Predicting the probability of default for small and medium enterprises based on financial indications

MINISTRY OF EDUCATION AND TRAINING

STATE BANK OF VIETNAM

BANKING UNIVERSITY OF HO CHI MINH CITY
-----oOo-----

NGUYEN DIEU LINH

PREDICTING THE PROBABILITY OF DEFAULT FOR
SMALL AND MEDIUM ENTERPRISES BASED ON
FINANCIAL INDICATORS

GRADUATION THESIS
MAJOR: FINANCE & BANKING
CODE: 7340201

HO CHI MINH CITY, 2021


MINISTRY OF EDUCATION AND TRAINING

STATE BANK OF VIETNAM

BANKING UNIVERSITY OF HO CHI MINH CITY

NGUYEN DIEU LINH

PREDICTING THE PROBABILITY OF DEFAULT FOR
SMALL AND MEDIUM ENTERPRISES BASED ON
FINANCIAL INDICATORS

GRADUATION THESIS
MAJOR: FINANCE & BANKING
CODE: 7340201

SCIENCE INSTRUCTOR
Ph.D. NGUYEN MINH NHAT

HO CHI MINH CITY, 2021


i
ABSTRACT
The internal credit rating system always plays an important role at commercial banks in
assessing customers' credit risk and assisting the bank in making credit decisions as
well as in management activities, risk treatment at the bank. At the same time, the
Government has been building a legal framework for the credit rating to improve
information transparency and support for banks to control credit risk from the
beginning as well as support the stock market, the bond market to promote capital
mobilization through the stock market, protect the rights and interests of investors.

Researching and selecting suitable rating models will significantly contribute to the
development of credit rating activities in Vietnam. However, the current models for
predicting default probability have certain limitations and are being debated,
inconsistency about these models' reliability, which leads to difficulty in choosing
the model is suitable to predict the probability of default of the business. Besides,
determining which financial ratios affect the ranking results is always the goal,
which needs to be studied in default prediction research. Up to now, there are still
not many studies published in Vietnam on selecting models to forecast the
probability of default of enterprises based on financial indicators.

Therefore, the thesis focuses on the issue of "Predicting the probability of default for
Small and Medium Enterprise based on financial indicators" to provide commercial
banks systematically a theoretical basis and empirical evidence related to the selection
of an appropriate business bankruptcy prediction model to contribute to improving the
efficiency in credit risk management of the bank in the future.
Based on the importance and necessity, the objective of this study is to: (i) determine
the criteria of an appropriate forecasting model; (ii) how to choose a model capable of
predicting the default probability of Small and Medium Enterprises (SMEs) at
Vietnamese commercial banks based on financial indicators. The results obtained from
this study aim to provide additional quantitative scientific evidence to answer which
predictive model gives the best results in predicting the probability of default of
medium firms and small in Vietnamese commercial banks; (iii) The most important


ii
contribution of this study is to develop a basic idea in the use of financial indicators
to forecast the default probability of SMEs, thereby contributing to improving
efficiency results in the credit risk control of commercial banks in Vietnam in the
coming time.
SMEs play a major role in most economies, particularly in developing countries.
SMEs account for the majority of businesses worldwide and are important
contributors to job creation and global economic development. Micro, small and
medium enterprises, commonly known as small and medium enterprises, are smallsized enterprises in terms of capital, labor or turnover. Small and medium
enterprises can be divided into three categories based on their size: micro
enterprises, small enterprises and medium enterprises. According to the World Bank
Group's criteria, a micro enterprise is an enterprise with a number of employees less
than 10 people; a small enterprise with a number of employees from 10 to less than
200 people and a capital of 20 billion or less; medium enterprises have from 200 to
300 employees with capital of 20 to 100 billion.
Probability of default is an important component applied in many credit risk

analysis and risk management activities. According to Basel II, it is a key parameter
used in calculating the level of economic capital capable of absorbing risks at credit
institutions. PD is one of the most useful ratios for classifying borrowers. All banks,
whether using standard or other advanced methods must provide supervisors with
an internal estimate of the PD relative to the borrower to the extent of the score. The
ranking result based on PD is considered relatively accurate as it is calculated on the
firm's actual financial ratios and can practically reflect the business's state. PD can
effectively reduce credit risk if fully considered.
Through a review of domestic and foreign studies shows that financial institutions
can apply many different credit rating models to predict the default probability of
enterprises. These predictive models can be polynomial models, logit models, probit
models, artificial neural network models. Besides, these ranking models use inputs
or different financial indicators to forecast the bankruptcy of a business. Financial
ratios are commonly used as short-term solvency, rate of return/total assets, total


iii
liabilities/total assets. However, with data sets built in different periods, the
conclusions about choosing the appropriate credit rating model and financial
indicators affecting the probability of default in the researchers are different, as well
as the application in Research to predict the possibility of default of SMEs
customers in Vietnam according to the author which is a new point. Through the
analysis, comparison and synthesis of the above studies and related issues, the
author has pointed out some research gaps, proposing the proposed research model
and expected method for the topic.
To accomplish the research objectives, the author implemented through 04 stages
according to the following steps: Stage one is collect and process data; The second
phase select the input variables of the model; The third stage run the regression on
selected credit rating models (the logit model, the probit model, the complementary
log-log model); The last stage use the Confusion matrix and F1 - Score to evaluate

each model's regression results. On that basis, select an appropriate credit rating
model and has the ability to predict well the probability of default of customers.
The study was conducted based on the data, which are taken from the annual financial
statements of approximately 400 companies from 2017 to 2019. These financial
statements have been audited to ensure the quality of the information source. Out of
400 businesses, there are 31 businesses in the field of consumer goods trading; 35
enterprises in the petroleum business sector; 39 businesses in the automotive business;
40 enterprises in the construction and installation industry; 43 enterprises in the
pharmaceutical industry and medical equipment; 45 enterprises in the textile and
garment industry; 47 enterprises in the fisheries sector (fish, shrimp, clam,...); 54
businesses in the iron and steel industry and 66 businesses in the agricultural sector
(rice, coffee, pepper,...). Based on the studies, the author selected 14 financial
indicators as independent variables for the credit rating models in the research paper.

Through analyzing the regression results from parametric models, and based on
criteria calculated from the confusion matrix (Accuracy, Sensitivity, Specificity,
Precision, F1 - Score) to compare and evaluate the ability to predict default


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probability of each model. Thereby finding a suitable model to predict the default
probability of enterprises.
The final result of the research shows that there are 5/14 variables play an important
role in predicting the default probability of customers, these are Income before
tax/Total assets, Total liabilities/Total assets, Earnings before tax, interest and
amortization/Long-term debt, Average cost of goods sold/Inventory and Total
revenue/Total assets. Through the research results, commercial banks can evaluate
and select customers in practice to minimize the risk that customers cannot repay
their loans.
From the research results, the author proposes some suggestions for commercial

banks on the development of the internal credit rating system in the coming time.
The thesis has found a model to predict the solvency (default probability) of SMEs
customers at commercial banks in Vietnam. The model can help stabilize credit
quality, minimize arising bad debts. Customers with a qualified credit rating (rated
A or higher) combined with the results of measuring the good repayment capacity
according to the model will have a low probability of incurring bad debt, according
to which credit risk for this group of customers is small.
The model can be seen as a supporting tool for commercial banks in credit granting,
assuring credit quality, and facilitating an efficient, safe, and sustainable expansion
and growth. From there, it can help banks select and maintain a good customer
structure, promote marketing strategies towards low-risk customers and develop a
network of reputable customers, ensuring debt repayment.
The model results are the basis for commercial banks to orient credit shrinking to
weak customers (high probability of default) and effective credit growth for wellperforming customers (low probability of bankruptcy). Simultaneously, building a
credit policy suitable for each type of customer in terms of credit terms, interest
rates, fees, requirements for security measures…to ensure safety in operation.
On the other hand, information to measure the solvency and the results of the model
also reflects many problems related to the business performance of the business and


v
the field - production and business sector. As a result, the model becomes a source of
information for future credit policy analysis, assessment, forecast and administration.


vi
DECLARATION
This thesis is the author’s own research, the research results are truthful, in which
there is no previously published content, or the content made by others except the
full citations cited in the thesis.

The author

Nguyen Dieu Linh


vii
ACKNOWLEDGEMENTS
First of all, I would like to express my sincere thanks and express my deep ratitude
to the teachers of Banking University of Ho Chi Minh City for their enthusiastic
teaching, as well as consolidating the solid foundation knowledge, helping me
successfully complete the university curriculum.
In particularly, I would like to express my sincere thanks to Mr. Nguyen Minh Nhat
for giving me the detailed guidance and wholehearted assistance in completing the
graduation thesis. Without his thoughtful support, it would be difficult for me to
complete this thesis well.
Due to my limited practical experience, the content of the graduation thesis cannot
avoid some shortcomings, I am looking forward to receiving further advice from
teachers to learn more experiences. I believe these experiences are extremely
valuable so that I can develop myself well in the future.
I sincerely thank you!


viii
TABLE OF CONTENTS
ABSTRACT.............................................................................................................. i
LIST OF ABBREVIATIONS.................................................................................. x
LIST OF FIGURES................................................................................................. x
LIST OF TABLES.................................................................................................. xi
CHAPTER 1: INTRODUCTION.......................................................................... 1
1.1. The urgency of the research......................................................................... 1

1.2. Research Objectives...................................................................................... 5
1.3. Research Questions....................................................................................... 5
1.4. Research Subjects.......................................................................................... 5
1.5. Research Methods......................................................................................... 6
1.6. Expected Contributions................................................................................ 6
1.7. The Structure of Research............................................................................ 7
CHAPTER 2: LITERATURE REVIEW............................................................... 9
2.1. Small And Medium Enterprises (SMEs)..................................................... 9
2.2. Probability Of Default (PD)....................................................................... 11
2.3. Financial Indicators.................................................................................... 13
2.4. Overview of probability of default models................................................ 14
2.4.1. probability of default models.................................................................. 14
2.4.2. The difference between Logit model, Probit model and Complementary
log-log model................................................................................................... 21
2.5. Related studies............................................................................................. 22
2.5.1. Related studies in Vietnam...................................................................... 22
2.5.2. The other related studies......................................................................... 24
CHAPTER 3: DATA AND METHODOLOGY OF RESEARCH.....................27


ix
3.1. Theoretical framework............................................................................... 27
3.2. Data collection and processing................................................................... 28
3.3. Selection of input variables in the default prediction model....................30
3.4. Models for predict the probability of default............................................ 35
3.4.1. Logit model............................................................................................ 35
3.4.2. Probit Model........................................................................................... 36
3.4.3. Complementary Log-Log Model............................................................ 36
3.5. The evaluation criteria of default prediction models................................ 37
3.5.1. Confusion Matrix.................................................................................... 37

3.5.2. F1-Score................................................................................................. 39
CHAPTER 4: EMPIRICAL RESULTS.............................................................. 41
4.1. Descriptive statistics results........................................................................ 41
4.2. Regression results of parametric models................................................... 44
CHAPTER 5: CONCLUSION AND RECOMMENDATION...........................51
5.1. Applying the model to forecast probability of default of SMEs customers
at commercial banks in Vietnam....................................................................... 51
5.1.1. Tools to assist in identifying groups of potential customers....................51
5.1.2. The model results are the basis of credit policy orientation....................52
5.1.3. Applying the model results to improve the efficiency of credit risk
management in commercial banks.................................................................... 54
5.2. Suggest using the model to forecast the probability of default at Credit
Rating Agencies in Vietnam.............................................................................. 54
5.3. Limitation of the topic and future research direction..............................56
5.3.1. Limitation............................................................................................... 56
5.3.2. Future research direction........................................................................ 57
REFERENCES ............................................................................................................
APPENDIX ..................................................................................................................


x
LIST OF ABBREVIATIONS

SMEs

Small and Medium Enterprises

ANN

Artificial Neural Networks

UK

United Kingdom

MDA

Multiple Discriminant Analysis

APV

Present Value Method

LIST OF FIGURES
Figure

Name of Table

Page

1.1

Forecast Bankruptcy Rate in 2021 compared to 2019

2

2.1

4 popular groups of financial indicators

14


xi
LIST OF TABLES
Table

Name of Table

Page

2.1

Review models to predict the probability of default

15

2.2

The difference between Logit model, Probit model and
Complementary log-log model

21

3.1

Synthesize the number of businesses - business lines

28

3.2

Separate bankrupt and non-bankrupt companies

30

3.3

Independent variables in the probability default prediction model

34

3.4

The data structure of Variables in the Logit model

35

3.5

Confusion Matrix

38

4.1

Descriptive statistics of the independent variables

41

4.2

Correlation matrix

43

4.3

Regression results of parametric models

44

4.4

Confusion matrix of the logit model

47

4.5

Confusion matrix of the probit model

48

4.6

Confusion matrix of the complementary log-log model

49

1
CHAPTER 1: INTRODUCTION
In this chapter, the author introduces the overview of the research, the reasons for
choosing the topic, the research objectives, the research questions, the subjects and
scope of the study, the research method, and the main content of the thesis.
1.1. The urgency of the research
In recent times, the Covid-19 has appeared and happened complicatedly, seriously
affecting all countries in the world. The wave of bankruptcy of large businesses
globally has been formed since mid-2020 when the Covid-19 pandemic affected
most major countries in the world. Euler Hermes (7/2020) forecasts the rate of
bankruptcies will increase to 35% between 2019 and 2021. Among the world's
economic powers, the US will suffer heavy losses when the number of insolvency
businesses is forecast to increase 57% by 2021 compared to this rate in 2019 before the outbreak of the COVID-19 pandemic. Bankruptcy cases are also
expected to increase 45% in Brazil, 43% in the UK and 41% in Spain, while in
China, where the epidemic started, the number of bankruptcies is forecast to grow
40%. It is estimated that by the end of 2021, all regions worldwide will increase
their default rates by double digits, with the strongest increase expected to occur in
North America (+56% year over year 2019), followed by Central and Eastern
Europe (+34%), Latin America (+33%), Western Europe (+32%) and Asia (+31%).
The Vietnamese economy is also influenced by the Covid-19 epidemic due to its large
openness, international integration is increasingly deep, so it suffers from many
negative impacts of epidemics in socio-economic fields. Operations of production,
supply and circulation of commerce, aviation, tourism, labor, and employment were
delayed and disrupted due to the effects of epidemics. Enterprises are heavily affected,
many businesses have to suspend operations or go bankrupt, dissolve or scale down
production and business. In the first 10 months of 2020, the number of enterprises
temporarily suspending their business for a term is 41.8 thousand, up

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58.7% over the same period in 2019; 13.5 thousand enterprises completed
dissolution procedures, up 0.1%.
Figure 1.1: Forecast Bankruptcy Rate in 2021 compared to 2019

Source: National Statistics, Solunion, Euler Hermes, Allianz Research.
In the context of the economy being heavily affected by the Covid-19 epidemic and the
increasing rate of business bankruptcy, so the internal credit rating system always plays
an important role at commercial banks in assessing the level of credit risk of customers
and assisting the bank in making credit decisions and in risk management at the bank.
In Vietnam, commercial banks are increasingly recognizing the importance of this
system in their credit operations and risk management, especially when Vietnamese
commercial banks are trying to meet the standards of Basel II.


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In that context, the urgency of this research topic is shown in the following specific
aspects:
Firstly, the current credit rating models have certain limitations and there are many
debates and inconsistency on the reliability of the credit rating models, leading to
difficulties in choosing the appropriate model of credit rating to forecast the
probability of default of the business (Huseyin & Bora, 2009). According to
research by Aysegul Iscanoglu (2005) and Hayden & Daniel (2010), there are many
research models in the field of credit rating such as discriminant analysis model,
Logit model (logistic regression), model decision tree, artificial neural networks
(ANN), Probit regression... model with the pros and cons of each model. There has
been much in-depth analysis on the above models. Platt (1991) used the Logit
model in testing and selecting financial variables and argued that it is better to use
the industry's average financial variables than to use the financial variables of a
single firm business bankruptcy report. Lawrence (1992) uses the Logit model to

predict the default probabilities of collateralized loans. Altman (1968) used
differential analysis models to find a linear function of the financial and market
variables to distinguish between the two types of insolvent and solvent enterprises...
Secondly, determining which financial indicators affect the ranking results is always
the goal, the issue to be studied in the research on the prediction of default. In 1926
– 1936, researchers only used basic financial ratios to rank along with some other
metrics such as Ramser & Foster (1931) with equity/total net revenue or Fitzpatrick
(1932) using equity/fixed assets ratio. In the next period, Altman (1968) used 5
financial indices in the differentiation analysis model to predict the insolvency of
businesses including equity/book value of debts, business net income/total assets,
operating income/total assets, profit after tax/total assets and working capital/total
assets. Also using a differentiation analysis model, however, Deakin (1972) chooses the
following 14 financial variables: cash/short-term debt, real cash flow/total debt,
cash/net sales, cash/fixed assets, current solvency, current assets/net sales, current
assets/total assets, income/total assets, high liquidity assets/short term liabilities, high


4
liquidity assets/net sales, high liquidity assets/total assets, total liabilities/total
assets, working capital/net sales, working capital/total assets. Over time, scientists
have found more financial indicators that can influence the credit rating results,
such as Blum (1974) using financial variables including the market rate of return,
liquidity ratio quick settlement, high liquidity assets/inventories, cash flow/total
liabilities, the book value of assets/total liabilities, the downward trend of profits,
downward trend of high liquidity assets/inventory or Back, Laitinen, Sere & Wesel
(1996) use 31 different indices.
Thirdly, the ranking method at banks in Vietnam is still subjective and qualitative
these days, based on the assessment – the experience of credit officers directly
managing customers (expert method). Therefore, it only supports decision-making
to grant credit, not a basis for decision-making because there is no highly reliable

scientific basis to forecast the bankruptcy of enterprises. Up to now, there are still
not many studies published in Vietnam on the selection of models to forecast the
probability of default of enterprises based on financial indicators.
Fourthly, the Government has been building a legal framework for the credit rating
sector to improve information transparency to support banks in controlling credit
risk from the beginning as well as to support the stock market and the bond market
promote capital mobilization through the stock market and protect the rights and
interests of investors. Researching and selecting suitable rating models will greatly
contribute to the development of credit rating activities in Vietnam. Specifically, the
Government has issued Decree No. 88/2014/NĐ-CP dated September 26, 2014,
providing for credit rating services, conditions for the operation of credit rating
enterprises established and operating in Vietnam; At the same time, according to the
Decision approving the development plan for credit rating services to 2020 and a
vision to 2030 of the Prime Minister No. 507/QĐ-TTg dated April 17, 2015, the
issuance of corporate bonds must be rated from of 2020.


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Obviously, the selection of a model to predict the probability of corporate default
based on appropriate financial indicators is mentioned as one of the measures to
manage the credit risk of Vietnamese commercial banks to divide type of customer
screening from the beginning and control the bank's default risk as recommended by
the Basel Committee (Basel II, 2004).
Therefore, the thesis focuses on the issue of "Predicting the probability of default for
Small and Medium Enterprise based on financial indicators" to provide commercial
banks systematically a theoretical basis and empirical evidence related to the selection
of an appropriate business bankruptcy prediction model to contribute to improving the
efficiency in credit risk management of the bank in the future.

1.2. Research Objectives

Based on determining the criteria of an appropriate forecasting model and how to
choose the model, the study will select a model capable of predicting the probability
of default of SMEs in Vietnam commercial banks based on financial indicators from
2017 to the end of 2019. This helps commercial banks screen customers and control
credit risks better.
1.3. Research Questions
In order to achieve the above objectives, the research has raised the following
research questions:
(i) How do financial indicators affect the probability of default of SMEs?
(ii) Which PD model gives the best results in forecasting the default probability of
SMEs?
1.4. Research Subjects
The research object of the thesis is the probability of default of SMEs customers
at Vietnamese commercial banks. Small and medium enterprises that satisfy one of
the following two criteria: (i) total capital is not over 100 billion VND; (ii) total
revenue of the preceding year not exceeding 500 billion VND.


6
Scope of the study: The study collects financial indicators from financial
statements of small and medium enterprises at Vietnamese commercial banks in the
period 2017 - 2019.
1.5. Research Methods
The thesis uses a combination of qualitative research methods and quantitative
research:
Qualitative research method: Discussing the views, perceptions, and assessments of
commercial banks on the issue of the credit rating of SMEs customers at
commercial banks in Vietnam. Research and study the factors affecting the credit
rating results of small and medium enterprises customers in commercial banks in
Vietnam. Since then, building scales to carry out quantitative research.

Quantitative research method: Redefining influencing financial factors and
measuring each factor's impact on the results of the credit rating of SMEs at
commercial banks in Vietnam. Using regression methods to build credit rating
models including logit, probit methods.
In addition, the thesis uses a combination of methods including: Descriptive statistical
method to organize data according to the characteristics that need description;
Comparative comparison between model and practice to make conclusions; Synthetic
analysis method to synthesize and analyze relevant data in the research process.

1.6. Expected Contributions
The research results of the thesis have scientific and practical significance are
shown on the following main aspects:
Analyze the basic theories, systematic background theories related to the default
probability prediction models, and the criteria for choosing the appropriate model
systematically and completely. On that basis, the study provides a fairly complete and
comprehensive way of published researches to see the gaps in previous studies related
to the selection of the most suitable model to forecast the default probability of small


7
and medium enterprises at Vietnamese commercial banks based on financial
indicators. This is an important basis for researchers to continue implementing
another related research.
Based on the found research problems and results, the study proposes to choose an
appropriate credit rating model capable of predicting the probability of default for
SMEs at Vietnamese commercial banks based on financial indicators to contribute
to improving the efficiency in credit risk control of commercial banks in Vietnam in
the coming time.
1.7. The Structure of Research
The main content of the thesis, in addition to the two part: introduction and

conclusion, the thesis includes 05 chapters as follows:
Chapter 1 (Introduction) present the urgency of the topic, research issue, research
objectives, research questions, research object and scope, research methods, the
contribution of the topic, and the thesis's structure to give readers an overall picture
of the entire study.
Chapter 2 (Literature Review) presents basic theories and background theories
related to credit ratings, default probability of enterprises and methods of measuring
and forecasting these contents as well as evaluation results of previous studies, this
has been published to clarify the urgency of the topic and provide a basis for
proposing research models and analyzing research results presented in the next
chapters.
Chapter 3 (Data And Methodology Of Research) presents in detail the research
model's content – the proposed probability default prediction model, detailed
description of the collected data, and the research methods used to show the
confidence level of the research results are presented in the next chapter. The
proposed default prediction model to use includes the parameter model (with logit,


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probit, complementary log-log models) and non-parametric model (including
decision tree model, random forest model).
Chapter 4 (Empirical Results) analyze regression results from parametric and
non-parametric models and based on indicators calculated from the confusion
matrix (Accuracy, Sensitivity, Specificity, Precision, F1 – Score) to compare and
evaluate the ability to predict the default probability of each model.
Chapter 5 (Conclusion and recommendation) summarizing the results achieved
by the thesis, thereby proposing solutions to help commercial banks improve the
ability to predict the probability of default of small and medium enterprises,
accordingly, promptly have the Orientation policy as well as adjusting creditgranting activities of commercial banks to achieve higher efficiency and reduce
credit risks, ensure capital safety. In addition, the thesis also suggests some

implications for corporate governance policies to minimize the risk of bankruptcy.
Besides, the thesis also gives the limitations and outstanding problems, from which
proposing the next research directions.


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CHAPTER 2: LITERATURE REVIEW
In this chapter, the author presents basic theories and background theories related to
small and medium enterprises, the default probability of enterprises and methods of
measuring and forecasting these contents, as well as assessment results of previous
studies that have been published to clarify the urgency of the topic, at the same time
provide a basis for the implementation of research model proposals and analysis of
research results are presented in the next chapters.
2.1. Small And Medium Enterprises (SMEs)
Small and Medium Enterprises (SMEs) play a major role in most economies,
particularly in developing countries. SMEs account for the majority of businesses
worldwide and are important contributors to job creation and global economic
development. They represent about 90% of businesses and more than 50% of
employment worldwide. Formal SMEs contribute up to 40% of national income
(GDP) in emerging economies. These numbers are significantly higher when
informal SMEs are included. According to our estimates, 600 million jobs will be
needed by 2030 to absorb the growing global workforce, which makes SMEs
development a high priority for many governments around the world. In emerging
markets, most formal jobs are generated by SMEs, which create 7 out of 10 jobs.
However, access to finance is a key constraint to SMEs growth, it is the second
most cited obstacle facing SMEs to grow their businesses in emerging markets and
developing countries.
Micro, small and medium enterprises, commonly known as small and medium
enterprises, are small-sized enterprises in terms of capital, labor or turnover. Small and
medium enterprises can be divided into three categories based on their size: micro

enterprises, small enterprises and medium enterprises. According to the World Bank
Group's criteria, a micro enterprise is an enterprise with a number of employees less
than 10 people; a small enterprise with a number of employees from 10 to less than 200
people and a capital of 20 billion or less; medium enterprises have from 200 to


10
300 employees with capital of 20 to 100 billion. In each country, people have their
own criteria for defining small and medium enterprises in their own country. In
Vietnam, according to Article 6 of the Government's Decree No. 39/2018/ND-CP
[1] dated March 11, 2018, stipulates:
1. Microenterprises in the fields of agriculture, forestry, fisheries and industry and
construction have an average annual number of employees participating in social
insurance not exceeding 10 people and total annual turnover not more than 3 billion
VND or total capital not exceeding 3 billion VND.
A microenterprise in the trade and service sector has an average annual number of
employees participating in social insurance not more than 10 people and total
annual revenue of no more than VND 10 billion or total capital of no more than
VND 3 billion.
2. Small enterprises in the fields of agriculture, forestry, fisheries and industry and
construction whose average number of employees participating in social insurance
does not exceed 100 people per year and total annual turnover does not exceed 50
VND billion or total capital is not more than 20 billion VND, but not a micro
enterprise as prescribed in Clause 1 of this Article.
A small enterprise in the trade and service sector has an average annual number of
employees participating in social insurance not exceeding 50 people and total annual
revenue of no more than VND 100 billion or total capital of no more than VND 50
billion but is not a micro enterprise as prescribed in Clause 1 of this Article.

3. Medium enterprises in agriculture, forestry, fisheries and industry and

construction with an average annual number of employees participating in social
insurance not exceeding 200 and total annual turnover not exceeding 200 VND
billion or the total capital source is not more than 100 billion VND but is not a small
enterprise or micro enterprise as prescribed in Clauses 1 and 2 of this Article.


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A medium-sized enterprise in the trade and service sector has an average annual
number of employees participating in social insurance not exceeding 100 and the
total revenue of the year does not exceed VND 300 billion or total capital is not
more than 100 billion VND is not a micro enterprise or a small enterprise as
prescribed in Clauses 1 and 2 of this Article.
2.2. Probability Of Default (PD)
Probability of default is an important component applied in many credit risk
analysis and risk management activities. According to Basel II, it is a key parameter
used in calculating the level of economic capital capable of absorbing risks at credit
institutions.
According to the definition set out by the Office of the Comptroller of the Currency:
“The probability of default is the risk that the borrower cannot or are not willing to
repay the debt in full or on time. Default risk stems from an analysis of the obligor's
ability to repay the debt under the contractual terms”. PD is often related to
financial features such as insufficient cash flow to cover costs, declining revenue or
operating margins, high leverage, reduced liquidity, or insufficient capacity to
perform successful business plans. In addition to these quantifiable factors, the
borrower's willingness to repay the debt which is also needed to be assessed to
determine the probability of default.
Or as Tysk (2010) explains, PD is a quantitative assessment of the likelihood of an
obligor going bankrupt within a certain time, usually a year. Usually, the default
probability outlines a company not fulfilling its liability for its loan or liabilities to
banks. Since the leading causes of insolvency are business losses or shortages of

money, it can also be insolvency that leads to the company's bankruptcy. To
estimate the probability of default, a bank can score companies based on their
ability to repay, thereby making better lending decisions.
PD is one of the most useful ratios for classifying borrowers. All banks, whether
using standard or other advanced methods must provide supervisors with an internal


12
estimate of the PD relative to the borrower to the extent of the score. The ranking
result based on PD is considered relatively accurate as it is calculated on the firm's
actual financial ratios and can practically reflect the business's state. PD can
effectively reduce credit risk if fully considered.
To predicting probability of default, they built the credit rating systems. The credit
rating is an opinion of the ability and readiness of an enterprise in making payments
on time for a specific debt during the life of the debt. The ranking system presented
in this report is denoted with 3 letters ABC, which are ranked from AAA (highest
stability) to C (highest risk level), respectively. Since then, the use of credit ratings
has become very popular and varied in terms of purposes and subjects of ranking,
therefore the way of view and viewpoint on social society has changed.
According to Michael K.Ong (2003), credit rating is a process of assessing and
classifying credit levels relative to different levels of risk, each rating being a clear
reflection and Briefly about the solvency of a rated company, at the same time, credit
rating is a process of using available and current information to forecast future results.
With Standard & Poor's viewpoint, credit rating is the assessment of the
creditworthiness of the party to fulfill financial obligations in the future based on
current factors and the assessor's perspective. In other words, credit rating is the
presentation of opinions about credit risk. Specifically is express an opinion on the
ability and readiness of the issuers (Rating issuers) - such as a corporation or state or
municipal government - to meet financial obligations enough and on time. Credit
ratings may also refer to the credit quality of an individually debt (Rating issues) - such

as a corporate bond or a government bond - or the associated risk assessment that could
lead to damage. Fitch Ratings affirms that in their opinion, credit rating is the
assessment of a person's ability to perform debt obligations such as interest rates,
concessional dividends, insurance, or other payables of a credit rating object. Fitch's
credit rating method combines both financial and non-financial factors. Therefore, the
evaluation index also shows that the organization's future profitability is assessed.