than 100 billions Great than 500 billions Less than 100 billions From 100 to 500 billions Note: *p< 05; **p< 01 and ***p < 001 Mean Difference (I-J) Std Error Sig -.51410(*) 10503 000*** -.46998(*) 10055 000*** 51410(*) 10503 000*** 04412 11443 921 46998(*) 10055 000*** -.04412 11443 921 Table 4-14 indicates that, the Sig (p=0.000) show that there is a statistically significant difference between Less than 100 billions and From 100 to 500 billions, and the Sig (p=0.000) also displays that there is significant difference between Less than 100 billions and Great than 500 billions 38 Games-Howell Post Hoc Test (The credit risk be caused from customers) Table 4-15 ANOVA The credit risk be caused from customers Sum of Squares Between Groups 2.447 Within Groups 31.969 Total 34.417 Note: *p< 05; **p< 01 and ***p < 001 df 97 99 Mean Square 1.224 330 F 3.713 Sig .028* Post Hoc Tests Table 4-16 Multiple Comparisons Dependent Variable: The credit risk be caused from customers Games-Howell Mean (I) Credit deposit (J) Credit deposit Difference (I-J) Std Error Less than 100 From 100 to 500 -.50463(*) 11819 billions billions Great than 500 -.39542(*) 11963 billions From 100 to 500 Less than 100 50463(*) 11819 billions billions Great than 500 10920 12716 billions Great than 500 Less than 100 39542(*) 11963 billions billions From 100 to 500 -.10920 12716 billions Note: *p< 05; **p< 01 and ***p < 001 Sig .000*** 005** 000*** 668 005** 668 Table 4-16 indicates that, the Sig (p=0.000) show that there is a statistically significant difference between Less than 100 billions and From 100 to 500 billions, and the Sig (p=0.005) also displays that there is significant difference between Less than 100 billions and Great than 500 billions 39 Games-Howell Post Hoc Test (The credit risk be caused from the weakness of risk management of bank) Table 4-17 ANOVA The credit risk be caused from the weakness of risk management of bank Sum of Mean Squares df Square F Between Groups 2.259 1.130 3.125 Within Groups 35.066 97 362 Total 37.325 99 Note: *p< 05; **p< 01 and ***p < 001 Sig .048* Post Hoc Tests Table 4-18 Multiple Comparisons Dependent Variable: The credit risk be caused from the weakness of risk management of bank Games-Howell Mean Std (I) Credit deposit (J) Credit deposit Difference (I-J) Error Sig Less than 100 billions From 100 to 500 -.47190(*) 12607 001*** billions Great than 500 -.41953(*) 11297 001*** billions From 100 to 500 Less than 100 47190(*) 12607 001*** billions billions Great than 500 05237 13811 924 billions Great than 500 Less than 100 41953(*) 11297 001*** billions billions From 100 to 500 -.05237 13811 924 billions Note: *p< 05; **p< 01 and ***p < 001 Table 4-18 indicates that, the Sig (p=0.001) show that there is a statistically significant difference between Less than 100 billions and From 100 to 500 billions, and the Sig (p=0.001) also displays that there is significant difference between Less than 100 billions and Great than 500 billions 40 4.3.2 Comparing three groups of causes of credit risk among working experience in bank Table 4-19 ONE – WAY ANOVA – Descriptives of the Group of Causes among Working Experience in Bank The reasons causing credit risk The credit risk be caused from customers The credit risk be caused from the weakness of risk management of bank Less than years From to years Great than years Total Less than years From to years Great than years Total Less than years From to years Great than years Total Std Deviation Minimum Maximum N Mean 46 2.1609 44745 1.20 3.00 23 2.4696 61970 1.20 3.00 31 2.6839 40914 1.40 3.00 100 2.3940 52912 1.20 3.00 46 2.1159 45389 1.50 3.00 23 2.5145 63943 1.33 3.00 31 2.6828 56822 1.50 3.00 100 2.3833 58961 1.33 3.00 46 1.9554 49128 1.35 3.00 23 2.4283 63815 1.55 3.00 31 2.6516 51226 1.60 3.00 100 2.2800 61402 1.35 3.00 Table 4-20 Test of Homogeneity of Variances – Among Working Experience in Bank for Each Group of Causes The reasons causing credit risk The credit risk be caused from customers The credit risk be caused from the weakness of risk management of bank Note: *p< 05; **p< 01 and ***p < 001 Levene Statistic df1 df2 7.119 97 001*** 5.809 97 004** 6.139 97 003** 41 Sig Table 4-20 shows that the Levene Statistics show that the different Working experience in bank are not homogeneous for the group of causes They should adopt the Post Hoc statistic test indicated for homogeneity not assumed if the means have significant differences Table 4-21 ANOVA Sum of Squares The reasons causing credit risk The credit risk be caused from customers The credit risk be caused from the weakness of risk management of bank Between Groups Within Groups Total Between Groups Within Groups Total Between Groups Within Groups Total df 5.236 22.480 97 27.716 99 6.465 27.952 97 34.417 99 9.632 27.693 97 37.325 99 Mean Square F 2.618 11.297 Sig .000*** 232 3.232 11.217 000*** 288 4.816 16.870 000*** 285 Note: *p< 05; **p< 01 and ***p < 001 Table 4-21 displays that, between groups differences for the reasons causing credit risk, the credit risk is caused from customers and the credit risk be caused from the weakness of risk management of bank are significant (p < 0.05) According to SPSS theory, for the group of causes of the reasons causing credit risk, the credit risk is caused from customers and the credit risk be caused from the weakness of risk management of bank using Games – Howell Post Hoc Test The following are the Post Hoc Test for these groups of causes 42 Games-Howell Post Hoc Test (The reasons causing credit risk) Table 4-22 ANOVA The reasons causing credit risk Sum of Squares Between Groups 5.236 Within Groups 22.480 Total 27.716 Note: *p< 05; **p< 01 and ***p < 001 df 97 99 Mean Square 2.618 232 F Sig 11.297 000*** Post Hoc Tests Table 4-23 Multiple Comparisons Dependent Variable: The reasons causing credit risk Games-Howell (J) Working Mean Std (I) Working experience experience Difference (I-J) Error Sig Less than years From to years -.30870 14508 099 Great than years -.52300(*) 09875 000*** From to years Less than years 30870 14508 099 Great than years -.21431 14865 331 Great than years Less than years 52300(*) 09875 000*** From to years 21431 14865 331 Note: *p< 05; **p< 01 and ***p < 001 Table 4-23 indicates that, the Sig (p=0.099) show that there is not a statistically significant difference between Less than years and From to years, and the Sig (p=0.000) also displays that there is significant difference between Less than years and Great than years Games-Howell Post Hoc Test (The credit risk be caused from customers) Table 4-24 ANOVA The credit risk be caused from customers Sum of Squares Between Groups 6.465 Within Groups 27.952 Total 34.417 Note: *p< 05; **p< 01 and ***p < 001 df 97 99 43 Mean Square 3.232 288 F 11.217 Sig .000*** Post Hoc Tests Table 4-25 Multiple Comparisons Dependent Variable: The credit risk be caused from customers Games-Howell (I) Working (J) Working Mean Difference experience experience (I-J) Less than years From to years -.39855(*) Great than years -.56685(*) From to years Less than years 39855(*) Great than years -.16830 Great than years Less than years 56685(*) From to years 16830 Note: *p< 05; **p< 01 and ***p < 001 Std Error 14918 12204 14918 16791 12204 16791 Sig .030* 000*** 030* 579 000*** 579 Table 4-25 indicates that, the Sig (p=0.030) show that there is a statistically significant difference between Less than years and From to years, and the Sig (p=0.000) also displays that there is significant difference between Less than years and Great than years Games-Howell Post Hoc Test (The credit risk be caused from the weakness of risk management of bank) Table 4-26 ANOVA The credit risk be caused from the weakness of risk management of bank Mean Sum of Squares df Square F Between Groups 9.632 4.816 16.870 Within Groups 27.693 97 285 Total 37.325 99 Note: *p< 05; **p< 01 and ***p < 001 44 Sig .000*** Post Hoc Tests Table 4-27 Multiple Comparisons Dependent Variable: The credit risk be caused from the weakness of risk management of bank Games-Howell (I) Working (J) Working Mean Difference Std experience experience (I-J) Error Sig Less than years From to years -.47283(*) 15150 010** Great than years -.69618(*) 11710 000*** From to years Less than years 47283(*) 15150 010** Great than years -.22335 16177 360 Great than years Less than years 69618(*) 11710 000*** From to years 22335 16177 360 Note: *p< 05; **p< 01 and ***p < 001 Table 4-27 indicates that, the Sig (p=0.010) show that there is a statistically significant difference between Less than years and From to years, and the Sig (p=0.000) also displays that there is significant difference between Less than years and Great than years 45 4.3.3 Comparing three groups of causes of credit risk among professional degree Table 4-28 ONE – WAY ANOVA – Descriptives of the Group of Causes among Professional Degree N The reasons causing credit risk The credit risk be caused from customers The credit risk be caused from the weakness of risk management of bank Intermediate vocational College University Graduated University Total Intermediate vocational College University Graduated University Total Intermediate vocational College University Graduated University Total Mean Std Deviation Minimum Maximum 1.8000 28284 1.60 2.00 2.4000 87 2.3793 54306 2.40 1.20 2.40 3.00 10 2.6400 32387 1.80 2.80 100 2.3940 52912 1.20 3.00 2.0000 70711 1.50 2.50 2.0000 87 2.3525 58592 2.00 1.33 2.00 3.00 10 2.7667 51640 1.50 3.00 100 2.3833 58961 1.33 3.00 1.8000 49497 1.45 2.15 1.7500 87 2.2506 61040 1.75 1.35 1.75 3.00 10 2.6850 53544 1.60 3.00 100 2.2800 61402 1.35 3.00 46 Table 4-29 Test of Homogeneity of Variances – Among Professional Degree for Each Group of Causes Levene Statistic The reasons causing credit risk df1 5.978(a) df2 Sig 96 004** The credit risk be caused from 2.491(b) 96 088 customers The credit risk be caused from the weakness of risk 2.852(c) 96 063 management of bank a Groups with only one case are ignored in computing the test of homogeneity of variance for The reasons causing credit risk b Groups with only one case are ignored in computing the test of homogeneity of variance for The credit risk be caused from customers c Groups with only one case are ignored in computing the test of homogeneity of variance for The credit risk be caused from the weakness of risk management of bank Note: *p< 05; **p< 01 and ***p < 001 Table 4-30 ANOVA Sum of Squares The reasons causing credit risk Between Groups Within Groups Total The credit risk be Between caused from customers Groups Within Groups Total The credit risk be Between caused from the Groups weakness of risk management of bank Within Groups Total df Mean Square 1.330 443 26.387 27.716 96 99 275 1.993 664 32.424 34.417 96 99 338 2.457 819 34.868 96 363 37.325 99 F Sig 1.612 192 1.967 124 2.255 087 Table 4-30 displays that, between groups differences for the reasons causing credit risk, the credit risk is caused from customers and the credit risk be caused from the weakness of risk management of bank are not significant (p > 0.05) 47 4.4 Multiple Regression Table 4-31 Correlations Matrix of the Predictors and Predicted Variable Credit deposit Pearson Correlation Credit deposit The reasons causing credit risk The credit risk be caused from customers The credit risk be caused from the weakness of risk management of bank Sig (1-tailed) Credit deposit The reasons causing credit risk The credit risk be caused from customers The credit risk be caused from the weakness of risk management of bank N Credit deposit The reasons causing credit risk The credit risk be caused from customers The credit risk be caused from the weakness of risk management of bank Note: *p< 05; **p< 01 and ***p < 001 The credit risk be The The credit caused from reasons risk be the weakness causing caused of risk credit from management risk customers of bank 1.000 211 136 158 211 1.000 848 841 136 848 1.000 905 158 841 905 1.000 017* 089 058 017* 000*** 000*** 089 000*** 000*** 058 000*** 000*** 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 Table 4-31 shows that from the predictors, does not show level correlation with the predicted credit deposit Among the predictor, the reasons causing credit risk, the credit risk is caused from customers and the credit risk be caused from the weakness of risk management of bank show middle level correlation which means that there may be collinearity relationship existed between them 48 Table 4-32 Model Summary for Credit Deposit Regression Adjusted R Std Error of the Model R R Square Square Estimate 228(a) 052 022 700 a Predictors: (Constant), The credit risk be caused from the weakness of risk management of bank , The reasons causing credit risk, The credit risk be caused from customers Table 4-32 shows the overall correlation coefficient between the predictors and criterion variable in this model is 228 while the regressional coefficient of determinant R Square is 052, which indicates that 5.2% of the credit deposit can be explained by the predictors The overall model of fit in Table 4-33 is shown there is not a statistically significant Table 4-33 ANOVA(b) of the Credit Deposit Model Sum of Mean Model Squares df Square F Sig Regression 2.580 860 1.757 161(a) Residual 46.980 96 489 Total 49.560 99 a Predictors: (Constant), The credit risk be caused from the weakness of risk management of bank , The reasons causing credit risk, The credit risk be caused from customers b Dependent Variable: Credit deposit Table 4-34 Coefficients(a) of Predictors for Credit Deposit Model Unstandardized Standardized Coefficients Coefficients t Sig Std B Error Beta 1.737 329 5.274 000*** (Constant) The reasons causing 436 credit risk The credit risk be caused from -.240 customers The credit risk be caused from the 075 weakness of risk management of bank a Dependent Variable: Credit deposit Note: *p< 05; **p< 01 and ***p < 001 49 265 326 1.643 104 303 -.200 -.791 431 285 065 263 793 Table 4-34 shows that does not have significant coefficient of determinant exclude Concstant term.The correlation matrix in table 4-31 also supports this result 50 ... causing credit risk The credit risk be caused from customers The credit risk be caused from the weakness of risk management of bank Note: *p< 05; **p< 01 and ***p < 001 The credit risk be The The credit. .. causes of credit risk among the size of credit deposit Table 4- 10 ONE – WAY ANOVA – Descriptives of the Group of Causes among the Size of Credit Deposit N The reasons causing credit risk The credit. .. Post Hoc Test (The credit risk be caused from the weakness of risk management of bank) Table 4- 17 ANOVA The credit risk be caused from the weakness of risk management of bank Sum of Mean Squares