Application of artificial intelligence in credit risk management at vietnam bank for agriculture and rural development

MINISTRY OF EDUCATION AND TRAINING STATE BANK OF VIETNAM SUMMARY OF DOCTORAL THESIS APPLICATION OF ARTIFICIAL INTELLIGENCE IN CREDIT RISK MANAGEMENT AT VIETNAM BANK FOR AGRICULTURE AND RURAL DEVELOPME[.]

MINISTRY OF EDUCATION

AND TRAINING

STATE BANK OF VIETNAM

-

SUMMARY OF DOCTORAL THESIS

APPLICATION OF ARTIFICIAL INTELLIGENCE IN CREDIT RISK MANAGEMENT AT VIETNAM BANK FOR AGRICULTURE AND

RURAL DEVELOPMENT

HANOI – 2022

NGUYEN TIEN HUNG

AND TRAINING

-

NGUYEN TIEN HUNG

HANOI – 2022

Specialization: Finance - Banking

Code: 9340201

Scientific supervisor:

Supervisor 1: Dr Bui Tin Nghi Supervisor 2: Assoc Prof Dr Nguyen Duc Trung

APPLICATION OF ARTIFICIAL INTELLIGENCE IN CREDIT RISK MANAGEMENT AT VIETNAM BANK FOR AGRICULTURE AND

RURAL DEVELOPMENT SUMMARY OF DOCTORAL THESIS

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TABLE OF CONTENTS

GENERAL INTRODUCTION 1

CHAPTER 1: LITERATURE REVIEW OF ARTIFICIAL INTELLIGENCE IN CREDIT RISK MANAGEMENT 4

1.1 Researches on credit risk management models 4

1.2 Researches on credit risk assessment 4

1.2.1 Researches on measurement of probability of default 5

1.2.2 Researches on loss given default (LGD) 5

1.2.3 Researches on default risk 6

1.3 Researches on artificial intelligence model in credit risk management 6

1.4 Research gap 8

CHAPTER 2: THEORETICAL BASIS FOR APPLICATION OF ARTIFICIAL INTELLIGENCE IN CREDIT RISK MANAGEMENT 9

2.1 Theoretical basis of credit risk management 9

2.1.1 Concept of credit risk management 9

2.1.2 Contents of credit risk management 9

2.2 Theoretical basis for application of artificial intelligence in credit risk management 10

2.2.1 Overview of artificial intelligence 10

2.2.2 Artificial intelligence in credit risk management 10

2.2.3 Measurement framework applied to artificial intelligence model in credit risk management 11

2.2.4 Data for artificial intelligence models in credit risk management 12 2.2.5 Criteria to evaluate the effectiveness of artificial intelligence models in credit risk measurement 13

2.2.6 Conditions for application of artificial intelligence in credit risk management 14

2.3 International experience in research and application of artificial intelligence in credit risk management 15

2.3.1 Experience from the UK 15

2.3.2 Experience from the USA 16

2.3.3 Experience from India 16

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2.3.4 Experience from the Financial Stability Board (FSB) 17

2.3.5 Experience from the World Bank (WB) 17

2.3.6 Lessons for commercial banks in Vietnam 17

CHAPTER 3: CURRENT SITUATION OF CREDIT RISK MANAGEMENT AT VIETNAM BANK FOR AGRICULTURE AND RURAL DEVELOPMENT 20

3.1 Overview of Agribank 20

3.2 Current situation of credit risk management at Agribank 20

3.3 Assessing the current situation of credit risk management at Agribank 20 3.3.1 Achievements 20

3.3.2 Limitations and causes 21

CHAPTER 4: BUILDING ARTIFICIAL INTELLIGENCE MODELS IN CREDIT RISK MANAGEMENT AT VIETNAM BANK FOR AGRICULTURE AND RURAL DEVELOPMENT 22

4.1 Model recommendation 22

4.2 Building PD model 22

4.2.1 Description of data collected 22

4.2.2 Results of PD model 23

4.3 Building LGD model 23

4.3.1 Data description 23

4.3.2 LGD model results 23

4.4 Building EAD model 24

4.5 Conditions for application of artificial intelligence in credit risk management 25

CHAPTER 5: SOLUTIONS FOR APPLICATION OF ARTIFICIAL INTELLIGENCE IN CREDIT RISK MANAGEMENT AT VIETNAM BANK FOR AGRICULTURE AND RURAL DEVELOPMENT 26

5.1 Development orientation for credit risk management activities in the Vietnamese commercial banking system 26

5.2 Development orientation for credit risk management activities at Agribank 26

5.3 Solutions for application of artificial intelligence in credit risk management at Agribank 26

5.3.1 About the organizational structure for credit risk management 26

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5.3.2 About the process of applying artificial intelligence in credit risk

management 27

5.3.3 About the group of necessary support solutions 28

5.4 Recommendations to the State Bank of Vietnam 29

CONCLUSION OF THESIS 31

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1 RATIONALE OF THE RESEARCH

In the explosion trend of the 4.0 revolution, artificial intelligence is gradually affirming its role as a pioneering technology for the banking industry

in general and the credit sector in particular This technology was developed more than 50 years ago, however, with the advancement of computer science, the abundance of data and the needs of the market, artificial intelligence is being developed strongly and gradually shaping the future game for banks Realizing the importance and potential of artificial intelligence, on January 26, 2021, the Prime Minister issued the Decision No 127/QD-TTg on the national strategy on research, development and application of artificial intelligence to 2030, which stipulates specific tasks of the banking industry, including: “Analyzing and predicting loan demand, borrowers, supporting credit granting activities, detect fraudulent behaviors; customizing banking services for customers; providing instant support services to customers through virtual assistants and chatbots”

In the commercial banking system of Vietnam, Vietnam Bank for Agriculture and Rural Development is the largest bank in terms of total assets and number of customers However, in the previous period, this bank showed many negative credit-related cases, greatly affecting the business operations and reputation of the Bank The consequences of these cases remained for a long time and caused direct impacts on employees’ lives

Considering the above situation, the author finds that the research on the

thesis: “Application of artificial intelligence in credit risk management at

Vietnam Bank for Agriculture and Rural Development” has great significance

in both theory and practice

2 OBJECTIVES OF THE RESEARCH

2.1 General objectives

The thesis does an overall research on the theory and practice of artificial intelligence in credit risk management, thereby proposing solutions for application of artificial intelligence in risk management at Vietnam Bank for Agriculture and Rural Development

2.2 Particular objectives

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The general objectives are concretized into the following four objectives:

First, systematize the theoretical basis of artificial intelligence in credit risk management;

Second, assess the current situation of credit risk management at Vietnam Bank for Agriculture and Rural Development in order to determine the conditions and solutions for applying artificial intelligence in this activity;

Third, apply artificial intelligence to build a credit risk assessment model according to the advanced approach of Basel II at Vietnam Bank for Agriculture and Rural Development;

Fourth, propose groups of solutions and recommendations to apply artificial intelligence in risk management at Vietnam Bank for Agriculture and Rural Development

3 SUBJECT AND SCOPE OF THE RESEARCH

3.1 Subject of the Research

Research subject of the thesis is artificial intelligence in credit risk management

3.2 Scope of the Research

Spatial scope of the research: Applying artificial intelligence in credit risk management at Vietnam Bank for Agriculture and Rural Development, particularly focusing on artificial intelligence modeling in credit risk measurement

Time scope of the research: 2009-2021 In particular, the data used to build the artificial intelligence model was collected during the period 2009 -

2014

4 RESEARCH METHODOLOGY

- Methods of statistics, description, analysis and synthesis

- Survey method

- Quantification method: The thesis uses artificial intelligence models including: Decision Tree Model (DT), Neural network model (NN) to measure credit risk and make comparisons with traditional models such as the logit model

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The thesis has made new contributions in both theory and practice as follows:

First, the thesis has systematized the theoretical basis of artificial intelligence in credit risk management Artificial intelligence models are analyzed and clarified according to the steps of credit risk management including: identification, measurement, use of management and reporting tools, and supervision The thesis also provides a theoretical framework for building and applying artificial intelligence in credit risk management at commercial banks

Second, the thesis has used the survey method for leaders and employees to assess the current situation of credit risk management at Vietnam Bank for Agriculture and Rural Development

Third, the thesis has built an artificial intelligence model to measure credit risk based on the real data at Vietnam Bank for Agriculture and Rural Development Credit risk measurement models are designed according to the Advanced Internal Ratings- Based Approach (AIRB) of Basel II

Fourth, the thesis has proposed a series of solutions and recommendations to apply the artificial intelligence model in credit risk management

6 CONCLUSION OF THE THESIS

In addition to the general introduction, conclusion, list of references and appendices, the thesis is structured with 5 chapters including:

Chapter 1: Literature review of artificial intelligence in credit risk management

Chapter 2: Theoretical basis of artificial intelligence application in credit risk management

Chapter 3: Current situation of credit risk management at Vietnam Bank for Agriculture and Rural Development

Chapter 4: Application of artificial intelligence to build credit risk measurement models at Vietnam Bank for Agriculture and Rural Development

Chapter 5: Solutions for application of artificial intelligence in credit risk management at Vietnam Bank for Agriculture and Rural Developmen

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CHAPTER 1: LITERATURE REVIEW OF ARTIFICIAL INTELLIGENCE IN CREDIT RISK MANAGEMENT 1.1 Researches on credit risk management models

The theoretical research, Bullivant (2010) presented comprehensive aspects of credit risk management Some guidelines and researches by IIA (2020) and Oliver Wyman (2016) were about the three-layer protection model

or the “four-layer protection” model as renamed by Basel (2015) that is considered the standard in the field of risk management in general and credit risk management in particular Also studying this model, Tammenga (2020) approached from the perspective of assessing the suitability of the model when using modern tools such as artificial intelligence

Nguyen Van Tien (2015) and Ghosh (2012) researched on the banking governance model in which the basic point of the credit risk management model is determined as the independence between the business division, risk management division and internal processing division while still ensuring the centralized credit management process

Research by Le Thi Huyen Dieu (2010) presented an overview of appropriate credit risk management models in the context of commercial banks

in Vietnam On the same basis, the research by Nguyen Bich Ngan (2020) simulated the portfolio risk management model according to the Foundation Internal Ratings- Based Approach (FIRB) of Basel

In addition, there are some studies on credit risk management at a particular bank that can be mentioned such as those by Tran Khanh Duong (2019), Nguyen Quang Hien (2016), Le Thi Hanh (2017) and Nguyen Nhu Duong (2018)

1.2 Researches on credit risk assessment

Koulafetis (2017) comprehensively studied credit risk measurement models, clearly specifying portfolio risk measurement models, from the ones recommended by Basel according to the standard approach (SA), the Foundation Internal Ratings- Based Approach (FIRB) and the Advanced Internal Ratings- Based Approach (AIRB) to those developed by the world’s oldest financial institutions such as CreditMetrics by JP Morgan (1997), KMV

by Moody's (2002), CreditRisk+ by Credit Suise (1997), CreditPortfolioView developed by Wilson (1997) and used by McKinsey

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according to the Basel's approach include those by Acharya et al (2006), Carey & Gordy (2007), Hibbeln (2010), Engelmann & Rauhmeier (2006), Witzany (2017), and Jacob (2010)

In this thesis, the author inherits the theoretical arguments about credit risk management model based on the Advanced Internal Ratings- Based Approach (AIRB) to build an internal credit rating system for Vietnam Bank for Agriculture and Rural Development

1.2.1 Researches on measurement of probability of default

Some typical international researches include: Altman (1968); Arminger

et al (1997); Vasanthi & Raja (2006); Autio et al (2009); Kocenda & Vojtek (2011); and Nwachukwu (2013)

Besides, there are researches using data from Vietnam such as: Dinh and Kleimeier (2007); Tra Pham and Lensink (2008); Linh et al (2020); and Thu et

al (2020)

1.2.2 Researches on loss given default (LGD)

Table 1.1: Summary of researches on LGD

Research Data Coun

try

Perio

d

Macroeconom

ic variable

Model Influence

of macro factors

Altman et

al (2005) 1000 observ

ations

USA

1982-2001 GDP Multivariate regression No influence

Bastos

ations

Portu

Tree (DT)

-

Bellotti

and Crook

(2011)

55,000 observ ations

1999-2005 UK interest rates, bank

unemployment

income index

Linear regression, logit (LR), tobit

Decision Tree (DT)

All macro variables

Caselli et

al (2008)

11,649 observ ations

Italy

1990-2004

GDP, employment rate, household consumption, annual

investment,

Multivariate regression

GDP, employme

nt rate