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1 INTRODUCTION 1995; Xiao et al., 1995; Zelizer, 1994) Age and other demographical features are also significant determinants of credit risk (Agarwal et al., 2011; Livingstone & Lunt, 1992; Tokunaga, 1993) Resident time, monthly savings, education background, accommodation ownership, occupational risk, and working time are the most widely explored dimensions among researchers (Agarwal et al., 2011; Dufhues, Buchenrieder, Quoc, & Munkung, 2011; Livingstone & Lunt, 1992; Ojiako & Ogbukwa, 2012; Hoàng Thị Kim Diễm, 2012; Lê Văn Triết, 2010) Although there exists a number of studies on the payment ability and bankruptcy possibility of personal clients, no one has paid attention to Co-operative Bank of Vietnam Moreover, little effort has been put into assessing different measurements to figure out the appropriate one to forcast credit risk related to personal customers at Co-operative Bank of Vietnam Therefore, the authors implement this research “Determinants of default possibility of individual customers at Co-operative Bank of Vietnam” Objectives of the study Firstly, the dissertation systematizes theoretical basis related to individual customer credit activities in Vietnam Secondly, the dissertation synthesizes and analyzes the previous studies in order to refer to and present the research model of the dissertation Thirdly, the dissertation builds a research model to evaluate factors affecting the solvency / default of individual customers at Co-operative Bank of Vietnam Fourthly, the dissertation analyzes and compares the estimation models to predict the default of debts to find a reference forecast model for credit activities of Co-operative banks of Vietnam Fifth, the research findings also provide some recommendations to help reduce the likelihood of customers defaulting at Co-operative Bank of Vietnams as well as improve the efficiency of individual customer credit operations Research questions Firstly, what factors affect the default of individual customers at Co-operative Bank of Vietnam? Second, is there any difference in the influence of factors in different estimation models in the default of individual customers? Third, which forecasting model best predicts the customer default with research data? Fourthly, what are the recommendations to reduce the default of customers as well as improve the efficiency of individual customer credit activities in Co-operative Bank of Vietnam? Object and scope of the study 4.1.1 The object of study The object of the study is to assess the impact of factors affecting the default of individual customers Background Lending by banks and other financial intermediaries aids individuals as well as firms in keeping their businesses in operation Lending also helps reduce poverty and other micro-activities (Mensah, 2013) By extending credit, these financial intermediaries however, have to face with chances of default from borrowers due to insolvency (Westley, 2005) Overdue loans have serious financial and non-financial implications for the operation of the microfinance institutions in which the repayment schedule is a crucial factor (Mensah, 2013) To protect against financial risks, reduce non-performing loans, and increase the ability of banks to identify individual-level risks, a system of risk warning is imperative As the proportion of individual lending in banking business is rapidly increasing, it is particularly relevant to predict the risk of default on personal loans (Zhang, 2011) For banking sector, the most important function is to mobilize capital or funds to generate profit, in which lending to customers is the biggest profit making activity (Le Van Te, 2009) This also comes with the highest risk for commercial banks Since Vietnam officially transitioned to a socialist-oriented market economy, the banking system has been constantly growing and has achieved certain achievements, but in that process encountered plenty of failures causing heavy losses to the economy Risk assessment, therefore, should be considered a first step or prerequisite in lending process According to a financial institution's report, the non-performing loans (NPLs) ratio of 22 Vietnamese banks in 2019 reached VND 78,5 trillion, which increased by 41% compared to of that of 2018 In particular, most banks tended to have higher non-performing loans compared to the previous year The biggest increases in the non-performing loan ratio were recorded at Tien Phong Bank and Ocean Bank, with the growth rates of NPL at 43,39% and 80,10% respectively The other banks all reported the rates of less than 40% (Corporate Financial Statements, 2020) Co-operative Bank of Vietnam is one of the banks that have set goals towards reducing non-performing loans in accordance to the regulations of State Banks Accordingly, the objective of reducing non-performing loans down to below 3% at the end of 2020 has brought certain challenges to Co-operative Bank of Vietnam Therefore, the bank itself needs to be approactive about credit quality improvement as well as the establishment of an optimal risk assessment system Therefore, identifying determinants of credit risk is cruicial for obtaining the set goals Worldwide, many studies have aimed at exploring and justifying the determining factors of bankruptcy possibility of banks’ personal clients Some indicates that clients with low income or from low classes tend to exploit loans less effectively and are less likely to manage to pay in comparisions to the weathier and upper-classed counterparts (Cox & Jappelli, 1993; Mathews & Slocum, 1969) Besides, gender is agrued to be a contributor to customers’ ability to pay (Lea et al., 4.1.2 Research scope Research data on the status of individual customer credit operations, the study variables in the model are collected until the end of 2019 By the end of 2019, individual bank credit history to the maturity date has been completed Customers have a history of borrowing in both short-term, medium-term and long-term terms since 2014 Therefore, data on repayment and non-repayment is collected at the end of 2019 Data collected from Co-operative Bank of Vietnam Research methods and data 5.1.1 Research Methods The dissertation uses both qualitative and quantitative research methods In qualitative research, the researcher conducted pre-model interviews about the factors affecting the customer's debt repayment At the same time, after obtaining the results of quantitative research, the PhD student also conducts interviews in explaining the results as well as recommendations in the document evaluation as well as assisting clients in the loan process business activities Quantitative research method is used by the PhD student during testing and finding out the factors that affect the default of individual customers at Co-operative Bank of Vietnam In addition, the default forecasting models for customers such as artificial neural networks (ANN) and Random Forest are used to compare with traditional estimation models such as Logistic and Probit 5.1.2 Data (1) Secondary information / data: collected through bank reporting (2) Primary data: collected based on Co-operative Bank of Vietnam database New contributions of the dissertation 6.1.1 Contribute to the theory Through qualitative and quantitative research methods, the study will provide the model as well as the determinants to the default of individual customers Besides, the dissertation also compares forecasting models of default Different techniques will produce different results The best model will be used to evaluate the factors affecting repayment In the author's research environment, the optimal default prediction model will be given for future reference 6.1.2 Contribute to practice The results of the dissertation will help banks as well as credit organization can refer to the appraisal of loan documents for individual customers in their banks At the same time, from the achieved results, the dissertation implements a credit rating model for individual customers to suggest to the Co-operative Bank of Vietnam The structure of the dissertation Chapter 1: Overview of research and theoretical basis of factors affecting the default of individual customers Chapter 2: Research methodology Chapter 3: Research results Chapter 4: Solutions and recommendations CHAPTER OVERVIEW OF RESEARCH AND THEORETICAL BASIS OF FACTORS AFFECTING THE DEFAULT OF INDIVIDUAL CUSTOMERS 21.1 Overview 1.1.1 The foreign studies on factors affecting default Abid et al (2018) conducted a comparative model of customer default forecast through Logit model and discriminant analysis (DA) model to distinguish between individuals with good and bad credit ratings The authors found that the LR model yielded a good 99% classification rate in predicting customer types, the DA method (where the good classification rate was only 68,49%, resulting in an error rate of significantly high, i.e 31,51%) (Abid et al., 2018) The results show that Logistic model has better predictability than DA model Mensah (2013) conducted a study about credit default when borrowing from banks Research results through regression analysis show that there is no significant relationship between default and loan repayment schedule Ojiaki & Ogbukwa's study of the ability of farmers to repay debt when borrowing from a state bank in Nigeria The logit model regression method used has shown results: there are only factors that have a real impact on the ability to repay: (1) Household size has the opposite effect on debt repayment; (2) The size of land use for agriculture has a positive effect on farmers' ability to pay debts and (3) The amount of loans has a positive impact on a household's ability to pay debts A study in Southeast Asian countries by Dufhues et al (2011) on credit repayment in Thailand and Vietnam For Vietnam, the research results of 198 surveyed households have found that there are two influential factors: (1) The loan of the household (2) ethnicity of the household head has a positive impact on ability to pay credit debts of households There are also other studies such as Kocenda & Vojtek (2011) using retail banking data from the Czech Republic, Peter & Peter (2006) estimating the likelihood of default related to income and other factors from Australian data 1.1.2 Domestic studies on factors affecting customers' ability to default Research by Dao Thi Thanh Binh (2019) on the construction of a credit scoring model for individual consumer loans in Vietnam The author uses the method of FICO system taking into account the situation of Vietnam Research by Pham Thi Thu Tra & Robert Lensink (2008) The authors found that small households with mortgages and / or guarantees mainly borrowed formally and semi-formally while female contractors, large households and borrowers did not need mortgages or guarantors mainly rely on informal loans The study of Duong Thi Thanh Hai (2014) on the characteristics and factors affecting personal credit at the current commercial banking system in Vietnam The results show that the characteristics of individual credit include: small loan size but large number of loans; individual credits with inflexible lending rates; Personal credit has the largest cost on the bank's credit portfolio; Personal credit has a high level of risk There are also many other studies such as Le Van Triet's (2010) research on improving the personal credit rating system of Asia Commercial Bank; Research by Hoang Thi Kim Diem (2012) with the bank for investment and development of Saigon South branch; Research by Nguyen Quoc Nghi (2013) conducted the assessment of factors affecting the ability of Agribank to repay loans on time; Research by Dinh Thi Huyen Thanh & Kleimeier (2007) Using the data set of 56,037 observations of a Vietnamese commercial bank 1.1.3 The estimation studies predict the risk of default of customers using the classification tree 1.2.3 The role of bank credit Firstly, bank credit ensures the production process takes place on a regular and continuous basis; secondly, this is a strong lever to promote the process of capital accumulation and concentration; thirdly, bank credit helps promote the equilibrium rate of profit among industries in the economy and is an important tool in organizing people's life 1.2.4 Bank credit classification Classification by time of credit extension; Classification by capital use purpose; Classification by the mode of repayment; Classification by the level of assurance; Classification by subjects of credit extension; Classification by credit origin; Classification by economic sector 1.3 Personal customer credit 1.3.1 Individual customer credit Based on the definition of "bank credit", science and technology credit can be understood as a form of credit in which a commercial bank acts as a transferor of its right to use capital to science and technology for the most used time repayment of both principal and interest (Nguyen Dang Don 2013) 1.3.2 Personal customer credit policy The science and technology credit policy at banks will depend on the objectives and operational policies and there will always be changes to suit the socio-economic conditions as well as ensure the development, sustainable and lucrative for the bank 1.3.3 Individual customer credit process The study of credit risk prediction following the approach of discriminant analysis model and neural network (ANN) in Tunisian of Khemakhem & Boujelbene (2015) The results obtained were compared with discriminant analysis The authors have shown that the artificial neural network (ANN) technique is more predictable Booth et al (2014) performed research on automated transactions with Random Forest and seasonality The results show that the weighted Random Forest classifications produce superior results in both profitability and predictive accuracy compared to other techniques There are also many other related studies such as Bennell et al (2006) using comprehensive data sets of rating agencies and countries between 1989 and 1999; Finch & Schneider (2007) undertook the study of accuracy classification of artificial neural network (ANN) compared with numerical analysis, logistic regression and classification plants; Pacelli & Azzollini (2011) research on the use of artificial neural networks for credit risk management in Italy; Zang (2011) implemented using ANN neural neural network model to predict the default of customers in commercial banks in China 1.2 Credit issues of the bank 1.2.1 Banking credit concept Bank credit is the relationship of asset transfer between the bank and other economic entities According to Article 20 of the Law on Credit Institutions, 2010, the regulation: “Credit extension is an agreement by a credit institution to allow customers to use an amount of money with the principle of repayment by lending, discounting operations discount, financial leasing, bank guarantee and other operations ” 1.2.2 Characteristics of bank credit Firstly, the subject in the credit relationship; Secondly, the subjects of commercial credit transactions include lending with money and leasing real estate or real estate; Thirdly, the capital transfer is based on "belief" and on the principle of unconditional repayment in a certain period of time; Fourth, the repayment value must be greater than the value at the time of lending; Fifthly, banking credit activities pose many risks Contact and guide customers to prepare loan documents; Appraisal of loan conditions; Determine the method of lending, consider the ability of the capital, interest rates; Loan appraisal; Approve the loan; Disbursement; Checking and supervising the loan; Collecting debts, principals and handling arising; Liquidation of credit contracts, loan security contracts, collateral security; Keep credit and loan guarantee records 1.4 Credit risk Credit risk is the inherent potential loss generated when granting credit to customers Any credit granted must comply with the following three basic principles: (1) The credit must be used for the right purpose and effectively; (2) The credit must be secured by assets; (3) The credit must be repaid both principal and interest within the committed term 1.5 Effect of credit default In general, the main impact of non-performance loan (NPLs) on banks is that the increase in NPLs limits the financial growth of banks (Karim et al., 2010; Kuo et al., 2010) 1.6 Credit rating activities in banks 1.6.1 Concept The definition of a credit rating is to make a statement about the level of creditworthiness of a financial liability or to assess the level of credit risk depending on factors including the ability to meet financial commitments, the ability to default when economic conditions change, the consciousness and willingness to repay the borrower The credit rating system is used to assess the financial responsibility of both corporate and S&T customers Within the scope of this dissertation, the author focuses on analysis and research on credit ratings for science and technology group 1.6.2 The role of credit rating Secondly, the credit scoring results are not a strong basis to help the bank make a decision to grant credit limits to customers The exact prediction level of customers' defaults is not high, so it can not eliminate the confusion, there are cases when customers are rated at a high, reliable and high level of credit Low risk but in fact not able to repay Banks will control customers' creditworthiness, evaluate the effectiveness of their loan portfolios by monitoring changes in outstanding loans and loan classification of customers thanks to the credit rating system 1.6.3 Principles of credit rating activities Firstly, credit analysis is based on the awareness and willingness to repay the borrower for each loan Secondly, assess long-term risk based on the impact of the business cycle as well as the trend and the possibility of future default Thirdly, a comprehensive and unified risk assessment is based on a credit scoring system and rating symbols Fourthly, the data collection for use in credit rating model should be done objectively and flexibly 1.6.4 Credit rating process Firstly, collect relevant information Secondly, analyze the information collected using models to draw conclusions about the credit rating of science and technology Third, monitor the credit status of the customer is rated appropriate adjustment 1.6.5 Several credit rating models FICO's individual credit score model CreditKarma credit score model Credit Sesame credit score model VantageScore credit score model Kleimeier's credit score model 1.4.6 Credit rating model at the bank The credit rating system at Vietnamese commercial banks currently uses the grading method The number of points customers achieve is the total score of financial and non-financial indicators with a certain proportion 1.4.7 A limited number of individual customer credit ratings today The practical model still has limitations that need to be overcome: Firstly, the criteria presented in the current credit rating model are still qualitative, the quantitative factors are still low because based on the method of experience and experts, and there have been no new updates for with quantitative statistical methods Thirdly, the actual credit rating model has encountered problems that are difficult to detect fraudulent acts of customers The assessment of these behaviors is only based on the experience of the credit officer in customer contact and information gathering Fourth, the current scoring model only provides the value of creditworthiness of customers at the time of credit extension, but not predictive for the future 1.7 Factors affecting the default of individual customers 1.7.1 Personal information of the customer The personal information about a customer is the internal information of that customer The study of these factors helps banks assess the overall overview of the customer, the basic ability of the customer to meet the conditions that the bank requires, the level of reliability in the customer The fact that customers make commitments with the bank and is also a source of information has a great influence on the decision of the bank about whether or not to provide credit to customers Factors in this information group include: age, gender, marital status, education level, occupation, current position in the job, judicial record 1.7.2 Factors of living conditions of customer Information about the living conditions of science and technology reflects the interaction of that customer with the society, thereby helping the bank to assess the impact of the external environment on its financial capacity as well like the perceived behavior of that customer This group of information includes factors such as: household size, number of dependents, classification of place of residence, characteristics of place of residence, stability of accommodation, home ownership, and ownership of other types of valuable properties 1.7.3 Financial factors of the customer Analyzing customers’ financial information and financial relationships is an important task for banks, which is crucial to assessing the likelihood of customers' default, affecting credit rating of customer as well as whether the bank makes a loan decision or not The financial indicators of the customers are the banks 1.7.4 Customer behavior factor Factors in the group are analyzed such as: relationship with the bank, the number and type of banking services that the customer is using, the number of loans, repayment time, time of the loan application procedure, calendar loan history and repayment 9 10 CHAPTER RESEARCH METHODS 2.3 Design 2.3.1 Sample Research data was collected from the database of Co-operative Bank of Vietnam Vietnam Historical data about whether or not individual defaults of individual customers at the bank will be used The sample collected 5498 customers at Co-operative Bank of Vietnam With a sample size of 5498, we can guarantee the reliability of the minimum number of samples when analyzing multivariate data 2.3.2 Data collection With the research variables, the author proceeds to send to the bank branch in charge to ask for information on the situation of debt repayment of individual customers Each bank is collected by NCS from about 100-500 customers In particular, the number of customers who are able to pay the debt and default (not to pay the debt) is also collected in a balanced manner by the PhD student to analyze without bias in the sample 2.4 Data analysis method 2.4.1 Data description 2.4.2 Correlations 2.4.3 Models of analysis and prediction of default of science and technology 2.4.3.1 Logit model 2.1 Research process With the content of the dissertation, the research process is presented as follows: The research gap Define research objectives Relevant theories, factors affecting the default of individual customers Theorical Research model The research variables obtained from the previous model, factors from qualitative interviews were included in the research model 2.4.3.2 Probit model 2.4.3.3 Discriminant analysis Data analysis - Logit model ANN, Random forest Check results on new data samples - So sánh mơ hình Complete the report 2.2 Research models and hypotheses From previous studies, the author offers the following research model: In which: DLi: the dependent variable on default Xi: Independent variables can affect the default DLi 2.4.3.4 Predictive model of artificial neuron network (ANN) 2.4.3.4 Forecasting model by Random Forest 11 12 CHAPTER RESULTS 3.1 Co-operative Bank of Vietnam 3.1.1 Introduction to Co-operative Bank of Vietnam The Co-operative Bank of Vietnam, formerly known as the Central People's Credit Fund, was established on August 5, 1995 and converted into a Co-operative Bank of Vietnam in accordance with License No.166/GP-NHNN dated June 4, 1995/2013 of the Governor of the State Bank of Vietnam The full name in Vietnamese: Co-operative Bank of Vietnam of Vietnam 3.1.2 Capital use activities of Co-operative Bank of Vietnams Capital of Co-operative Bank of Vietnams from 2016 to 2017 increased by 2828 billion dong, equivalent to 10,49% In which, equity increased by 63 billion dong, equivalent to 1,76%; The equity is mainly raised from funds but not from charter capital 3.2 Situation on individuals who borrow money at Co-operative Bank of Vietnams according to the research sample Descriptive statistics of the study variables continuously indicate that the average education of subjects is 21 years of schooling The largest one is 36 years and the smallest is 12 years The standard deviation of 6,1 indicates a relatively large level of educational inequality Next, the age of science and technology loans borrowed on average is 32 years old The largest one is 53 years old and the youngest is 20 years old In terms of household size, the average number of borrowers in the family is 5, the largest is and the smallest is The average number of dependents in the family is 3, the largest is and the smallest is person The average loan amount is 551 million, the smallest is 100 million and the largest is 10 billion Average working time and experience of science and technology are 14 years, the minimum is year of experience and the maximum is 30 years of working experience The ratio of principal and interest payment to the average income is 0,397, equivalent to 39%, of which the largest is 60% and the smallest is 20% 3.3 The results of analyzing the factors affecting the default of individual customers 3.3.1 Logistic regression results Table Logistic regression results for customers (Intercept) Education_ Intermediate College/University Estimate Std Error z value p-value -0,0898 0,1369 -0,6550 0,512187 Gender 0,8570 0,1421 6,0300 1,64e-09 *** Marriage -0,6551 0,1146 -5,7160 1,09e-08 *** Judicial Records 0,6929 0,2229 3,1090 0,001879 ** Business property -4,9570 0,1703 -29,1140 < 2e-16 *** Age -0,0729 0,0089 -8,1810 2,82e-16 *** Household size -0,2300 0,0966 -2,3800 0,017301 ** Number of dependents 0,0528 0,0707 0,7470 0,455127 Graduate university Loan 0,0000 0,0003 0,0280 0,977847 Job 1,7440 1,0010 1,7420 0,081507 Location _TruongBoPhan -0,0234 0,2071 -0,1130 0,910178 0,5550 0,3011 1,8430 0,065263* Work time Nhanvien -0,0226 0,0105 -2,1470 0,031771 ** Type of work -0,5450 0,1414 -3,8540 0,000116 *** Income -0,1218 0,0107 -11,3770 < 2e-16 *** Term_Mid-term -1,3230 0,2322 -5,6960 1,23e-08 *** -0,8374 0,2398 -3,4920 0,000480 *** -0,0890 0,1550 -0,5740 0,565849 -1,4100 0,2621 -5,3780 7,54e-08 *** Long-term Status_ Slow time Slow times or more Purpose_Use the right purpose 0,2248 0,1134 1,9830 0,047378 ** Diversify the profession 0,5236 0,1137 4,6050 4,12e-06 *** Special properties are real estate -1,7930 0,1247 -14,3750 < 2e-16 *** Monthly payment rate 0,4287 0,4787 0,8960 0,370498 Life insurance -1,0980 0,2286 -4,8050 1,55e-06 *** Observations Estimate Std Error z value p-value 14,2000 0,8484 16,7370 < 2e-16 *** 0,2253 0,1951 1,1550 0,248272 -0,6597 0,1915 -0,8339 0,71345 5,498 Note:*p