IELTS Reading: AI và Ngành Dịch Vụ Tài Chính – Đề Thi Mẫu Có Đáp Án Chi Tiết

Mở Bài

Trí tuệ nhân tạo (AI) đang tạo ra những thay đổi sâu sắc trong ngành dịch vụ tài chính toàn cầu, từ ngân hàng số đến quản lý đầu tư tự động. Chủ đề “How Does AI Affect The Financial Services Industry?” xuất hiện ngày càng thường xuyên trong kỳ thi IELTS Reading, đặc biệt là từ năm 2020 trở lại đây khi công nghệ tài chính phát triển mạnh mẽ.

Bài viết này cung cấp một bộ đề thi IELTS Reading hoàn chỉnh với 3 passages tăng dần độ khó, từ Easy (Band 5.0-6.5) đến Hard (Band 7.0-9.0). Bạn sẽ được luyện tập với 40 câu hỏi đa dạng theo đúng format thi thật, bao gồm Multiple Choice, True/False/Not Given, Matching và nhiều dạng khác. Mỗi câu hỏi đều có đáp án chi tiết kèm giải thích về vị trí thông tin, kỹ thuật paraphrase và chiến lược làm bài hiệu quả.

Đề thi này phù hợp cho học viên từ band 5.0 trở lên, giúp bạn làm quen với văn phong học thuật về công nghệ tài chính, mở rộng vốn từ vựng chuyên ngành và rèn luyện kỹ năng đọc hiểu trong môi trường thi thực tế. Hãy dành 60 phút để hoàn thành bài test này như một buổi thi thật!

1. Hướng Dẫn Làm Bài IELTS Reading

Tổng Quan Về IELTS Reading Test

IELTS Reading Test kéo dài 60 phút với 3 passages và tổng cộng 40 câu hỏi. Điểm số được tính dựa trên số câu trả lời đúng, không bị trừ điểm khi sai. Mỗi passage có độ dài khoảng 700-1000 từ và độ khó tăng dần.

Phân bổ thời gian khuyến nghị:

• Passage 1: 15-17 phút (13 câu hỏi)
• Passage 2: 18-20 phút (13 câu hỏi)
• Passage 3: 23-25 phút (14 câu hỏi)

Lưu ý dành 2-3 phút cuối để kiểm tra và chuyển đáp án vào answer sheet. Đừng mắc kẹt quá lâu ở một câu hỏi – hãy đánh dấu và quay lại sau.

Các Dạng Câu Hỏi Trong Đề Này

Đề thi mẫu này bao gồm 7 dạng câu hỏi phổ biến nhất trong IELTS Reading:

1. Multiple Choice – Chọn đáp án đúng từ 3-4 phương án
2. True/False/Not Given – Xác định thông tin đúng/sai/không được đề cập
3. Yes/No/Not Given – Xác định quan điểm tác giả
4. Matching Headings – Nối tiêu đề phù hợp với đoạn văn
5. Sentence Completion – Hoàn thành câu với từ trong bài
6. Matching Features – Nối thông tin với các đối tượng
7. Summary Completion – Hoàn thành đoạn tóm tắt

2. IELTS Reading Practice Test

PASSAGE 1 – The Digital Revolution in Banking

Độ khó: Easy (Band 5.0-6.5)

Thời gian đề xuất: 15-17 phút

The banking industry has undergone a remarkable transformation in recent years, largely due to the integration of artificial intelligence (AI) into its operations. Traditional banks, once characterised by long queues and paperwork-intensive processes, are now embracing digital solutions that make financial services more accessible and efficient for customers worldwide.

AI-powered chatbots have become one of the most visible changes in modern banking. These virtual assistants are available 24 hours a day, seven days a week, helping customers with routine inquiries such as checking account balances, transferring money, or reporting lost cards. Unlike human staff, chatbots never need breaks and can handle thousands of simultaneous conversations. This has significantly reduced waiting times for customers and allowed bank employees to focus on more complex issues that require human judgment and empathy.

Another area where AI has made a substantial impact is in fraud detection. Financial institutions lose billions of dollars annually to fraudulent activities, but AI systems are proving to be powerful allies in this battle. These systems analyse millions of transactions in real-time, looking for unusual patterns that might indicate fraud. For example, if a credit card that is normally used in London suddenly shows activity in Tokyo, the AI system can flag this transaction for review or even temporarily block the card until the customer confirms the purchase. Traditional rule-based systems could never achieve this level of sophistication and speed.

Personal financial management has also been revolutionised by AI technology. Many banking apps now offer intelligent budgeting tools that track spending habits and provide customised advice on how to save money. These tools can analyse a customer’s transaction history, identify recurring expenses, and suggest areas where they might reduce spending. Some advanced systems can even predict when a customer might run low on funds and send proactive alerts to help them avoid overdraft fees. This type of personalised service was once only available to wealthy clients with dedicated financial advisors, but AI has democratised access to such guidance.

The loan application process has been significantly streamlined through AI implementation. In the past, applying for a loan could take days or even weeks, involving multiple meetings with bank officers and extensive documentation. Today, AI systems can assess a loan application in minutes by analysing the applicant’s credit history, income stability, and other relevant factors. This not only speeds up the process but also reduces human bias in lending decisions. The algorithms evaluate applications based purely on data and predefined criteria, potentially making the system fairer for all applicants.

However, the rise of AI in banking is not without concerns and challenges. Many customers, particularly older generations, feel uncomfortable with the idea of machines handling their money and personal information. They miss the human interaction that traditional banking provided and worry about what happens when technology fails. Data privacy is another significant concern. Banks collect enormous amounts of personal and financial data to train their AI systems, and customers are increasingly aware of the risks associated with data breaches and unauthorised access. Regulators worldwide are working to establish strict guidelines about how financial institutions can collect, store, and use customer data.

Despite these challenges, the trend towards AI integration in banking appears irreversible. The benefits in terms of efficiency, accessibility, and cost reduction are simply too significant for banks to ignore. As the technology continues to evolve and mature, we can expect to see even more innovative applications that will further transform how we manage our finances. The key will be finding the right balance between technological advancement and maintaining the human touch that many customers still value.

Questions 1-6

Do the following statements agree with the information given in the passage?

Write:

• TRUE if the statement agrees with the information
• FALSE if the statement contradicts the information
• NOT GIVEN if there is no information on this

1. AI chatbots in banking can handle multiple customer conversations at the same time.
2. Traditional banks had faster service than modern digital banks.
3. AI fraud detection systems can analyse transactions more quickly than rule-based systems.
4. Wealthy clients were the first to receive personalised budgeting advice from AI systems.
5. AI loan assessment systems completely eliminate all forms of discrimination in lending.
6. Older customers generally prefer traditional banking methods to AI-powered services.

Questions 7-10

Complete the sentences below.

Choose NO MORE THAN TWO WORDS from the passage for each answer.

7. AI chatbots allow bank employees to concentrate on __ that need human skills.
8. AI systems look for __ in transactions that might suggest fraudulent activity.
9. Advanced banking apps can predict when customers might __ and send warnings.
10. Regulators are creating __ about how banks can handle customer information.

Questions 11-13

Choose the correct letter, A, B, C or D.

11. According to the passage, what is one advantage of AI loan processing?

• A. It requires more documentation than traditional methods
• B. It reduces the influence of personal prejudice in decisions
• C. It takes longer but is more accurate
• D. It only works for wealthy applicants

12. The main concern about AI in banking mentioned in the passage is:

• A. The high cost of implementation
• B. The lack of human interaction and data security
• C. The complexity of the technology
• D. The resistance from bank employees

13. What does the author suggest about the future of AI in banking?

• A. It will completely replace human bankers
• B. It will likely stop developing soon
• C. It will continue to grow despite challenges
• D. It will only be used by large banks

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PASSAGE 2 – AI-Driven Investment Strategies and Wealth Management

Độ khó: Medium (Band 6.0-7.5)

Thời gian đề xuất: 18-20 phút

The advent of artificial intelligence has fundamentally altered the landscape of investment management and wealth advisory services. What was once an exclusive domain reserved for high-net-worth individuals with access to sophisticated financial advisors has now become increasingly democratised through robo-advisors and algorithmic trading platforms. These AI-driven systems are not merely automating existing processes; they are redefining the very nature of investment strategy formulation and execution.

Robo-advisors, which emerged prominently in the aftermath of the 2008 financial crisis, represent one of the most tangible manifestations of AI in wealth management. These digital platforms utilise complex algorithms to provide automated, algorithm-driven financial planning services with minimal human supervision. Upon registration, users complete a detailed questionnaire about their financial situation, risk tolerance, and investment goals. The AI system then constructs a diversified portfolio tailored to the individual’s profile, typically consisting of low-cost exchange-traded funds (ETFs) across various asset classes. What distinguishes these systems from traditional investment approaches is their ability to continuously monitor and rebalance portfolios in response to market fluctuations and changes in the user’s circumstances, all while maintaining optimal tax efficiency through strategies like tax-loss harvesting.

The proliferation of robo-advisors has had profound implications for the wealth management industry. Research from Impact of automation on retail sales suggests that similar trends are occurring across different sectors. Traditional financial advisors, who typically charge annual fees ranging from 1% to 2% of assets under management, have found themselves competing with robo-advisors that often charge less than 0.5%. This fee compression has forced many human advisors to reconsider their value proposition and increasingly focus on providing holistic financial planning and emotional support during market volatility – aspects where human judgment and empathy remain irreplaceable.

Algorithmic trading, another AI-powered innovation, has transformed the dynamics of financial markets themselves. These systems, often employing machine learning techniques, can process vast quantities of market data, news articles, social media sentiment, and even satellite imagery to identify trading opportunities in milliseconds. High-frequency trading (HFT) firms, which account for a substantial portion of daily trading volume in major stock exchanges, rely entirely on AI algorithms that can execute thousands of trades per second. Such systems can detect minute price discrepancies across different markets and exploit them before human traders even become aware of their existence.

The sophistication of these AI trading systems has reached remarkable levels. Some algorithms employ natural language processing (NLP) to analyse corporate earnings calls, regulatory filings, and news reports, attempting to gauge market sentiment and predict price movements before they occur. Others utilise computer vision to process alternative data sources – for instance, analysing satellite images of retail parking lots to estimate a company’s quarterly sales figures before official announcements. This unprecedented access to information and computational power has created what some economists call an “information asymmetry” between institutions employing cutting-edge AI and traditional market participants, similar to challenges seen in Challenges of protecting intellectual property in the digital age.

Yet the ascendancy of AI in investment management is not without controversy and risks. Critics argue that algorithm-driven trading contributes to increased market volatility and has been implicated in several “flash crashes” – sudden, severe market downturns that occur and recover within minutes. The interconnectedness of AI trading systems means that a malfunction in one algorithm can trigger a cascade of automated responses across the market, potentially causing systemic instability. The 2010 Flash Crash, during which the Dow Jones Industrial Average plummeted nearly 1,000 points in minutes before recovering, highlighted the potential dangers of over-reliance on automated trading systems.

Furthermore, the “black box” nature of many AI investment systems raises important questions about accountability and transparency. Machine learning models, particularly deep neural networks, often make decisions through processes that even their creators struggle to fully explain. This opacity becomes problematic when algorithms make investment recommendations or trading decisions that result in significant losses. Who bears responsibility when an AI system’s opaque reasoning leads to financial harm – the developers, the financial institution deploying it, or the users who relied on it?

Regulatory bodies worldwide are grappling with these challenges, attempting to develop frameworks that encourage innovation while safeguarding market integrity and protecting investors. The European Union’s Markets in Financial Instruments Directive (MiFID II) and similar regulations in other jurisdictions now require greater transparency in algorithmic trading and impose stringent testing requirements for trading algorithms. However, the pace of technological advancement often outstrips regulatory capacity, creating an ongoing cat-and-mouse game between regulators and financial technologists.

Hệ thống giao dịch thuật toán AI phân tích dữ liệu thị trường chứng khoán thời gian thực

Questions 14-18

Choose the correct letter, A, B, C or D.

14. According to the passage, robo-advisors primarily differ from traditional advisors in:

• A. The types of investments they recommend
• B. Their ability to continuously adjust portfolios automatically
• C. The questionnaires they use
• D. Their focus on high-net-worth individuals

15. The passage suggests that traditional financial advisors are now focusing more on:

• A. Reducing their fees to compete with robo-advisors
• B. Learning how to programme algorithms
• C. Providing comprehensive planning and emotional guidance
• D. Working exclusively with wealthy clients

16. High-frequency trading systems can:

• A. Replace human traders completely
• B. Predict market crashes accurately
• C. Execute trades faster than humans can perceive
• D. Guarantee profits for investors

17. What is the “black box” problem mentioned in the passage?

• A. The physical appearance of AI systems
• B. The difficulty in understanding how AI makes decisions
• C. The cost of implementing AI systems
• D. The lack of data for AI systems

18. The passage indicates that regulatory responses to AI in finance are:

• A. Completely effective in controlling risks
• B. Struggling to keep pace with technological developments
• C. Unnecessary and restrictive
• D. Mostly focused on developing countries

Questions 19-23

Complete the summary below.

Choose NO MORE THAN TWO WORDS from the passage for each answer.

Robo-advisors have become popular since the 2008 financial crisis, offering automated investment services. Users answer questions about their financial goals and (19) __, and the AI creates a suitable portfolio. These systems can perform (20) __ to maintain ideal investment proportions and use strategies like (21) __ to reduce tax obligations. Traditional advisors typically charge between 1% and 2% annually, but robo-advisors often charge below (22) __. This price difference has forced human advisors to emphasise their ability to provide (23) __ during uncertain market conditions.

Questions 24-26

Do the following statements agree with the claims of the writer in the passage?

Write:

• YES if the statement agrees with the claims of the writer
• NO if the statement contradicts the claims of the writer
• NOT GIVEN if it is impossible to say what the writer thinks about this

24. AI trading algorithms have completely eliminated market volatility.
25. The 2010 Flash Crash demonstrated the risks of excessive dependence on automated systems.
26. Robo-advisors will eventually make human financial advisors completely obsolete.

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PASSAGE 3 – The Socioeconomic Ramifications of AI Implementation in Financial Services

Độ khó: Hard (Band 7.0-9.0)

Thời gian đề xuất: 23-25 phút

The pervasive integration of artificial intelligence throughout the financial services sector represents far more than a mere technological evolution; it constitutes a fundamental restructuring of the economic architecture underpinning modern capitalism. While much discourse has centred on the operational efficiencies and consumer benefits derived from AI implementation, insufficient attention has been devoted to the profound socioeconomic implications that accompany this transformation. These ramifications extend across multiple dimensions – from labour market disruption and wealth concentration to financial inclusion and systemic risk reconfiguration – each warranting rigorous scrutiny if policymakers are to navigate this transition judiciously.

The employment consequences of AI-driven automation in financial services present a paradox of creative destruction that defies simplistic characterisation. Conventional wisdom suggests that technological advancement inevitably displaces human labour, and indeed, empirical evidence supports substantial job losses in certain categories. Bank tellers, loan officers, compliance analysts, and entry-level traders have all experienced significant workforce reductions as their functions become increasingly algorithmised. The trends observed in Effects of automation on job displacement mirror similar patterns across the financial sector. Research by the International Labour Organization estimates that up to 40% of back-office banking positions in developed economies could be rendered redundant by AI systems within the next decade. This displacement disproportionately affects middle-skilled workers, potentially exacerbating income inequality and contributing to the hollowing out of the economic middle class – a phenomenon with far-reaching political ramifications.

However, this narrative of displacement obscures a more nuanced reality. The same technological forces eliminating certain roles simultaneously create demand for new skill sets and occupational categories. Data scientists, AI ethicists, algorithm auditors, cybersecurity specialists, and human-AI interaction designers represent emerging professions that barely existed a decade ago. The critical question is not whether AI will eliminate jobs in aggregate, but whether the educational infrastructure and retraining mechanisms exist to facilitate workforce transition from declining to emerging occupations. Unfortunately, current evidence suggests a significant temporal and skill mismatch – workers displaced by automation often lack the specialised technical expertise required for newly created positions, and reskilling initiatives have proven inadequate to bridge this chasm. This structural unemployment threatens to create a permanent underclass of economically marginalised individuals, with destabilising social consequences.

The relationship between AI in financial services and wealth inequality merits particular attention, as it operates through multiple causal pathways. First, algorithm-driven investment platforms have demonstrably improved returns for users by reducing fees, optimising tax efficiency, and providing access to sophisticated strategies previously available only to the ultra-wealthy. This democratisation of financial expertise theoretically promotes wealth equalisation. Paradoxically, however, the most substantial beneficiaries of AI-enhanced financial services tend to be those already possessing considerable assets. The “Matthew effect” – whereby initial advantages compound over time – manifests clearly in this context. Wealthy individuals and institutions can afford the most advanced AI tools, access proprietary algorithms developed by leading technology firms, and employ teams of specialists to optimise their AI-human collaborative strategies. Meanwhile, less affluent users typically interact with commoditised, less sophisticated versions of AI financial services.

Moreover, the concentration of AI development capability within a small number of technology giants – primarily based in the United States and China – creates new forms of economic hegemony. These firms effectively control the algorithmic infrastructure upon which global financial services increasingly depend, granting them extraordinary leverage over the financial ecosystem. This concentration raises concerns about anti-competitive practices, data monopolisation, and the potential for technological rent-seeking that could siphon economic value from the broader economy into the coffers of a few dominant platforms. The situation shares similarities with challenges seen in Blockchain in global trade, where technological control can create new power asymmetries.

Financial inclusion – the extent to which individuals and businesses can access affordable, appropriate financial services – presents another ambiguous dimension of AI implementation. Proponents emphasise that AI can extend financial services to previously underserved populations, particularly in developing nations where traditional banking infrastructure remains sparse. Mobile-based AI platforms can assess creditworthiness using alternative data sources such as mobile phone usage patterns, social network analysis, and even psychometric testing, enabling lending to individuals lacking formal credit histories. This has facilitated microfinance initiatives that have demonstrably improved economic opportunities for marginalised communities.

Conversely, AI systems can perpetuate and amplify existing biases embedded in historical data. If training datasets reflect discriminatory lending practices of the past, algorithms may inadvertently institutionalise these biases, creating algorithmic redlining that systematically disadvantages certain demographic groups. Research has documented cases where AI credit-scoring systems assign lower ratings to applicants from minority communities or certain geographic areas, not due to any objective risk assessment but because the underlying data reflects historical discrimination. Unlike human decision-makers, who can be educated about bias and held accountable, AI systems operate with an aura of objectivity that may make their discriminatory outcomes more difficult to identify and challenge.

Hệ thống ngân hàng di động AI mang đến dịch vụ tài chính cho cộng đồng chưa được phục vụ

The systemic risk profile of the financial system has been fundamentally altered by AI integration, creating both risk mitigation and novel vulnerabilities. On one hand, AI-powered risk management systems provide unprecedented capabilities for monitoring portfolio exposures, stress-testing scenarios, and identifying emerging threats. These systems can process granular data across vast asset holdings in real-time, enabling more responsive risk management than was previously conceivable. Regulatory technology (RegTech) applications of AI have similarly enhanced supervisory capabilities, allowing regulators to monitor financial institutions more effectively and identify potential solvency issues or fraudulent activities before they metastasise into systemic crises.

However, AI simultaneously introduces opaque interdependencies and correlation risks that could amplify contagion during market dislocations. If numerous financial institutions deploy similar AI algorithms trained on comparable datasets, their systems may generate correlated trading strategies that exacerbate market movements during periods of stress. The phenomenon of “herding behaviour” – traditionally associated with human psychology – could be algorithmically replicated at far greater scale and speed. Furthermore, the complexity and opacity of advanced AI systems create challenges for crisis management. During financial emergencies, regulators and policymakers require rapid understanding of system dynamics to implement appropriate interventions. When key decisions are made by inscrutable algorithms, this diagnostic capability may be compromised, potentially delaying or misdirecting stabilisation efforts.

The governance frameworks necessary to address these multifaceted challenges remain underdeveloped. Effective oversight of AI in financial services requires interdisciplinary expertise spanning computer science, economics, law, ethics, and domain-specific financial knowledge – a combination rarely found within individual regulatory agencies. International coordination becomes imperative given the cross-border nature of financial services, yet regulatory harmonisation faces significant obstacles including divergent national interests, varying technological capabilities, and fundamentally different philosophies regarding the appropriate balance between innovation and prudential oversight. The tendency of the financial industry to regulatory arbitrage – relocating activities to jurisdictions with more permissive rules – further complicates governance efforts.

Questions 27-31

Choose the correct letter, A, B, C or D.

27. According to the passage, the main problem with job displacement due to AI is:

• A. The total number of jobs being eliminated
• B. The mismatch between lost jobs and new opportunities
• C. The resistance of workers to learn new skills
• D. The lack of jobs in data science

28. The “Matthew effect” in AI financial services refers to:

• A. A biblical principle applied to banking
• B. The tendency for initial wealth advantages to increase over time
• C. The equal distribution of AI benefits
• D. A specific algorithm developed by a person named Matthew

29. What does the passage suggest about AI and financial inclusion?

• A. AI universally improves access to financial services
• B. AI has no impact on financial inclusion
• C. AI can both improve and worsen financial inclusion
• D. AI only benefits people in developed countries

30. “Algorithmic redlining” refers to:

• A. Using red ink in financial documents
• B. Drawing lines on maps to mark territories
• C. Systematic discrimination through automated systems
• D. A specific programming technique

31. The passage suggests that AI creates systemic risk because:

• A. All banks might adopt similar algorithms leading to correlated behaviour
• B. AI systems are always less reliable than humans
• C. Regulators understand AI too well
• D. Financial institutions avoid using AI

Questions 32-36

Complete the summary using the list of words/phrases, A-L, below.

The implementation of AI in financial services has complex socioeconomic effects. While it eliminates jobs like bank tellers and (32) __, it creates new positions such as data scientists and (33) __. The problem is that displaced workers often lack the (34) __ needed for new roles. Regarding wealth inequality, AI can both help and harm. It provides better returns through reduced fees and (35) __, but wealthy individuals can access more advanced AI tools. The concentration of AI development in a few technology companies creates new forms of (36) __ in the financial system.

A. compliance analysts
B. technical expertise
C. financial advisors
D. AI ethicists
E. tax efficiency
F. economic hegemony
G. market volatility
H. customer service
I. regulatory frameworks
J. data monopolisation
K. investment strategies
L. algorithmic bias

Questions 37-40

Do the following statements agree with the claims of the writer in the passage?

Write:

• YES if the statement agrees with the claims of the writer
• NO if the statement contradicts the claims of the writer
• NOT GIVEN if it is impossible to say what the writer thinks about this

37. AI systems in finance are inherently objective and free from discrimination.
38. RegTech applications have improved regulators’ ability to monitor financial institutions.
39. International regulatory coordination for AI in finance is easily achievable.
40. The complexity of AI systems can hinder effective crisis management during financial emergencies.

3. Answer Keys – Đáp Án

PASSAGE 1: Questions 1-13

1. TRUE
2. FALSE
3. TRUE
4. NOT GIVEN
5. NOT GIVEN
6. TRUE
7. complex issues
8. unusual patterns
9. run low on funds
10. strict guidelines
11. B
12. B
13. C

PASSAGE 2: Questions 14-26

14. B
15. C
16. C
17. B
18. B
19. risk tolerance
20. rebalance portfolios
21. tax-loss harvesting
22. 0.5%
23. emotional support
24. NO
25. YES
26. NOT GIVEN

PASSAGE 3: Questions 27-40

27. B
28. B
29. C
30. C
31. A
32. A
33. D
34. B
35. E
36. F
37. NO
38. YES
39. NO
40. YES

4. Giải Thích Đáp Án Chi Tiết

Passage 1 – Giải Thích

Câu 1: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: AI chatbots, handle, multiple conversations, same time
• Vị trí trong bài: Đoạn 2, dòng 3-4
• Giải thích: Bài đọc nói rõ “can handle thousands of simultaneous conversations” – điều này paraphrase “multiple conversations at the same time”. Từ “simultaneous” có nghĩa là cùng một lúc.

Câu 2: FALSE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: traditional banks, faster service, modern digital banks
• Vị trí trong bài: Đoạn 1-2
• Giải thích: Đoạn 1 mô tả ngân hàng truyền thống có “long queues” (hàng đợi dài), trong khi AI đã “significantly reduced waiting times” (giảm đáng kể thời gian chờ). Điều này cho thấy ngân hàng hiện đại nhanh hơn, ngược lại với câu phát biểu.

Câu 3: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: AI fraud detection, analyse transactions, more quickly, rule-based systems
• Vị trí trong bài: Đoạn 3, câu cuối
• Giải thích: Câu “Traditional rule-based systems could never achieve this level of sophistication and speed” xác nhận rằng AI nhanh hơn hệ thống dựa trên quy tắc truyền thống.

Câu 6: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: older customers, prefer, traditional banking methods
• Vị trí trong bài: Đoạn 6, dòng 2-3
• Giải thích: Bài viết nói “Many customers, particularly older generations, feel uncomfortable with the idea of machines handling their money” và “They miss the human interaction that traditional banking provided”, cho thấy họ ưa thích phương pháp truyền thống hơn.

Câu 7: complex issues

• Dạng câu hỏi: Sentence Completion
• Từ khóa: bank employees, concentrate on, need human skills
• Vị trí trong bài: Đoạn 2, câu cuối
• Giải thích: “allowed bank employees to focus on more complex issues that require human judgment and empathy”

Câu 11: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: advantage, AI loan processing
• Vị trí trong bài: Đoạn 5
• Giải thích: Bài viết nói “reduces human bias in lending decisions” và “The algorithms evaluate applications based purely on data”, cho thấy AI giảm thiểu định kiến cá nhân (personal prejudice).

Câu 12: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: main concern, AI in banking
• Vị trí trong bài: Đoạn 6
• Giải thích: Đoạn này nêu hai mối quan tâm chính: “They miss the human interaction” và “Data privacy is another significant concern”, tương ứng với đáp án B.

Passage 2 – Giải Thích

Câu 14: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: robo-advisors, differ from, traditional advisors
• Vị trí trong bài: Đoạn 2
• Giải thích: “What distinguishes these systems from traditional investment approaches is their ability to continuously monitor and rebalance portfolios” – khả năng liên tục theo dõi và điều chỉnh danh mục đầu tư tự động là điểm khác biệt chính.

Câu 15: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: traditional financial advisors, focusing more on
• Vị trí trong bài: Đoạn 3
• Giải thích: “This fee compression has forced many human advisors to reconsider their value proposition and increasingly focus on providing holistic financial planning and emotional support” – tư vấn viên truyền thống giờ tập trung vào kế hoạch tài chính toàn diện và hỗ trợ cảm xúc.

Câu 19: risk tolerance

• Dạng câu hỏi: Summary Completion
• Vị trí trong bài: Đoạn 2, dòng 4
• Giải thích: “users complete a detailed questionnaire about their financial situation, risk tolerance, and investment goals”

Câu 24: NO

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: AI trading algorithms, completely eliminated, market volatility
• Vị trí trong bài: Đoạn 6
• Giải thích: Bài viết nói “algorithm-driven trading contributes to increased market volatility” – tăng biến động thị trường, không phải loại bỏ hoàn toàn, do đó câu phát biểu mâu thuẫn với quan điểm tác giả.

Câu 25: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: 2010 Flash Crash, demonstrated risks, excessive dependence
• Vị trí trong bài: Đoạn 6, câu cuối
• Giải thích: “The 2010 Flash Crash… highlighted the potential dangers of over-reliance on automated trading systems” – tác giả đồng ý rằng sự kiện này chứng minh rủi ro của sự phụ thuộc quá mức.

Passage 3 – Giải Thích

Câu 27: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: main problem, job displacement
• Vị trí trong bài: Đoạn 3
• Giải thích: “The critical question is not whether AI will eliminate jobs in aggregate, but whether the educational infrastructure and retraining mechanisms exist to facilitate workforce transition” và “temporal and skill mismatch” – vấn đề chính là sự không tương thích giữa việc làm bị mất và cơ hội mới.

Câu 28: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: Matthew effect
• Vị trí trong bài: Đoạn 4
• Giải thích: “The ‘Matthew effect’ – whereby initial advantages compound over time” – hiệu ứng này đề cập đến xu hướng lợi thế ban đầu tăng lên theo thời gian.

Câu 29: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: AI, financial inclusion
• Vị trí trong bài: Đoạn 6-7
• Giải thích: Đoạn 6 nói “AI can extend financial services to previously underserved populations”, trong khi đoạn 7 nói “AI systems can perpetuate and amplify existing biases” – cho thấy AI có thể cả cải thiện và làm xấu đi tình hình.

Câu 37: NO

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: AI systems, inherently objective, free from discrimination
• Vị trí trong bài: Đoạn 7
• Giải thích: Tác giả nói “AI systems can perpetuate and amplify existing biases” và “algorithms may inadvertently institutionalise these biases” – điều này mâu thuẫn với ý kiến rằng AI vốn khách quan và không có phân biệt đối xử.

Câu 38: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: RegTech, improved regulators’ ability, monitor
• Vị trí trong bài: Đoạn 8, giữa đoạn
• Giải thích: “Regulatory technology (RegTech) applications of AI have similarly enhanced supervisory capabilities, allowing regulators to monitor financial institutions more effectively” – tác giả đồng ý rằng RegTech đã cải thiện khả năng giám sát.

Câu 40: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: complexity of AI, hinder, crisis management
• Vị trí trong bài: Đoạn 8, cuối đoạn
• Giải thích: “When key decisions are made by inscrutable algorithms, this diagnostic capability may be compromised, potentially delaying or misdirecting stabilisation efforts” – tác giả cho rằng độ phức tạp của AI có thể cản trở quản lý khủng hoảng.

5. Từ Vựng Quan Trọng Theo Passage

Passage 1 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
remarkable	adj	/rɪˈmɑːkəbl/	đáng chú ý, xuất sắc	remarkable transformation	remarkable achievement, remarkable progress
integration	n	/ˌɪntɪˈɡreɪʃn/	sự tích hợp, hội nhập	integration of AI	system integration, seamless integration
accessible	adj	/əkˈsesəbl/	dễ tiếp cận, có thể truy cập	more accessible and efficient	easily accessible, readily accessible
simultaneous	adj	/ˌsɪmlˈteɪniəs/	đồng thời, cùng lúc	simultaneous conversations	simultaneous translation, simultaneous events
fraud detection	n phrase	/frɔːd dɪˈtekʃn/	phát hiện gian lận	fraud detection systems	fraud detection software, fraud detection techniques
sophisticated	adj	/səˈfɪstɪkeɪtɪd/	tinh vi, phức tạp	level of sophistication	sophisticated technology, sophisticated system
recurring	adj	/rɪˈkɜːrɪŋ/	tái diễn, lặp lại	recurring expenses	recurring problem, recurring payment
democratised	v	/dɪˈmɒkrətaɪzd/	dân chủ hóa	democratised access	democratise knowledge, democratise information
streamlined	v/adj	/ˈstriːmlaɪnd/	được sắp xếp hợp lý	significantly streamlined	streamlined process, streamlined system
data breach	n phrase	/ˈdeɪtə briːtʃ/	vi phạm dữ liệu, rò rỉ	risks of data breaches	prevent data breach, data breach incident
irreversible	adj	/ˌɪrɪˈvɜːsəbl/	không thể đảo ngược	appears irreversible	irreversible damage, irreversible change
innovative	adj	/ˈɪnəveɪtɪv/	đổi mới, sáng tạo	innovative applications	innovative solution, innovative approach

Passage 2 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
advent	n	/ˈædvent/	sự xuất hiện, khởi đầu	advent of AI	advent of technology, advent of digital age
fundamentally	adv	/ˌfʌndəˈmentəli/	về cơ bản, căn bản	fundamentally altered	fundamentally different, fundamentally change
robo-advisor	n	/ˈrəʊbəʊ ədˈvaɪzə/	cố vấn robot	robo-advisors emerged	use robo-advisor, robo-advisor platform
proliferation	n	/prəˌlɪfəˈreɪʃn/	sự tăng nhanh, gia tăng	proliferation of robo-advisors	rapid proliferation, nuclear proliferation
algorithmic trading	n phrase	/ˌælɡəˈrɪðmɪk ˈtreɪdɪŋ/	giao dịch thuật toán	algorithmic trading transformed	algorithmic trading system, algorithmic trading strategy
high-frequency trading	n phrase	/haɪ ˈfriːkwənsi ˈtreɪdɪŋ/	giao dịch tần số cao	high-frequency trading firms	high-frequency trading strategy
natural language processing	n phrase	/ˈnætʃrəl ˈlæŋɡwɪdʒ ˈprəʊsesɪŋ/	xử lý ngôn ngữ tự nhiên	employing NLP	NLP techniques, NLP algorithm
information asymmetry	n phrase	/ˌɪnfəˈmeɪʃn əˈsɪmətri/	bất cân xứng thông tin	created information asymmetry	reduce information asymmetry, information asymmetry exists
flash crash	n phrase	/flæʃ kræʃ/	sụp đổ nhanh (thị trường)	several flash crashes	flash crash incident, caused flash crash
black box	n phrase	/blæk bɒks/	hộp đen, khó hiểu	black box nature	black box system, black box approach
accountability	n	/əˌkaʊntəˈbɪləti/	trách nhiệm giải trình	questions about accountability	ensure accountability, lack of accountability
opacity	n	/əʊˈpæsəti/	sự mờ đục, không minh bạch	opacity becomes problematic	financial opacity, opacity of system
regulatory bodies	n phrase	/ˈreɡjələtəri ˈbɒdiz/	cơ quan quản lý	regulatory bodies worldwide	financial regulatory bodies, regulatory bodies impose
stringent	adj	/ˈstrɪndʒənt/	nghiêm ngặt	stringent testing requirements	stringent regulations, stringent measures
outstrip	v	/ˌaʊtˈstrɪp/	vượt xa, hơn	outstrips regulatory capacity	outstrip demand, outstrip supply

Passage 3 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
pervasive	adj	/pəˈveɪsɪv/	lan tràn, phổ biến	pervasive integration	pervasive influence, pervasive technology
ramifications	n	/ˌræmɪfɪˈkeɪʃnz/	hậu quả, tác động	socioeconomic ramifications	far-reaching ramifications, serious ramifications
judiciously	adv	/dʒuːˈdɪʃəsli/	một cách khôn ngoan	navigate judiciously	use judiciously, act judiciously
creative destruction	n phrase	/kriˈeɪtɪv dɪˈstrʌkʃn/	phá hủy sáng tạo	paradox of creative destruction	process of creative destruction
algorithmised	v	/ˈælɡərɪðmaɪzd/	được thuật toán hóa	become algorithmised	algorithmised process, algorithmised decision
redundant	adj	/rɪˈdʌndənt/	dư thừa, bị loại bỏ	rendered redundant	become redundant, make redundant
hollowing out	n phrase	/ˈhɒləʊɪŋ aʊt/	sự trống rỗng, khoét rỗng	hollowing out of middle class	economic hollowing out, hollowing out phenomenon
in aggregate	adv phrase	/ɪn ˈæɡrɪɡət/	tổng cộng, nhìn chung	jobs in aggregate	considered in aggregate, measured in aggregate
structural unemployment	n phrase	/ˈstrʌktʃərəl ˌʌnɪmˈplɔɪmənt/	thất nghiệp cơ cấu	structural unemployment threatens	reduce structural unemployment
marginalised	adj	/ˈmɑːdʒɪnəlaɪzd/	bị gạt ra lề	economically marginalised	marginalised communities, marginalised groups
Matthew effect	n phrase	/ˈmæθjuː ɪˈfekt/	hiệu ứng Matthew	Matthew effect manifests	demonstrate Matthew effect
hegemony	n	/hɪˈɡeməni/	quyền bá chủ, thống trị	economic hegemony	cultural hegemony, global hegemony
rent-seeking	n	/rent ˈsiːkɪŋ/	tìm kiếm lợi nhuận (phi sản xuất)	technological rent-seeking	rent-seeking behaviour, engage in rent-seeking
perpetuate	v	/pəˈpetʃueɪt/	làm tồn tại mãi, duy trì	perpetuate biases	perpetuate stereotypes, perpetuate inequality
algorithmic redlining	n phrase	/ˌælɡəˈrɪðmɪk ˈredlaɪnɪŋ/	phân biệt đối xử thuật toán	creating algorithmic redlining	prevent algorithmic redlining
systemic risk	n phrase	/sɪˈstemɪk rɪsk/	rủi ro hệ thống	systemic risk profile	reduce systemic risk, manage systemic risk
contagion	n	/kənˈteɪdʒən/	sự lan tỏa, lây lan	amplify contagion	financial contagion, contagion effect
herding behaviour	n phrase	/ˈhɜːdɪŋ bɪˈheɪvjə/	hành vi bầy đàn	herding behaviour replicated	market herding behaviour
inscrutable	adj	/ɪnˈskruːtəbl/	khó hiểu, không thể đoán được	inscrutable algorithms	inscrutable process, inscrutable decision
regulatory arbitrage	n phrase	/ˈreɡjələtəri ˈɑːbɪtrɑːʒ/	lách luật quy định	tendency to regulatory arbitrage	engage in regulatory arbitrage

Kết Bài

Chủ đề “How does AI affect the financial services industry?” không chỉ phổ biến trong IELTS Reading mà còn phản ánh xu hướng công nghệ đang định hình lại toàn bộ ngành tài chính toàn cầu. Qua bộ đề thi mẫu này, bạn đã được trải nghiệm đầy đủ ba mức độ khó từ Easy đến Hard, với tổng cộng 40 câu hỏi đa dạng theo đúng format IELTS chính thức.

Ba passages đã cung cấp cho bạn góc nhìn toàn diện về tác động của AI: từ những ứng dụng cơ bản như chatbot và phát hiện gian lận (Passage 1), đến robo-advisors và giao dịch thuật toán phức tạp hơn (Passage 2), và cuối cùng là những hệ quả xã hội-kinh tế sâu xa bao gồm thất nghiệp cơ cấu, bất bình đẳng và rủi ro hệ thống (Passage 3). Mỗi passage không chỉ kiểm tra kỹ năng đọc hiểu của bạn mà còn giúp mở rộng kiến thức về một lĩnh vực quan trọng của thế giới hiện đại.

Phần đáp án chi tiết với giải thích từng câu sẽ giúp bạn hiểu rõ vị trí thông tin, cách paraphrase và kỹ thuật làm bài cho từng dạng câu hỏi. Đừng bỏ qua bảng từ vựng với hơn 40 từ và cụm từ quan trọng – đây là tài nguyên quý giá cho việc học từ vựng chuyên ngành và chuẩn bị cho các chủ đề tương tự về công nghệ, tài chính và xã hội.

Hãy thực hành đề thi này nhiều lần, phân tích kỹ các câu trả lời sai và học từ vựng một cách có hệ thống. Với sự kiên trì và phương pháp đúng đắn, bạn hoàn toàn có thể đạt được band điểm IELTS Reading mong muốn. Chúc bạn ôn tập hiệu quả và thành công trong kỳ thi sắp tới!