Application of artificial intelligence in credit risk management at Vietnam Bank for Agriculture and Rural Development

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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.

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Nội dung Text: Application of artificial intelligence in credit risk management at Vietnam Bank for Agriculture and Rural Development

MINISTRY OF EDUCATION STATE BANK OF VIETNAM AND TRAINING ------------------ NGUYEN TIEN HUNG SUMMARY OF DOCTORAL THESIS APPLICATION OF ARTIFICIAL INTELLIGENCE IN CREDIT RISK MANAGEMENT AT VIETNAM BANK FOR AGRICULTURE AND RURAL DEVELOPMENT HANOI – 2022
MINISTRY OF EDUCATION STATE BANK OF VIETNAM AND TRAINING ------------------ NGUYEN TIEN HUNG SUMMARY OF DOCTORAL THESIS APPLICATION OF ARTIFICIAL INTELLIGENCE IN CREDIT RISK MANAGEMENT AT VIETNAM BANK FOR AGRICULTURE AND RURAL DEVELOPMENT Specialization: Finance - Banking Code: 9340201 Scientific supervisor: Supervisor 1: Dr. Bui Tin Nghi Supervisor 2: Assoc. Prof. Dr. Nguyen Duc Trung HANOI – 2022
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 I
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 II
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 III
GENERAL INTRODUCTION 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 1
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. 2
5. NEW FINDINGS OF THE THESIS 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 3
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. 4
Many studies focusing on analyzing credit risk measurement models 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 Perio Macroeconom Model Influence try d ic variable of macro factors Altman et 1000 USA 1982- GDP Multivariate No al (2005) observ 2001 regression influence ations Bastos 374 Portu 1995- --- Logit (LR), --- (2010) observ gal 2000 Decision ations Tree (DT) Bellotti 55,000 UK 1999- UK bank Linear All macro and Crook observ 2005 interest rates, regression, variables (2011) ations unemployment logit (LR), rate, UK tobit income index Decision Tree (DT) Caselli et 11,649 Italy 1990- GDP, Multivariate GDP, al (2008) observ 2004 employment regression employme ations rate, household nt rate consumption, annual investment, 5
Dermine 374 Portu 1995- GDP, Logit (LR) No and observ gal 2000 frequency of influence Carvalho ations default by (2006) sector Khieu et al 1364 USA 1987- GDP OLS, Logit GDP (2012) observ 2007 ations Rosh and 1653 USA 1982- GDP Tobit GDP Sheule observ 2009 (2012) ations Source: Synthesized by the author 1.2.3. Researches on default risk Valvonis's research (2008) showed that the determination of EAD depends largely on the credit product and the content of the commitment between the bank and the customer. EAD can be divided into two groups: fixed group and variable group. For the group with variable loan balance, EAD needs to be estimated according to the model. The studies by Barakova and Parthasarathy (2013), Leow and Crook (2016), Tong et al (2016), Luo and Murphy (2020) examined a variety of EAD affecting factors and EAD forecasting techniques on diverse data sets from individual customers to business customers. 1.3. Researches on artificial intelligence model in credit risk management. International researches on artificial intelligence in the credit field were carried out in a variety of ways, from different aspects, including general theoretical studies on artificial intelligence and building empirical models. Table 1.2: Summary of researches on artificial intellectual models in credit risk measurement Research Date Model Best model West - Germany, 1000 MDA, LR, Germany: MOE (correct (2000) observations (30% of KNN, NN classification ratio bad debts) (MLP, 78.6%) - Australia: 690 MOE, RBF, Australia: RBF (correct observations (56 % of LVQ, FAR); classification ratio bad debts) 88.78%), MLP (correct classification ratio 87.68%) Desai et al - USA: 962 NN (MLP, LR (correct classification 6
(1996) observations (18.42% MNN), ratio 81.7%) of bad debts), 918 MDA, LR MLP (correct observations (25.98% classification ratio of bad of bad debts), 853 debts 42.08%) observations (21.15% of bad debts) Abdou Egypt: 581 NN (MLP, MLP (correct (2008) observations (25.5% of PNN) classification ratio bad debts) 94.84%) Brown et Germany, Australia, 03 MDA, LR, Best SVM with the al (2012) other datasets DT, KNN, dataset of 30% of bad SVM, NN, debts RF, GB Best RF with the dataset of 1% of bad debts Tsai and - Germany, 1000 MLP, Germany: MMLP Wu observations (30% of MMLP (correct classification (2008) bad debts) ratio 83.38%) - Australia: 690 Australia: MLP (correct observations (56 % of classification ratio bad debts) 97.32%) - Japan: 690 Japan: MLP (correct observations (56 % of classification ratio bad debts) 87.94%) Altman 66 observations (48.4% MDA 95% (1968) of bankruptcy) Wiginton 1908 observations ( MDA, LR LR (correct classification (1980) 41.8 % of bad debts) ratio 61.84%) Tang et al - Australia: 690 NN (PNN, Australia: PNN (85.64%) (2018) observations (56 % of MLP) bad debts) Japan: PNN (85.54%) - Japan: 690 observations (56 % of bad debts) Zhao et al - Germany, 1000 MLP 87% (2015) observations (30% of bad debts) Source: Synthesized by the author 7
1.4. Research gap After doing the literature review, the thesis identified a number of research gaps as follows: First, domestic researches have mentioned some aspects of risk management such as organizational models, principles, management processes, and measurement methods, but only few researches on the use artificial intelligence in credit risk management. So far, there is no research on the theoretical framework for building artificial intelligence models in credit risk management. Second, almost neither international nor domestic studies have comprehensively studied the use of artificial intelligence in credit risk measurement according to the Basel's advanced approach. Third, there are no domestic studies on the application of artificial intelligence in risk management at a particular bank. This is the practical gap that the thesis will focus on. 8
CHAPTER 2: THEORETICAL BASIS FOR APPLICATION OF ARTIFICIAL INTELLIGENCE IN CREDIT RISK MANAGEMENT 2.1. Theoretical basis of credit risk management 2.1.1. Concept of credit risk management Credit risk management can be expressed in different forms, but the concepts and views are generally about the nature of credit risk: it is the process of formulating and implementing risk management policies and strategies regarding risk identification and measurement, risk prevention, risk handling and risk control measures in order to maximize profits within acceptable risk levels. 2.1.2. Contents of credit risk management 2.1.2.1. Principles of credit risk management To ensure effective credit risk management, Basel (2000) set out 17 principles of credit risk management, divided into five groups: establishing an appropriate credit risk environment; operating under an effective credit granting process; maintaining appropriate credit management, measurement and monitoring processes; ensuring a safe control system against credit risks; and the role of supervisors. 2.1.2.2. Credit risk management models at commercial banks * Credit risk management models according to the organizational structure Centralized credit risk management model This model separates credit activities at a branch and a head office. The branch only performs business/ sales/ customer relations functions, while the head office performs credit risk management/ credit appraisal/ analysis and credit approval as well as operational support functions. Distributed credit risk management model The distributed risk management model gives each bank branch a position, which is very independent from the head office as a subsidiary bank in the parent bank. This model does not separate between risk management, business and operations functions. In particular, the bank's credit department fully performs 3 functions and is responsible for all stages of loan preparation. *Three-layer risk management organization model 9
The three-layer protection model is built with the aim of distinguishing risk management tasks by different functional departments in the bank. This model provides a simple and effective approach, to enhance communication between risk management and control by clarifying the roles and responsibilities of stakeholders. One advantage of this model is that it is suitable for all credit institutions regardless of the size and complexity of business operations. 2.1.2.3. Credit risk management process The credit risk management process is built in four stages: identification, measurement, use of risk management tools and risk reporting (Ghosh, 2012). 2.2. Theoretical basis for application of artificial intelligence in credit risk management 2.2.1. Overview of artificial intelligence. Artificial intelligence (AI) can be understood as the intelligence demonstrated by machines. In other words, the term AI is used when a machine mimics human “cognitive” functions, such as “learning” and “problem solving”. The main applications of AI mentioned include: analysis, automated advice, process automation and reporting, in which the applications of risk analysis including credit risk are considered as bringing the greatest benefit to the banks. 2.2.2. Artificial intelligence in credit risk management According to the guidance of the World Bank (2019), artificial intelligence in credit risk management is divided into 3 categories according to training algorithms including: supervised learning, unsupervised learning and other related techniques. Table 2.1: Classification of artificial intelligence models AI for supervised AI for unsupervised AI for other related learning learning techniques  Decision tree and  K-means clustering  Automated Feature mixed models.  Hierarchical Engineering - AFE  Support vector clustering  Reinforcement machine Learning - RL 10
 Neural network  Natural Language Processing - NLP Source: Synthesized by the author based on the WB guidelines (2019) With the diversity of the above algorithms, artificial intelligence can participate in almost contents of the credit risk management process. The risk management process is normally established in 4 basic steps including: identification, measurement, use of risk management tools, monitoring and reporting. In addition, the use of artificial intelligence also allows banks to control credit risk right at the step of customer search. Figure 2.1: Artificial intelligence in credit risk management Source: Synthesized by the author 2.2.3. Measurement framework applied to artificial intelligence model in credit risk management. Basel II introduced a series of credit risk approaches with increasing complexity and internalization including: Standard Approach (SA), Foundation Internal Ratings- Based Approach (FIRB), and the Advanced Internal Ratings- Based Approach (AIRB). In the research scope of the thesis, the author uses the measurement framework according to the advanced approach of Basel II in which the PD, LGD and EAD models are estimated through historical data of the bank. 11
2.2.4. Data for artificial intelligence models in credit risk management. *Individual: age, gender, marriage, Legal residence, education, employment, informatio insurance, dependents, membership status n about in related organizations and groups customers *Enterprise: type, field of business, address, size PD Informatio Assets, capital, income, expenditure, n about financial performance, manageability, financial solvency status Information Limit, outstanding balance, interest rate, about the term, disbursement method, repayment loan method LGD Information Types of services used at the bank, usage about transaction behavior & habits history Information Type, form of ownership, value, volatility, about Collaterals commitment related to collaterals EAD Bad debt & overdue debt handling policy, Internal information credit control & supervision policy, of the Bank collateral handling policy and customer support policy GDP, import-export balance, Outside Information unemployment rate, inflation index, business industry & market index Figure 2.2: Data used in PD, LGD and EAD models Source: Synthesized by the author 12
2.2.5. Criteria to evaluate the effectiveness of artificial intelligence models in credit risk measurement. 2.2.5.1. For the PD model Correct classification ratio matrix This is one of the most commonly used criteria in the researches on credit rating models (Abdou, 2009; Zheng et al. 2004). Training data Test data All data CCG CCB Aver CCG CCB Aver CCG CCB Aver Model % % age % % age % % age ratio ratio ratio A B C In particular, CCG% is the correct classification ratio of good debts, representing the number of correctly classified good debts out of the total number of actual good debts; CCB% is the correct classification ratio of bad debts, representing the number of correctly classified bad debts out of the total number of actual bad debts; The average correct classification ratio is calculated based on the total number of correctly classified cases out of the total number of observations. Misclassification cost The misclassification cost of each model will be determined according to the West (2000) formula given as follows: Misclassification cost = 𝐶1 x𝑃1 x 𝜋1 + 𝐶2 x 𝑃2 x 𝜋2 Where: 𝐶1 and 𝐶2 are misclassification costs for type I and II errors, respectively; 𝑃1 and 𝑃2 are the probabilities of type I and II errors; 𝜋1 and 𝜋2 are the ratios of bad debts and good debts. ROC (Receiver operating characteristic) curve ROC is a commonly used graph in binary classification models. In the field of credit ratings, this curve is generated by representing the correct 13
classification ratio of bad debts (sensitivity) based on the misclassification ratio of good debts (– 1-specificity) (Abdou, 2009). 2.2.5.2. For the LGD and EAD models For the LGD and EAD models, the dependent variable is a continuous variable, so error measures will be used to evaluate the effectiveness of the model (Scandizzo, 2016). One of the most used indicators to evaluate artificial intelligence models is RSE (Relative squared error) using the following formula: Where: 𝑦̂𝑖 and 𝑦𝑖 are the predicted and observed values; mean (y) is the mean observed value. 2.2.6. Conditions for application of artificial intelligence in credit risk management. Many researches have shown significant advantages of artificial intelligence over traditional methods in credit risk management. However, for application of these models in practice, it requires comprehensive attention on the conditions and requirements set forth with these models. Technology Credit Benefits Regulation related Competition factors Ability to "Race" Computation reduce costs, Regulations Availability among credit al increase on safety, of institutions Improvemen income, transparency infrastructur ts, increase risk and data e and data Algorithms, management used. for adoption Costs Figure 2.3: Conditions for deployment of artificial intelligence model Source: FSB (2017) 14
2.3. International experience in research and application of artificial intelligence in credit risk management. 2.3.1. Experience from the UK. About strategy for research and application of artificial intelligence Investment and research in artificial intelligence in the UK boomed in the early years of the 21st century due to the strong development of Machine learning technology and hardware technology. Artificial intelligence applications are utilized in most stages from customer contact (front-office) activities to internal (back-office) activities with focus on anti-money laundering and customer related services, credit risk management, valuation, insurance and underwriting. About selection of artificial intelligence models Regulatory agencies such as the Bank of England, the Prudential Regulation Authority (PRA) and the Financial Conduct Authority (FCA) are completely neutral on technology application in financial institutions, and no restrictions or bans are imposed on the artificial intelligence models applied. According to statistics, the artificial intelligence model based on Decision Tree (DT) is most commonly used, followed by the natural language processing and neural network approaches. About data for artificial intelligence models Financial institutions in the UK use a variety of data types for artificial intelligence models including: structured data, semi-structured data and unstructured data. Structured data refers to data contained in columns and rows whose elements can be linked by predefined fields. About the process of applying artificial intelligence models into practice The process of applying the artificial intelligence model introduced by the Bank of England includes 5 steps as follows: data collection and input; data selection and classification; model running; model appraisal; implementation and safeguards. About risks and challenges to be solved when applying artificial intelligence models 15