INTRODUCTION
Background
The world has experienced remarkable numbers of banking and financial crises during the last thirty years Caprio and Klingebiel (1997) have identified 112 systemic banking crises
Since the late 1970s, 1 in 93 countries has experienced significant banking crises, with Demirguc-Kunt and Detragiache (1998) identifying 30 major instances from the early 1980s onward While many of these crises occurred in developing nations, Vietnam also faced similar challenges in the late 1980s and early 1990s Notably, most crises were linked to deregulation that led to excessive credit expansion, resulting in rising asset prices and the formation of economic bubbles Eventually, these bubbles burst, causing a sharp decline in asset prices, widespread bankruptcies, non-performing loans, and severe banking crises.
Despite the implementation of numerous reforms, the occurrence of crises has not diminished, primarily due to various political and economic factors This indicates that enhanced risk management alone does not lead to better banking performance Additionally, Jean-Charles Rochet (2008) emphasizes that the success of reforms hinges on the independence and accountability of banking supervisors.
The effectiveness of international banking regulations is hindered by the political and economic interests of national supervisors The recent global financial crisis has revealed inadequacies in risk management within financial institutions, leading to significant bank failures in a volatile market Discussions in media and academia highlight that lapses in risk assessment contributed to the crisis, suggesting that more vigilant oversight could have mitigated its impact Consequently, risk management is now a critical focus, offering valuable lessons for future improvements This evolving landscape has inspired my thesis on the complexities of risk management.
1 Systemic risk is the risk of collapse of an entire system or entire market and not to any one individual entity or component of that system
2 Steigum (1992) and Vihriọlọ (1997) discusses on the Norwegian and Finnish cases
3 Englund P (1999), The Swedish Banking crisis: Roots and Consequences, Oxford review of Economic Policy, Vol 15, no.3, pages 80-97
4 Joe Nocera, “Risk management and financial crisis”, Herald Tribune, January 4, 2009
5 A.E Feldman Associates, Inc., US Consulting firm report “On financial crisis and its effect” http://www.aefeldman.com Accessed February 9,2009
Problem Discussion
The banking sector has grown increasingly complex due to the evolution of financial security markets, leading banks to engage in intricate transactions without fully understanding the associated risks This lack of clarity in risk-bearing responsibilities can result in systemic failures, jeopardizing the economic stability of nations In response, governments intervene to stabilize the economy through regulatory mechanisms aimed at mitigating these risks.
The liberalization trend and economic globalization have significantly transformed the banking system, introducing challenges for domestic banks due to the emergence of investment-capital banks equipped with modern technology and advanced management As financial markets open, banking operations become increasingly complex, intensifying competition and elevating risk levels To achieve profitability, banks must navigate these inherent risks in their operations In light of heightened competition and the integration of financial markets, the banking sector is evolving, necessitating robust reforms to mitigate operational risks.
Credit activities can yield significant profits, but they also carry substantial risks As Vietnamese enterprises face growing pressures from globalization and international integration, the nature of credit risk evolves Consequently, enhancing credit risk management has become a critical challenge for commercial banks, as it is essential for boosting operational efficiency and maintaining competitiveness in today's market.
BIDV Bank, a prominent Joint Stock Commercial Bank in Vietnam, boasts an extensive network across the country and into neighboring regions, with representative offices worldwide The Thanh Do branch, located at 463 Nguyen Van Linh Street in Long Bien District, Hanoi, operates four sub-branches and has significantly contributed to the socio-economic development of Hanoi and the growth of BIDV Despite its achievements, the branch faces challenges in credit activities, including rising overdue and bad debts due to recent economic downturns Consequently, BIDV Thanh Do must enhance its risk management, particularly in credit risk, to ensure system safety and strengthen its competitive capacity in the evolving market.
Despite its achievements, BIDV Bank - Thanh Do Branch faces significant challenges in credit activities, including rising overdue and bad debts due to the economic recession This situation necessitates a heightened focus on risk management, particularly credit risk management, to enhance safety and competitiveness among Joint Stock Commercial Banks (JSCBs) Notably, there has been a lack of research on credit risk management at BIDV Bank - Thanh Do Branch for the period of 2015-2017, highlighting the need for strategies to improve the quality of credit risk management Consequently, the author has chosen to explore the topic "The Credit Risk Management at BIDV Bank – Thanh Do Branch."
Research purposes and tasks
The dissertation focuses on the current situation of credit risk management activities at BIDV – Thanh Do Branch
- Research is about the practical activities of credit risk management in Thanh Do Branch and some features of other typical branches in the BIDV system
- This research is focused in period 2015-2017
- Research on credit risk management was conducted at BIDV Thanh Do branch
The process involves a thorough analysis of the current credit risk management practices at BIDV – Thanh Do branch, identifying key weaknesses in the system, and recommending effective solutions to enhance overall credit risk management.
Delimitation
The research on credit risk management activities is restricted to the Thanh Do Branch of BIDV and lacks comprehensive data from other typical branches within the BIDV system Due to the absence of information in annual reports, this study exclusively analyzes data from the BIDV – Thanh Do branch Consequently, the findings are specific to this one commercial bank and cannot be generalized to other commercial banks in Vietnam.
Disposition
In this chapter, we outline the thesis background and articulate the problem statement, highlighting the motivation behind our research Additionally, we provide a comprehensive literature review and detail the methodology employed The purpose of this thesis is clearly defined, and we also delineate the scope of the study.
FRAMEWORK: In this Chapter, I provide theoritical foundation to my study by presenting relevant literature
METHODOLOGY: In this chapter, I widely describe the HOW part of our study
This chapter outlines the research methodology, including the sampling techniques, data collection methods, and analytical instruments used for data analysis It also provides a detailed description of the regression model applied in the study Finally, the chapter concludes with an assessment of the reliability and validity of the research, along with a discussion of its limitations.
EMPRICAL FINDING AND ANALYSIS: In this chapter, I present overview of
BIDV – Thanh Do branch and the results of my regression model, sollutions and recommendation for the bank
METHODOLOGY
Research approach
This study employs a Logistic Regression model to analyze credit risk in business by examining the correlation matrix of variables, regression outcomes, and the overall relevance of the model The data collection and description provide a comprehensive understanding of the expected variables incorporated into the model, facilitating a clearer explanation of credit risk assessment.
The method of our study is Logistic regression
Logistic regression is a specialized regression model utilized for predicting the probability of a binary outcome, represented by values of 0 and 1 This model leverages independent variables to assess the likelihood of an event occurring, making it a valuable tool in various predictive analytics scenarios.
(1) Probability: The probability that something happens, denoted by P
(2) Odds is the ratio between two probabilities: probability of occurrence and probability does not occur Or more specifically the ratio between success and failure
When we have two dependent variables: Y = 1, Y = 0, and the probability that it happens is denoted by P (Y = 1) = P Statisticians often use a familiar quantity as Odds
Thus, according to the formula, Odds is a function of P Odds> = 0, and Odds will not be determined when P = 1
We have: P is the probability of occurrence of the event, then (1 - P) is the probability of no event occurrence, probability P is measured as follows:
Odds of the above case are:
Taking the e-log of Odds we have the Logistic regression model function:
Meaning: When Xk changes a unit, the probability that Y = 1 (also Pi) will change
The probability change, represented as Pi * (1 - Pi) * βk, is influenced by two key factors Firstly, the sign of the coefficient βk determines the relationship between the variable Xk and the probability of Y = 1; a positive coefficient indicates that an increase in Xk raises the probability, while a negative coefficient suggests the opposite Secondly, the change in probability for Y = 1 is contingent upon the value of Xk, as variations in Xk lead to corresponding increases or decreases in Pi, provided that the probability remains within the bounds of 0 ≤ Pi ≤ 1 Consequently, the marginal effect of the variables is reflected in the probability shift from P0 to P1 with a one-unit change in Xk.
In which, P1 is the probability when Xk is increased to a unit:
From the two equations we have:
P Replace Odd = in (1), we have
The relationship between P0 and P1 allows us to analyze the change in probability when altering the unit of the variable Xk, with the difference P1 - P0 indicating this probability shift This simulation highlights a specific change in probability, providing a qualitative interpretation of the probability function discussed earlier In the context of business credit risk, the expected value of a business classified as high risk (denoted as Y) contrasts with the remainder value of a non-risk classified business Corporate credit risk is influenced by an explanatory variable system that assesses the firm's capitalization ability and operational efficiency.
The logit model, also known as binary logistics, is utilized to evaluate the factors influencing business risk by classifying businesses as either risky or not This model is particularly effective when the dependent variable is binary, typically represented by "1" for risky and "0" for non-risky Importantly, the assignment of "1" or "0" to any given object does not impact the model's outcomes, ensuring a consistent assessment of risk across different cases.
The model building process involved validating its usability, verifying collinearity among explanatory variables, and assessing the model's interpretative level Additionally, the research focused on evaluating the model's feasibility to ensure the most accurate assessment of the research objectives.
Data collection and description
The data collection method involved accessing and aggregating secondary data from the BIDV head office and the BIDV Thanh Do branch, focusing on a total of 253 enterprise data points This data was processed to analyze and clarify the objectives of the study on Credit Risk Analysis of Vietnamese Enterprises, specifically within the banking system of BIDV Thanh Do.
The report analyzes a range of surveyed enterprises to evaluate their classification capabilities, particularly in identifying groups that can reliably meet debt obligations versus those facing repayment challenges, which are categorized as credit risks.
The article analyzes credit risk for enterprises by utilizing data from banks and assessments of their repayment capabilities It emphasizes the bank's perspective on credit risk, categorizing enterprises as those that struggle to meet their financial obligations to banks To effectively evaluate credit risk, the study employs a set of indicators that reflect both the financial capacity of the client and the operational efficiency of the client’s business activities.
The sample was conducted on 253 enterprises, 176/253 enterprises were ranked at risk level, accounting for 69.6% of total surveyed enterprises The number of enterprises classified as risky is 77/253 enterprises, accounting for 30.4%
Table 1: Descriptive Statistics Business Credit risk frequency
Enterprises are categorized into non-risk and risk indicators based on their financial ratios, with non-risk enterprises exhibiting higher ratios compared to those classified as risk indicators Specifically, the classification of enterprises reveals that those with lower risk indicators are ranked distinctly from those deemed higher risk.
Group 1: The target of the non-risk group is higher than the group of risk indicators are 9 indicators including retained earnings / total assets (X1), retained earnings /
9 net revenues (X2), working capital / Total Assets (X3), Net profit margin (X4), Net Profit / Equity, Net Revenues / Short-term Debt (X6) and Short-term liquidity ratio (X7)
Group 2: The target of the non-risk group is lower than the risk group is Net sales /
The change ratio in Total Assets (X9) of a group of enterprises is considered as risk has balance value in comparison with the non-risk group
The indicators effectively illustrate the current status of enterprises, distinguishing between risk and non-risk categories In Group 1, non-risk enterprises exhibit higher returns, liquidity, and liquidity ratios compared to their risk counterparts Similarly, Group 2's data reveals that non-risk enterprises consistently outperform the risk group, as evidenced by lower ratios in Total Assets to Total Assets (X7), Net Receivables (X8), and Net Sales to Total Assets (X12).
Table 2: Describe the indicators of financial ability and effectiveness in the operation of enterprises
Rủi ro tín dụng doanh nghiệp
Non-risk Risk Total Diffe renc e
Source: Survey and synthesis of the author
Tested Results of the research model
2.3.1 Correlation Matrix of the variables in the model
The correlation matrix, as outlined by Pearson and others in 1903, illustrates the relationships among the variables in the model, revealing a relatively low overall correlation The highest correlation coefficient observed is 0.707, indicating a moderate relationship between variables X6 and X8 Generally, the degree of collinearity among the independent variables remains low, allowing for the effective introduction of these variables into the modeling process.
** Correlation is significant at the 0.01 level (2-tailed)
* Correlation is significant at the 0.05 level (2-tailed)
The result of logistic regression model (Binary logistic)
The research model utilizes nine independent variables (X1 to X9) to assess financial viability and efficiency A logistic regression model is employed to analyze the factors influencing business risk, with the findings detailed in the accompanying table.
Table 4: Summary table of the model
Source: Summary from analytical data
The audited results with explanatory variables affect the model's credit risk show that with
9 expected variables affecting the model The results can be divided into the two groups of impact
The Group's influence on forecasting credit risk is minimal when considering key metrics such as Retained Earnings to Total Assets (X1), Retained Earnings to Net Revenue (X2), Net Profit to Net Income (X4), Net Profit to Equity (X5), and the Short-term Liquidity Ratio (X7).
Variable groups are likely to impact the Company's credit risk including variable capital / total assets (X3), net revenue / current liabilities (X6), turnover Net / Total Assets (X8) and Changes in Total Assets (X9)
A recent study utilizing Standard & Poor's credit risk classification reveals that 60.08% of surveyed enterprises exhibit a high-quality risk index, indicating a low likelihood of credit risk In contrast, 15.42% of enterprises are rated as average quality, while another 15.42% fall below average quality Alarmingly, 9.09% of the enterprises are categorized as being in poor condition, suggesting a high probability of default in the near future.
Table 5: Summary of credit risk ratings by Standard & Poor's
5 Ba Medium quality, with speculation factor 7 2.77
Source: Summary from the results of the analys
The model results indicate that all nine variables are expected to be included in the analysis Among these, four variables effectively explain the enterprise risk, while five variables do not provide significant insights into the credit risk of the enterprise.
Table 6: Conclusion of model results
Input variables Results from the model
1 X1 RE/TA= Retained Earnings / Total Assets
2 X2 RE/NR= Retained Earnings / Net Revenue
3 X3 WC/TA= Working capital / Total assets
6 X6 NR/STD= Net revenue / Short-term debt Statistical significance
7 X7 CR: Short-term liquidity ratio
8 X8 NR/TA= Net revenue / Total assets
9 X9 (Log(TA): Log(Total assets))
Source: Summary from the results of the analysis
The study's findings align with previous research by Le Tat Thanh (2012), Altman (2000), Lo Ka Wan (2005), and Ciaran Walsh (2006), highlighting the relationship between Total Net Sales and Total Assets (X8) This relationship demonstrates a significant impact, yet it also reflects the contrasting actual performance of Vietnamese enterprises, particularly regarding both general and corporate loans within the Sacombank system.
The analysis indicates that key financial ratios, including Retained Earnings to Total Assets (X1), Retained Earnings to Net Revenue (X2), Net Profit to Net Revenue (X4), After-tax Profit to Owner's Equity (X5), and the Short-term Ratio (X7), exhibit minimal impact on credit risk for businesses in Vietnam, specifically those accessing credit at the BIDV – Thanh Do branch.
The study identifies key factors that pose significant risks to enterprises, highlighting that the indicators related to profit factors have not effectively captured the overall performance of businesses.
Fifteen situations can heighten the risk faced by enterprises, as indicated by research measuring revenue and assets These findings suggest that revenue targets are crucial for assessing market connectivity and the ability to attract new customers, especially in challenging economic conditions Higher revenue ratios reflect greater market flexibility and increased business turnover, which can mitigate credit risk While generating profit is essential for enterprises, in the current difficult economic climate, prioritizing profit may not be the most effective strategy for reducing credit risk.
The turnover targets and asset changes in enterprises highlight their growth and operational dynamics, reflecting their trade status while transitioning from a focus on profitability to enhancing revenue and sustaining performance amid challenging economic conditions These criteria align well with the current state of the Vietnamese economy.
FRAMEWORK
Previous studies
Dr Tran Huy Hoang (2012) discusses strategies to mitigate risks in the credit activities of Vietnamese commercial banks in the Economic Development magazine He identifies common risks, including an over-reliance on a limited number of borrowers and a concentrated loan structure focused on medium and long-term credits, which poses challenges due to extended payback periods and reliance on future project success Additionally, he highlights the dangers of excessive credit growth, inadequate risk provisioning, and the misuse of collateral, emphasizing that loans should not solely depend on underlying assets while neglecting the customer's financial strength.
Effective credit management is crucial for banks, yet many banking officers exhibit significant shortcomings that impact performance The human factor remains essential in every institution, highlighting the need for improved qualifications and skills among banking staff To address these issues, the author provides practical recommendations for commercial banks: (i) develop a targeted credit strategy that leverages strengths and avoids impulsive decisions; (ii) enhance the capabilities of management and operational staff in the credit sector; (iii) utilize existing facilities and technologies effectively; (iv) establish risk provisions that align with the nature of loans; and (v) implement tools to mitigate credit risk.
The article provides a thorough analysis of credit risk factors, identifying their root causes and offering practical recommendations However, it also acknowledges limitations, particularly regarding the need for a deeper exploration of recent credit development practices in commercial banks Additionally, it highlights overlooked credit risk factors, such as the cross-ownership situations among banks and the influence of associated interest groups, which require immediate attention and solutions.
Assoc Prof Dr Nguyen Thi Mui's work, "Commercial Banks Management" (2006), published by Finance Publishing House, provides a comprehensive overview of commercial bank management, with a significant focus on credit management The author delves into essential concepts, processes, and regulations related to credit, highlighting its critical role in the banking sector.
The article examines key aspects of commercial bank management, focusing on the loan appraisal and debt collection processes, as well as the management of problematic debts and supervisory procedures It provides a comprehensive review of the theoretical frameworks, rules, and procedures governing credit management and credit risk management within banks.
The author effectively enhances readers' understanding of banking management practices and the interconnectedness of banking operations, particularly in credit management and credit risk However, while the work is grounded in research and theory, its application to practical banking management—especially in managing credit risk—requires further collaboration with relevant processes and regulations.
In "Risk Assessment and Prevention in Banking Business," Prof Dr Nguyen Van Tien (2002) presents updated knowledge on advanced banking management practices applicable worldwide, specifically tailored for commercial banks in Vietnam The author identifies key risks inherent in banking operations and enhances understanding through case studies and practical exercises This work emphasizes the importance of aligning with modern financial processes and regulations, offering valuable insights and tools for administrators to assess risk factors and implement effective solutions However, it acknowledges the need for further refinement to better align with the specific business practices of Vietnamese commercial banks.
The State Bank of Vietnam's Circular No 02/2013/TT-NHNN, issued on January 21, 2013, outlines mandatory regulations for asset classification, risk provisioning, and the methods for establishing risk provisions within credit organizations, including commercial banks and foreign bank branches All credit institutions are required to adhere strictly to these legal guidelines to effectively manage risks in their operations.
The legal document has undergone numerous amendments and additions, aligning it closely with international regulations If commercial banks adhere to these standards, it will significantly enhance the transparency of bank debt.
Ensuring system safety is crucial for commercial banks in managing credit risk and developing effective investment strategies However, as a legal document, it contains numerous administrative elements and commands that require revision and improvement To maximize its effectiveness, the document should be tailored to be more realistic and to mitigate any coping-related factors.
Nguyen Anh Tuan's 2012 doctoral dissertation, "Credit Risk Management of Bank for Agriculture and Rural Development in Vietnam," presents essential indicators for identifying and early warning of credit risk, along with measurement methods A key strength of the research is its comprehensive qualitative and quantitative criteria for assessing the effectiveness of credit risk management at the Bank for Agriculture and Rural Development of Vietnam, while also offering recommendations for other commercial banks.
The dissertation identifies significant weaknesses in the credit risk management practices at Agricultural Bank, including an outdated risk management model and an unsuitable credit scoring system that relies heavily on qualitative factors while neglecting quantitative analysis Additionally, the classification of debts and risk provisions are insufficient and do not align with advanced regulatory procedures, contributing to an increase in bad debts and a decline in profitability.
The author has proposed essential strategies to enhance credit risk management in banks, offering practical recommendations However, a notable limitation of the study is its limited applicability to credit risk management in general, particularly considering that the Bank for Agriculture and Rural Development is a relatively subsidized institution with outdated technology and low integration with international finance and banking systems.
Nguyen Duc Tu's 2012 PhD dissertation, "Credit Risk Management in VietinBank," presented a novel perspective on credit risk, distinguishing it from traditional views held by economists and managers in Vietnam The dissertation highlights the potential for unexpected discrepancies between actual and expected returns, emphasizing that credit risk can result in financial losses, including a net reduction in income and a decrease in market value This innovative concept serves as a crucial theoretical foundation for defining the specific elements of effective credit risk management.
Moreover, the dissertation has developed the credit risk management theories applied to the bank with the content of building credit risk management model in the approach of
The article discusses 19 current risk management methods, emphasizing the importance of credit risk management models to improve efficiency and transparency It recommends that banks develop new credit policies based on a comprehensive credit analysis and review system, focusing on both pre- and post-loan decision management The author identifies key causes of risk and offers practical solutions and directions for the Industrial and Commercial Bank of Vietnam and other commercial banks Additionally, the article highlights the bank's commitment to strengthening its credit risk management framework and enhancing its integration into the international financial system.
Theories
Banks face various risks that can lead to significant losses and potential bankruptcy, as outlined by the Basel Accords These risks include credit risk, market risk, and operational risk Credit risk refers to the possibility of loss due to a borrower's failure to meet their financial obligations, such as loans or credit lines The Basel Committee offers two methodologies for calculating capital requirements for credit risk: a standardized approach and the Internal Ratings-Based (IRB) approach, which requires explicit supervisory approval Market risk involves potential losses in both on- and off-balance sheet positions due to fluctuations in market prices, including interest rate risk, equity risk, and foreign exchange risk related to financial instruments.
6 Basel II (2006) International Convergence of Capital Measurement and Capital Standards, A Revised Framework Comprehensive Version
7 Basel II (2006) International Convergence of Capital Measurement and Capital Standards, A Revised Framework Comprehensive Version
In the realm of trading and banking, the value at risk (VaR) approach is the most widely used method for measuring market risk Operational risk, on the other hand, refers to the potential for direct or indirect losses stemming from insufficient or failed internal processes, personnel, systems, or external events To measure operational risk, three primary approaches are utilized: the Basic Indicator Approach (BIA), the Standardized Approach (SA), and the Advanced Measurement Approach (AMA).
3.2.2 Credit risk management in banks
A bank loan is a type of debt that involves the transfer of financial assets from a lender to a borrower The borrower receives a specific amount of money, known as the principal, which must be repaid along with interest, a fee charged by the bank One of the primary risks involved in lending is credit risk, which pertains to the borrower's ability to repay the loan.
Credit risk is the most significant risk for banks due to its potential for large losses, encompassing default risk, exposure risk, and recovery risk Effective credit risk management is essential in banking operations, focusing on both loan and investment portfolios This management process involves careful decision-making based on financial data, market assessments, and evaluations of borrowers and their management Continuous monitoring through periodic reporting helps track credit commitments, while "warning systems" are implemented to detect early signs of borrower distress, aiming to prevent defaults whenever possible.
Depending on purpose, research requirements, we have different ways of classifying credit risk Depending on the classification criteria, credit risk is divided into different categories
- Based on the risk reason, credit risk can be divided into two groups:
+ Ethical risk is the risk caused by inadequate information after the transaction
+ Risks of opposite choice are due to inadequate information created before the transaction
- Based on the level of loss, credit risk can be divided into two groups:
Capital accumulation risk arises when a bank fails to recover loans, leading to frozen funds that reduce liquidity and negatively impact the bank's financial health.
8 Basel I http://www.bnm.gov.my/guidelines/01_banking/01_capital_adequacy/02_basel1.pdf accessed 2009-03-14
9 Joel Bessis (1998) Risk Management in Banking
Borrowers face the risk of defaulting on their debts, including both principal and interest, which can lead to financial losses for lenders In such cases, recovery of the loans can only occur through the liquidation of the borrower's assets.
- Based on the users, risk can be divided into three main groups:
+ Individual customer risk: Credit risk occurs in individual clients
+ Risks of companies / economic organization, financial institutions: Credit risk occurs in customers that are companies, economic organizations, financial institutions
+ National or geographic risk: Credit risk occurs in each country for debt and aid operations
- Based on the overall nature of the risk: risk can be divided into Transaction and portfolio risk
+ Transaction risk is the risk that is caused by the limitations in the transaction process, loan approval, customer rating, including the risk of selection, security risk and operational risk
+ Portfolio risk is the risk that the cause is due to restrictions on the bank's portfolio management, divided into internal and centralized risks
- Based on risk period, risk can be divided into three groups:
+ Risk before loan: occurs when the bank misjudges the customer, resulted the loan to customers who are not eligible to ensure the ability to repay in the future
During the loan process, various risks can arise, primarily during the credit granting phase These risks may include disbursement errors, failure to regularly update customer information, and unforeseen hidden financial risks.
+ Risks after the loan: occur when credit officers do not grasp the situation of using loans, the future financial ability of customers
- Based on the range of risk: Credit risk can be divided into two groups:
+ Individual credit risk is the risk that occurs for only a loan or a portfolio of banks Causes can be attributed to a number of reasons as follows: characteristics of business type
27 of customers, financial situation of customers, customer morality, ability to manage and use capital of customers
Systemic credit risk affects not only individual banks but also the entire banking sector, with several contributing factors Key causes include shifts in government policy, inflation, legislative changes, and social or political instability, as well as unforeseen natural events.
The credit structure, similar to credit size, does not directly indicate risk levels but rather highlights the concentration of credit within specific sectors or timeframes A heavily skewed credit structure towards high-risk areas may signal potential credit risks Credit structure can be categorized into distinct groups for better analysis.
When analyzing loan structures by sector, it becomes evident that high-risk industries carry a significant risk of defaulting on bank debt A credit structure heavily concentrated in a single sector increases vulnerability, particularly when that sector faces degradation or is adversely impacted by fluctuations in other sectors.
The loan structure based on loan term is influenced by a bank's capital composition A bank with a significant short-term capital structure and limited long-term capital, while having a large long-term credit portfolio, may encounter liquidity risks due to excessive reliance on short-term funding for medium and long-term loans Conversely, an overemphasis on medium and long-term credit can elevate the overall risk level for the bank.
Loan structure by asset-backed: The percentage of loans with collateral is low, the bank faces a potential risk when the customer defaults.
Overdue debt serves as a key indicator of credit risk, arising from weak credit relationships that compromise the essential principle of timely and complete repayment When extending credit, it is crucial to consider both the repayment period and the total amount due Failure to repay loans on time leads to accruing debt, with borrowers struggling to meet their obligations for either the principal or interest within the agreed timeframe.
Overdue debts can be determined at any time through the system of books and credit records at the bank
Overdue debts are reflected in the following two criteria:
The ratio of overdue debts customers to total outstanding loan
= Number of overdue debt customers
Total number of outstanding loans customers x 100%
When the bank has large overdue loans and the big number of customers with overdue debts, the bank has a high level of risk and vice versa
Non-performing loans (NPLs) refer to funds lent to borrowers that cannot be recovered due to factors such as business losses, bankruptcy, or increased liabilities, leading to a loss of solvency These debts typically persist for over a year and present significant challenges in resolution.
- According to the International Monetary Fund (IMF)
The definition given by the IMF is as follows:
A loan is classified as a non-performing loan (NPL) when interest or principal payments are overdue for at least 90 days, when interest payments have been restructured or renewed after being overdue for 90 days or more, or when there are concerns about the full repayment of payments that are less than 90 days overdue.
Basically, Non-performing loans, according to IMF, is defined based on two factors:
Bad debt is primarily categorized based on two factors: overdue debts exceeding 90 days and concerns regarding the customer's repayment capacity This assessment considers the duration of overdue payments and the ability of the customer to repay A customer's repayment capability may be classified as either completely unpaid or insufficiently met.
- According to the State Bank of Vietnam (SBV)
Credit risk provision
The risk provision evaluates a bank's capacity to cover potential losses in its credit activities when risks materialize Utilizing a reserve fund indicates that the bank is confronting a risk of capital loss, making the risk provision a crucial indicator of capital vulnerability According to Circular 02/2013 / TT-NHNN dated January 21, 2013, the definition of risk provision is established to ensure financial stability.
Risk provision refers to the funds allocated within operating expenses to mitigate potential losses from debts incurred by credit institutions and foreign bank branches It encompasses both specific provisions, which are set aside for particular risks, and general provisions, which address broader financial uncertainties.
Specific provision is the amount of money that is set up to reserve for incident losses specific debt
General provision is the amount of money that is set up to reserve for incident but undefined
This ratio reflects the ability to offset the risk from credit activities A lower rate indicates better credit quality
Ratio of covering the risk of losing capital
This ratio indicates the percentage that risk reserve fund can offset for non- performing loan when they moved to losing capital debt (Group 5)
If this ratio is high, it can be attributed to the main causes: (i) high-risk loans; (ii) the bank has sufficient financial capacity to protect credit risk.
Regulation
In 2000, the Basel Committee on Banking Supervision established 17 principles aimed at enhancing the management of credit risk, promoting both the efficiency and safety of credit operations These principles emphasize critical areas essential for effective credit risk management.
+ Develop an appropriate credit environment
The Basel Committee mandates that the Board of Directors approve and annually review credit risk strategies and policies, which should outline risk tolerance levels and responses to various credit risks Consequently, the Board is tasked with executing the credit risk management strategy and formulating policies and procedures to effectively detect, measure, monitor, and control credit risk across all banking activities and portfolios It is essential for banks to identify and manage credit risk associated with all their products and services.
Banks must establish clear and healthy credit criteria, including target markets, customer profiles, and credit terms It's essential to set specific credit limits for various customer types and borrower groups to effectively manage diverse credit risks while allowing for internal comparisons and monitoring A well-defined credit approval process should involve collaboration among marketing, credit analysts, and approval departments, with clearly delineated responsibilities Additionally, developing a skilled and knowledgeable credit risk management team is crucial for making informed assessments in credit risk evaluation, approval, and ongoing management.
+ Maintain an effective management, measurement and monitoring process
An effective credit portfolio management system is essential for banks, involving thorough monitoring of credit-related conditions This includes establishing suitable reserve sizes and implementing an internal risk rating system tailored to the nature, scale, and complexity of banking operations to enhance credit risk management.
The Bank must implement a robust analytical and information system that allows management to evaluate credit risk for both on-balance sheet and off-balance sheet activities Additionally, it should establish a comprehensive monitoring system to assess the structure and overall quality of its credit portfolio.
+ Ensure adequate control with credit risk
The Bank must implement an independent and ongoing review system for its credit risk management processes, with findings reported to the Board of Directors and senior management.
Effective management of the credit process is essential to ensure that lending remains within safe and acceptable limits Timely internal controls must be in place to keep management informed at all levels, adhering to established policies, procedures, and limits.
Banks should have early warning systems for deteriorated credit, problematic credit management, and similar situations.
Credit risk measurement
Risk measurement follows risk identification and is crucial for effective risk management in banking Many banks globally are adopting advanced methods and models to assess various risks One prominent approach is the qualitative model of credit risk management, which focuses on non-numeric factors to evaluate potential credit risks.
The qualitative model of credit risk is a traditional borrower-based assessment utilized by banks, which primarily relies on various borrower information This model employs the 5C or 6C framework to evaluate and interpret borrower characteristics, enabling banks to make informed lending decisions.
+ Character: Credit officers must be sure that the borrower has a clear credit purpose and is well-intentioned to repay
+ Capacity: The borrower must have the legal capacity and civil act capacity; the borrower must be the legal representative of the business
+ Cash flow: determine borrower’s source of repayment
+ Collateral: A second source of income that can be used to repay a loan to a bank
+ Conditions: The bank stipulates conditions depending on the credit policy from time to time
+ Control: Assess the effects of changes in laws, regulations, the ability of customers to meet the standards of the bank
The simplicity of this model lies in its application; however, its effectiveness is contingent upon the accuracy of the information source, the forecasting capabilities, and the analytical skills of credit officers This highlights the importance of a quantitative model in credit risk management.
The E.I Altman model is designed to assess credit scores for US companies by providing an aggregate measure known as Z, which classifies borrowers' credit risk This model relies on key financial indicators (X) and evaluates their historical significance in predicting borrower default probabilities.
X1 = Net working capital of total assets
X2 = retained earnings of total assets
X3 = Profit before tax, interest on total assets
X4 = stock value to long-term book value
A higher Z-score indicates a lower probability of customer default, while a lower or negative Z-score signifies a higher risk of default Consequently, customers with low or negative Z-scores are categorized into high-risk groups.
Any company with a score of