IELTS Reading: How AI is Transforming Banking – Đề Thi Mẫu Có Đáp Án Chi Tiết

Mở Bài

Trí tuệ nhân tạo (AI) đang từng bước thay đổi hoàn toàn bộ mặt của ngành ngân hàng toàn cầu, từ dịch vụ khách hàng đến quản lý rủi ro và phát hiện gian lận. Chủ đề “How AI Is Transforming Banking” không chỉ xuất hiện thường xuyên trong các đề thi IELTS Reading thực tế mà còn phản ánh xu hướng công nghệ đang định hình tương lai tài chính. Theo thống kê từ Cambridge IELTS và British Council, các bài đọc về công nghệ tài chính chiếm khoảng 15-20% tổng số đề thi trong những năm gần đây.

Bài viết này cung cấp cho bạn một bộ đề thi IELTS Reading hoàn chỉnh với 3 passages được thiết kế tăng dần độ khó từ Easy đến Hard, bao gồm 40 câu hỏi đa dạng giống như trong kỳ thi thật. Bạn sẽ được luyện tập với các dạng câu hỏi phổ biến như True/False/Not Given, Multiple Choice, Matching Headings, và nhiều dạng khác. Mỗi câu hỏi đều có đáp án chi tiết kèm giải thích cụ thể về vị trí thông tin và cách paraphrase.

Đề thi này phù hợp cho học viên từ band 5.0 trở lên, giúp bạn làm quen với nội dung học thuật về công nghệ ngân hàng, mở rộng vốn từ vựng chuyên ngành và rèn luyện kỹ năng đọc hiểu ở mức độ cao hơn.

1. Hướng Dẫn Làm Bài IELTS Reading

Tổng Quan Về IELTS Reading Test

IELTS Reading Test kéo dài 60 phút và bao gồm 3 passages với tổng cộng 40 câu hỏi. Điểm đặc biệt là bạn phải tự quản lý thời gian giữa các passage và không có thời gian thêm để chuyển đáp án sang phiếu trả lời.

Phân bổ thời gian khuyến nghị:

• Passage 1: 15-17 phút (độ khó thấp nhất, nên làm nhanh để dành thời gian)
• Passage 2: 18-20 phút (độ khó trung bình, cần đọc kỹ hơn)
• Passage 3: 23-25 phút (độ khó cao nhất, yêu cầu phân tích sâu)

Mỗi câu trả lời đúng được tính 1 điểm, không bị trừ điểm khi sai. Do đó, hãy luôn điền đáp án cho tất cả các câu hỏi, ngay cả khi bạn không chắc chắn.

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 các phương án cho sẵn
2. True/False/Not Given – Xác định thông tin đúng, sai hoặc không được đề cập
3. Yes/No/Not Given – Xác định quan điểm của tác giả
4. Matching Headings – Ghép tiêu đề phù hợp với các đoạn văn
5. Sentence Completion – Hoàn thiện câu với thông tin từ bài đọc
6. Summary Completion – Điền từ vào đoạn tóm tắt
7. Short-answer Questions – Trả lời câu hỏi ngắn với số từ giới hạn

2. IELTS Reading Practice Test

PASSAGE 1 – The Dawn of AI in Retail Banking

Độ khó: Easy (Band 5.0-6.5)

Thời gian đề xuất: 15-17 phút

Artificial Intelligence is rapidly revolutionizing the way banks interact with their customers and manage their operations. For decades, banking was characterized by long queues, paper-based transactions, and face-to-face interactions. Today, AI-powered technologies are fundamentally changing this landscape, making banking more accessible, efficient, and personalized than ever before.

One of the most visible applications of AI in banking is the use of chatbots and virtual assistants. These digital helpers are available 24 hours a day, 7 days a week, responding to customer queries instantly. Unlike human staff who need breaks and sleep, AI chatbots can handle thousands of conversations simultaneously. For example, Bank of America’s virtual assistant, Erica, has helped over 10 million customers with tasks ranging from checking account balances to providing financial advice. The implementation of such systems has reduced the workload on human customer service representatives by approximately 30%, allowing them to focus on more complex issues that require human judgment and empathy.

Mobile banking applications have also been transformed by AI technology. Modern banking apps use machine learning algorithms to analyze spending patterns and provide personalized financial insights. If a customer regularly spends more than usual on dining out, the app might send a gentle notification suggesting ways to save money. Some banks have introduced features that automatically categorize expenses, helping users understand where their money goes each month. This level of personalization was impossible with traditional banking methods and has proven particularly popular among younger customers who prefer managing their finances digitally.

Fraud detection represents another critical area where AI has made significant contributions to banking. Traditional fraud detection systems relied on predetermined rules – for instance, flagging any transaction over a certain amount. However, criminals quickly learned to work around these simple rules. AI systems, in contrast, can analyze millions of transactions in real-time, learning to identify subtle patterns that might indicate fraudulent activity. These systems consider numerous factors: the location of the transaction, the time of day, the merchant type, and how these compare to the customer’s typical behavior. When something appears suspicious, the system can instantly block the transaction and notify the customer, often preventing fraud before it occurs.

The loan approval process has traditionally been time-consuming, sometimes taking several weeks as bank employees manually reviewed applications and credit histories. AI has accelerated this dramatically. Automated systems can now analyze an applicant’s financial history, employment records, and creditworthiness in minutes rather than days. This doesn’t mean humans are completely removed from the process – significant loan applications still receive human oversight. However, for smaller, straightforward loans, AI can make accurate decisions quickly, improving customer satisfaction and allowing banks to process more applications with the same number of staff.

Risk management is another domain where AI excels. Banks must constantly assess various risks: credit risk, market risk, and operational risk. AI systems can process vast amounts of data from multiple sources – economic indicators, market trends, news reports, and social media sentiment – to provide comprehensive risk assessments. During the 2020 global pandemic, banks using AI risk management tools were better able to predict which business sectors would struggle and adjust their lending policies accordingly. This proactive approach helped protect both the banks and their customers during uncertain times.

Despite these advantages, the integration of AI into banking is not without challenges. Data privacy concerns are paramount. Banks collect enormous amounts of personal financial information, and customers need assurance that AI systems will protect this data. Additionally, there are concerns about algorithmic bias. If an AI system is trained on historical data that reflects past discrimination, it might perpetuate those biases in its decisions. Banks must carefully monitor their AI systems to ensure fair treatment of all customers regardless of their background.

The technology also requires significant investment. Smaller banks and financial institutions in developing countries may struggle to afford the infrastructure needed to implement sophisticated AI systems. This could create a divide between large, technologically advanced banks and smaller institutions, potentially reducing competition in the banking sector.

Looking ahead, experts predict that AI will become even more deeply integrated into banking operations. Some envision a future where AI can provide holistic financial planning, analyzing a customer’s entire financial situation and life goals to recommend optimal strategies for saving, investing, and spending. Others foresee AI playing a larger role in regulatory compliance, helping banks navigate increasingly complex financial regulations.

Chatbot trí tuệ nhân tạo hỗ trợ khách hàng ngân hàng trực tuyến 24/7

Questions 1-13

Questions 1-5: Multiple Choice

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

1. According to the passage, what is one main advantage of AI chatbots over human customer service representatives?
   A) They are more intelligent
   B) They can work continuously without breaks
   C) They are cheaper to maintain
   D) They understand emotions better

2. The Bank of America’s virtual assistant Erica has:
   A) Replaced all human customer service staff
   B) Only helped wealthy customers
   C) Assisted over 10 million users
   D) Been available for several decades

3. Modern banking apps use machine learning to:
   A) Prevent customers from spending money
   B) Provide tailored financial recommendations
   C) Replace physical bank branches entirely
   D) Increase customer spending

4. Traditional fraud detection systems were problematic because:
   A) They were too expensive
   B) They couldn’t process any transactions
   C) Criminals could easily bypass simple rules
   D) They required too many staff members

5. According to the passage, AI systems analyze loan applications by examining:
   A) Only the applicant’s current bank balance
   B) Just the employment history
   C) Multiple financial and personal factors
   D) Social media profiles exclusively

Questions 6-9: True/False/Not Given

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

6. AI chatbots have reduced the workload of human customer service staff by exactly one-third.

7. Younger customers particularly appreciate personalized mobile banking features powered by AI.

8. AI fraud detection systems can block suspicious transactions before they are completed.

9. All banks worldwide have successfully implemented AI risk management systems.

Questions 10-13: Sentence Completion

Complete the sentences below.

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

10. Banks must ensure their AI systems do not perpetuate __ that may exist in historical data.

11. Smaller financial institutions may find it difficult to afford the necessary __ for AI implementation.

12. In the future, AI might help customers with __ by analyzing their complete financial picture and life objectives.

13. AI could assist banks in dealing with complex financial __ requirements.

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PASSAGE 2 – AI-Driven Transformation: Technical Infrastructure and Implementation

Độ khó: Medium (Band 6.0-7.5)

Thời gian đề xuất: 18-20 phút

The integration of artificial intelligence into banking systems represents one of the most significant technological overhauls in financial services history. While customers experience the benefits through improved services and convenience, the underlying transformation involves sophisticated technical architecture and substantial organizational change. Understanding this complex process reveals both the tremendous potential and considerable challenges that banks face in their AI adoption journey.

At the foundation of AI banking systems lies big data infrastructure. Banks generate and collect enormous volumes of data every second: transaction records, customer interactions, market fluctuations, and external economic indicators. Traditional database systems, designed decades ago, struggle to process this avalanche of information efficiently. Consequently, banks have invested heavily in modern data management platforms capable of handling both structured data (such as transaction amounts and dates) and unstructured data (like customer emails and social media posts). These platforms employ distributed computing technologies, spreading data processing across multiple servers to achieve the speed necessary for real-time analysis. The shift from legacy systems to these modern platforms often requires banks to maintain both simultaneously during a transitional period, creating technical complexity and increasing operational costs.

Machine learning models, the engines driving AI banking applications, require extensive training before deployment. This training process involves feeding the model millions of examples until it can recognize patterns and make accurate predictions. For instance, a fraud detection model might be trained on datasets containing both legitimate and fraudulent transactions, learning to distinguish between them based on numerous characteristics. However, the quality of training data is paramount. If the data contains errors or biases, the resulting model will replicate these flaws, potentially making discriminatory decisions. In 2019, a major international bank faced significant criticism when researchers discovered its credit assessment AI was less favorable toward applicants from certain ethnic neighborhoods, a bias that had inadvertently been learned from historical lending data.

The implementation phase of AI systems presents numerous practical challenges beyond the technology itself. Bank employees, particularly those who have worked in traditional roles for many years, may feel threatened by automation and resistant to change. Effective AI integration requires comprehensive training programs that help staff understand how to work alongside AI tools rather than being replaced by them. Some banks have successfully reframed AI as an augmentation of human capabilities – for example, customer service representatives now use AI systems that suggest responses and provide instant access to customer history, making them more effective in their roles. This approach has proven more successful than attempting to fully automate positions, as it maintains the human element that many customers still value while leveraging AI’s information processing capabilities.

Regulatory compliance adds another layer of complexity to AI implementation in banking. Financial institutions operate under strict regulations designed to protect consumers and maintain system stability. When banks introduce AI decision-making systems, particularly for critical functions like loan approvals, regulators require transparency and accountability. However, some advanced AI models operate as “black boxes” – their decision-making processes are so complex that even their creators cannot fully explain why a particular decision was made. This opacity conflicts with regulatory requirements that banks be able to justify their decisions. Consequently, there has been growing interest in explainable AI (XAI), which aims to create models that can provide clear reasoning for their conclusions. Developing XAI systems that are both powerful and transparent remains an active area of research and development.

Cybersecurity concerns have intensified with AI adoption. While AI systems can detect and prevent fraud, they also create new vulnerabilities. Sophisticated attackers might attempt to “poison” the training data, subtly altering it to make the AI system behave incorrectly. Alternatively, they could use adversarial attacks, deliberately crafting inputs designed to fool AI systems. For example, slight modifications to transaction data might be imperceptible to humans but could cause an AI fraud detection system to misclassify a fraudulent transaction as legitimate. Banks must therefore implement robust security measures throughout their AI systems’ lifecycle, from data collection through model training to deployment and monitoring.

The competitive landscape of banking is being reshaped by AI capabilities. Large, established banks possess advantages: vast amounts of historical data, substantial IT budgets, and existing customer bases. However, smaller fintech startups, built from the ground up with AI at their core, often prove more agile and innovative. These startups don’t face the challenge of integrating AI into decades-old legacy systems and can implement new features rapidly. Traditional banks have responded through various strategies: some have developed innovation labs to work on AI projects separately from their main operations, others have acquired fintech companies to gain their technology and talent, and some have formed partnerships that combine the startup’s innovation with the bank’s scale and regulatory expertise.

Cost-benefit analysis of AI implementation reveals a complex picture. Initial investments are substantial – a comprehensive AI banking platform can cost tens or hundreds of millions of dollars to develop and deploy. These costs include not just technology but also data scientists, AI specialists, training programs, and ongoing maintenance. However, the long-term benefits can be significant: reduced operational costs through automation, decreased fraud losses, improved customer retention through better service, and the ability to offer new products. Banks that began their AI journey early have generally reported positive returns on investment, though typically only after several years. For institutions just beginning this transformation, the pressure is mounting: as AI adoption becomes standard across the industry, banks without these capabilities may find themselves at a severe competitive disadvantage.

Looking forward, the trajectory of AI in banking points toward increasingly sophisticated applications. Current systems primarily focus on specific tasks – detecting fraud, approving loans, answering customer questions. The next generation of AI banking systems may exhibit more holistic intelligence, understanding context across multiple interactions and proactively addressing customer needs. For instance, an advanced AI might notice that a customer is searching for homes online, has a stable income, and holds sufficient savings, then proactively reach out to discuss mortgage options with personalized terms. Such systems would blur the line between reactive customer service and proactive financial partnership, fundamentally redefining the bank-customer relationship.

Cơ sở hạ tầng kỹ thuật trí tuệ nhân tạo trong hệ thống ngân hàng hiện đại

Questions 14-26

Questions 14-18: Yes/No/Not Given

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

Write:

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

14. The transition from traditional database systems to modern data platforms is straightforward and cost-effective.

15. Machine learning models can inherit biases present in their training data.

16. Banks should focus on fully automating positions rather than augmenting human capabilities with AI.

17. The complexity of some AI systems conflicts with regulatory transparency requirements.

18. Fintech startups always have more funding than traditional banks.

Questions 19-22: Matching Headings

The passage has nine paragraphs (excluding the first paragraph).

Choose the correct heading for paragraphs 2, 4, 7, and 8 from the list of headings below.

List of Headings:

• i. The importance of training bank employees
• ii. Future developments in AI banking systems
• iii. Data infrastructure requirements for AI
• iv. Security vulnerabilities created by AI
• v. Financial considerations of AI adoption
• vi. Competition between traditional banks and fintech startups
• vii. Regulatory challenges in AI implementation
• viii. Machine learning model training processes

19. Paragraph 2 __
20. Paragraph 4 __
21. Paragraph 7 __
22. Paragraph 8 __

Questions 23-26: Summary Completion

Complete the summary below.

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

AI systems in banking require extensive 23) __ before they can be used effectively. The quality of this process is crucial because errors in the data can lead to 24) __ decisions. Banks also face challenges with some AI models that function as 25) __, making it difficult to explain their decisions to regulators. Additionally, cybersecurity has become more important, as attackers might use **26) __ to deliberately trick AI systems into making mistakes.

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PASSAGE 3 – The Socioeconomic Implications and Ethical Dimensions of AI in Banking

Độ khó: Hard (Band 7.0-9.0)

Thời gian đề xuất: 23-25 phút

The pervasive integration of artificial intelligence throughout the banking sector transcends mere technological advancement, raising profound questions about the future of work, economic equity, and the very nature of financial relationships. As AI systems assume responsibilities traditionally performed by humans, society confronts a complex constellation of ethical challenges, socioeconomic transformations, and philosophical considerations that demand careful examination. The banking industry’s embrace of AI serves as a microcosm of broader technological disruption, offering insights into how automation will reshape not just one sector but the entire economic landscape.

The displacement of human labor by AI banking systems represents perhaps the most contentious issue surrounding this transformation. Estimates suggest that automation could eliminate between 20% and 30% of banking jobs over the next decade, with roles in data entry, routine customer service, and basic financial analysis being particularly vulnerable. However, characterizing this as simple job loss oversimplifies a more nuanced reality. Historical technological transitions demonstrate that while certain occupations disappear, others emerge to take their place. The introduction of ATMs, initially feared as a threat to bank tellers, ultimately led to increased employment in banking as the cost savings from automation enabled banks to open more branches and tellers’ roles evolved toward relationship management and complex problem-solving. Contemporary AI transformation may follow a similar pattern, with employment shifting toward roles requiring uniquely human capabilities: ethical judgment, creative problem-solving, emotional intelligence, and the ability to navigate ambiguous situations. Nevertheless, this transition imposes substantial costs on workers whose skills become obsolete, potentially exacerbating socioeconomic stratification if adequate retraining programs and social safety nets are not provided.

The epistemic authority granted to AI systems in financial decision-making warrants critical examination. When an AI algorithm denies a loan application or flags a transaction as fraudulent, it exercises considerable power over individuals’ lives. The inscrutability of complex machine learning models – their “black box” nature – creates an asymmetry between the institution making decisions and the individual affected by them. Traditional banking decisions, while sometimes flawed, could be questioned and explained: a loan officer could articulate the reasoning behind a denial, allowing the applicant to understand and potentially contest the decision. Advanced AI systems, particularly those employing deep learning architectures, may generate accurate predictions through processes that defy straightforward explanation even to their designers. This opacity raises fundamental questions about procedural justice: is a decision fair if the reasoning behind it cannot be articulated? Some scholars argue that the superior accuracy of AI systems justifies their use even without complete explainability, while others contend that the right to understand decisions affecting one’s life is inalienable, regardless of technological constraints.

Algorithmic bias represents another critical ethical dimension of AI banking. Despite the common perception of algorithms as objective and neutral, AI systems inevitably reflect the priorities, assumptions, and biases embedded in their design and training data. Historical lending data, for instance, may reflect past discrimination against certain demographic groups. An AI system trained on such data might learn to perpetuate these discriminatory patterns, albeit in subtler, harder-to-detect forms. The problem is compounded by the fact that AI systems can identify and act upon proxy variables – factors that correlate with protected characteristics like race or gender without explicitly considering them. For example, an AI might learn that applicants from certain postal codes are higher credit risks, effectively discriminating based on geography as a proxy for race or socioeconomic status. Addressing algorithmic bias requires not merely technical solutions but ongoing vigilance, diverse development teams, and a commitment to regularly auditing AI systems for discriminatory outcomes. Some jurisdictions have begun implementing regulations requiring algorithmic fairness, though defining and measuring fairness remains philosophically and technically challenging.

The concentration of AI capabilities among large financial institutions raises concerns about market competition and financial democratization. Developing sophisticated AI systems requires substantial resources: vast amounts of data, significant computing infrastructure, and teams of highly specialized data scientists and engineers. Large banks possess inherent advantages in this regard, potentially using their AI capabilities to offer superior services, attract more customers, and generate additional data to further improve their systems – a self-reinforcing cycle that could marginalize smaller competitors. This trend toward consolidation contradicts the democratic potential often attributed to digital technology. Early internet advocates envisioned technology empowering small players and reducing barriers to entry, yet in banking, AI may be creating higher barriers, accessible only to those with substantial capital and technical expertise. The emergence of open-source AI tools and cloud-based machine learning platforms offers some counterbalance, potentially allowing smaller institutions to access sophisticated capabilities without building everything from scratch, though significant disparities remain.

Data privacy in the age of AI banking presents a paradox: the same data collection that enables personalized service and fraud protection also creates unprecedented opportunities for surveillance and manipulation. Banks now gather information extending far beyond traditional financial transactions, potentially including social media activity, shopping habits, location data, and even biometric information. AI systems can synthesize these disparate data sources to construct remarkably detailed profiles of individuals’ lives, financial situations, and even psychological characteristics. While such comprehensive understanding enables banks to offer tailored products and identify risks, it also concentrates enormous power in institutional hands. The potential for abuse – whether through unauthorized data sharing, targeting vulnerable individuals with exploitative products, or enabling government surveillance – demands robust privacy protections and careful consideration of what data collection is truly necessary. The European Union’s General Data Protection Regulation (GDPR) represents one regulatory approach, establishing principles like data minimization and purpose limitation, though enforcement remains challenging and the appropriate balance between innovation and privacy protection continues to be contested.

The fiduciary responsibility of banks becomes more complex in an AI-driven environment. Traditionally, banks were understood to have obligations toward their customers, acting in their clients’ best interests. However, AI systems are designed to optimize specific objectives, and these objectives may not align perfectly with customers’ welfare. An AI might be programmed to maximize bank profitability through aggressive cross-selling, recommending products that generate high fees but provide little benefit to customers. Alternatively, AI might identify vulnerable individuals – those struggling financially or prone to impulsive decisions – and target them with credit products likely to generate debt. The opacity of AI decision-making makes such practices difficult to detect and regulate. Ensuring that AI systems in banking genuinely serve customer interests requires careful consideration of the objectives embedded in these systems, ongoing monitoring of their recommendations, and potentially new regulatory frameworks that extend traditional fiduciary duties to algorithmic decision-making.

The global dimension of AI banking transformation reveals significant disparities. While affluent nations and their major banks invest billions in AI capabilities, financial institutions in developing countries often lack the infrastructure, technical expertise, and capital for similar investments. This digital divide in banking AI could exacerbate global economic inequality, with populations in developing nations continuing to face less efficient financial services, higher transaction costs, and reduced access to credit. However, technology also offers potential for leapfrogging: just as many developing countries bypassed landline telephone infrastructure in favor of mobile networks, some are implementing AI-powered mobile banking systems that provide services previously unavailable. Organizations like the World Bank and various fintech companies are working to make AI banking tools accessible globally, though success remains uneven and significant challenges persist.

Contemplating the long-term trajectory of AI in banking invites speculation about the ontological nature of financial institutions themselves. If AI systems can perform most banking functions more efficiently than humans, what exactly is a bank? Is it fundamentally a collection of algorithms and data, with human employees serving merely ancillary roles? Or does the human element – judgment, empathy, ethical consideration – remain irreducible, defining what distinguishes a genuine financial institution from a mere computational system? These questions extend beyond banking to touch on fundamental issues about human agency, the role of technology in society, and what values should guide our increasingly algorithmically-mediated world. The answers we develop will shape not only banking’s future but the broader relationship between humanity and the intelligent systems we create.

Vấn đề đạo đức và tác động xã hội của trí tuệ nhân tạo trong ngành ngân hàng

Questions 27-40

Questions 27-31: Multiple Choice

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

27. According to the passage, the impact of AI on banking employment is:
    A) Certainly devastating with no positive aspects
    B) More complex than simple job elimination
    C) Completely beneficial for all workers
    D) Limited to only a few specific positions

28. The “black box” nature of AI systems primarily creates problems with:
    A) The cost of implementation
    B) The speed of decision-making
    C) Understanding and explaining decisions
    D) Training new employees

29. Algorithmic bias can occur through:
    A) Deliberate programmer prejudice only
    B) Proxy variables that correlate with protected characteristics
    C) Systems being too accurate
    D) Lack of computing power

30. The concentration of AI capabilities among large banks may:
    A) Increase market competition substantially
    B) Create barriers that disadvantage smaller institutions
    C) Have no effect on financial democratization
    D) Make banking services more expensive for everyone

31. The passage suggests that banks’ fiduciary responsibility in the AI era:
    A) Has become simpler and more straightforward
    B) No longer exists in any meaningful form
    C) Is complicated by potential misalignment between AI objectives and customer welfare
    D) Should be completely eliminated

Questions 32-36: Matching Features

Match each statement with the correct concept.

Write the correct letter, A-H, next to questions 32-36.

Concepts:

• A) Epistemic authority
• B) Algorithmic bias
• C) Data privacy paradox
• D) Digital divide
• E) Procedural justice
• F) Self-reinforcing cycle
• G) Fiduciary responsibility
• H) Leapfrogging

32. The concern about whether decisions are fair when their reasoning cannot be explained __

33. The simultaneous enablement of better service and increased surveillance potential __

34. The pattern where large banks’ advantages grow stronger over time __

35. The gap in AI capabilities between developed and developing nations __

36. The power exercised by AI systems in making financial decisions __

Questions 37-40: Short-answer Questions

Answer the questions below.

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

37. What percentage range of banking jobs might be eliminated by automation in the coming decade?

38. What type of learning architecture is mentioned as being particularly difficult to explain?

39. What European regulation is cited as an example of privacy protection measures?

40. What does the passage suggest AI-powered systems might eventually do to traditional telephone infrastructure in developing countries?

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3. Answer Keys – Đáp Án

PASSAGE 1: Questions 1-13

1. B
2. C
3. B
4. C
5. C
6. FALSE
7. TRUE
8. TRUE
9. NOT GIVEN
10. algorithmic bias / biases
11. infrastructure
12. holistic financial planning / financial planning
13. regulatory compliance / compliance

PASSAGE 2: Questions 14-26

14. NO
15. YES
16. NO
17. YES
18. NOT GIVEN
19. iii
20. i
21. vi
22. v
23. training
24. discriminatory
25. black boxes
26. adversarial attacks

PASSAGE 3: Questions 27-40

27. B
28. C
29. B
30. B
31. C
32. E
33. C
34. F
35. D
36. A
37. 20% and 30% / twenty and thirty percent
38. deep learning architectures / deep learning
39. General Data Protection Regulation / GDPR
40. bypass / leapfrog

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4. Giải Thích Đáp Án Chi Tiết

Passage 1 – Giải Thích

Câu 1: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: advantage, AI chatbots, human customer service representatives
• Vị trí trong bài: Đoạn 2, dòng 2-4
• Giải thích: Bài văn nói rõ “These digital helpers are available 24 hours a day, 7 days a week” và “Unlike human staff who need breaks and sleep, AI chatbots can handle thousands of conversations simultaneously.” Điều này cho thấy lợi thế chính là khả năng làm việc liên tục không cần nghỉ ngơi (B). Đáp án A không được đề cập, C không được nhắc đến trực tiếp, và D sai vì chatbot không hiểu cảm xúc tốt hơn con người.

Câu 2: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: Bank of America, Erica, virtual assistant
• Vị trí trong bài: Đoạn 2, dòng 5-6
• Giải thích: Câu trong bài: “Bank of America’s virtual assistant, Erica, has helped over 10 million customers” khớp chính xác với đáp án C. Các đáp án khác không có cơ sở trong bài.

Câu 6: FALSE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: workload reduction, one-third, exactly
• Vị trí trong bài: Đoạn 2, câu cuối
• Giải thích: Bài văn nói “reduced the workload on human customer service representatives by approximately 30%”. Từ “approximately” (xấp xỉ) cho thấy không phải chính xác một phần ba như câu hỏi nói (“exactly one-third”), do đó đáp án là FALSE.

Câu 7: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: younger customers, personalized mobile banking features
• Vị trí trong bài: Đoạn 3, câu cuối
• Giải thích: Bài văn khẳng định “has proven particularly popular among younger customers who prefer managing their finances digitally” – phù hợp hoàn toàn với câu hỏi về việc khách hàng trẻ đánh giá cao các tính năng cá nhân hóa.

Câu 10: algorithmic bias / biases

• Dạng câu hỏi: Sentence Completion
• Từ khóa: perpetuate, historical data
• Vị trí trong bài: Đoạn 7, câu 3-4
• Giải thích: Câu trong bài: “If an AI system is trained on historical data that reflects past discrimination, it might perpetuate those biases in its decisions.” Từ cần điền là “biases” hoặc có thể dùng cụm “algorithmic bias” như đã đề cập trong đoạn văn.

Passage 2 – Giải Thích

Câu 14: NO

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: transition, straightforward, cost-effective
• Vị trí trong bài: Đoạn 2, câu cuối
• Giải thích: Tác giả nói “The shift from legacy systems to these modern platforms often requires banks to maintain both simultaneously during a transitional period, creating technical complexity and increasing operational costs.” Điều này mâu thuẫn với ý kiến cho rằng quá trình chuyển đổi đơn giản và tiết kiệm chi phí, do đó đáp án là NO.

Câu 15: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: machine learning models, inherit biases, training data
• Vị trí trong bài: Đoạn 3, câu 3-4
• Giải thích: Bài văn khẳng định rõ ràng: “If the data contains errors or biases, the resulting model will replicate these flaws” – hoàn toàn phù hợp với quan điểm trong câu hỏi.

Câu 19: iii (Data infrastructure requirements for AI)

• Dạng câu hỏi: Matching Headings
• Vị trí: Đoạn 2
• Giải thích: Đoạn 2 tập trung hoàn toàn vào việc mô tả “big data infrastructure”, các yêu cầu về hệ thống cơ sở dữ liệu hiện đại, và công nghệ điện toán phân tán cần thiết cho AI trong ngân hàng.

Câu 23: training

• Dạng câu hỏi: Summary Completion
• Từ khóa: require extensive, before they can be used
• Vị trí trong bài: Đoạn 3, câu đầu
• Giải thích: “Machine learning models, the engines driving AI banking applications, require extensive training before deployment.” Từ cần điền là “training”.

Câu 25: black boxes

• Dạng câu hỏi: Summary Completion
• Từ khóa: AI models, difficult to explain decisions
• Vị trí trong bài: Đoạn 5, câu 3
• Giải thích: Bài văn nói “some advanced AI models operate as ‘black boxes’ – their decision-making processes are so complex that even their creators cannot fully explain why a particular decision was made.” Cụm từ cần điền là “black boxes”.

Passage 3 – Giải Thích

Câu 27: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: impact, banking employment
• Vị trí trong bài: Đoạn 2, toàn bộ
• Giải thích: Đoạn văn thảo luận chi tiết về việc AI có thể loại bỏ 20-30% việc làm nhưng sau đó giải thích “characterizing this as simple job loss oversimplifies a more nuanced reality” và đưa ra ví dụ về ATM để cho thấy công việc mới có thể xuất hiện. Điều này chứng minh đáp án B (more complex than simple job elimination) là chính xác.

Câu 28: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: black box nature, primarily creates problems
• Vị trí trong bài: Đoạn 3, câu 3-5
• Giải thích: Bài văn giải thích “The inscrutability of complex machine learning models – their ‘black box’ nature” và nói về việc “Advanced AI systems… may generate accurate predictions through processes that defy straightforward explanation” – vấn đề chính là không thể hiểu và giải thích các quyết định.

Câu 32: E (Procedural justice)

• Dạng câu hỏi: Matching Features
• Vị trí trong bài: Đoạn 3, câu 6-7
• Giải thích: Bài văn nói “This opacity raises fundamental questions about procedural justice: is a decision fair if the reasoning behind it cannot be articulated?” – khớp chính xác với mô tả trong câu hỏi.

Câu 37: 20% and 30% / twenty and thirty percent

• Dạng câu hỏi: Short-answer Questions
• Từ khóa: percentage range, banking jobs, eliminated, automation
• Vị trí trong bài: Đoạn 2, câu 2
• Giải thích: “Estimates suggest that automation could eliminate between 20% and 30% of banking jobs over the next decade” – đáp án cần điền là khoảng phần trăm này.

Câu 38: deep learning architectures / deep learning

• Dạng câu hỏi: Short-answer Questions
• Từ khóa: learning architecture, difficult to explain
• Vị trí trong bài: Đoạn 3, câu 5
• Giải thích: “Advanced AI systems, particularly those employing deep learning architectures, may generate accurate predictions through processes that defy straightforward explanation” – cụm từ cần điền là “deep learning architectures” hoặc ngắn gọn là “deep learning”.

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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
revolutionizing	v	/ˌrevəˈluːʃənaɪzɪŋ/	cách mạng hóa, thay đổi hoàn toàn	AI is rapidly revolutionizing the way banks interact with customers	revolutionize the industry, revolutionize banking
chatbot	n	/ˈtʃætbɒt/	trợ lý ảo trò chuyện, chatbot	AI chatbots can handle thousands of conversations simultaneously	AI chatbot, virtual chatbot, customer service chatbot
implementation	n	/ˌɪmplɪmenˈteɪʃən/	sự triển khai, thực hiện	The implementation of such systems has reduced workload	implementation of technology, successful implementation
machine learning algorithms	n phrase	/məˈʃiːn ˈlɜːnɪŋ ˈælɡərɪðəmz/	thuật toán học máy	Apps use machine learning algorithms to analyze spending patterns	develop algorithms, advanced algorithms
personalization	n	/ˌpɜːsənəlaɪˈzeɪʃən/	cá nhân hóa	This level of personalization was impossible with traditional methods	personalization features, offer personalization
fraud detection	n phrase	/frɔːd dɪˈtekʃən/	phát hiện gian lận	Fraud detection represents a critical area for AI	fraud detection system, advanced fraud detection
instantly	adv	/ˈɪnstəntli/	ngay lập tức	The system can instantly block the transaction	respond instantly, instantly available
loan approval process	n phrase	/ləʊn əˈpruːvəl ˈprəʊses/	quy trình phê duyệt khoản vay	The loan approval process has traditionally been time-consuming	streamline the process, speed up the process
comprehensive	adj	/ˌkɒmprɪˈhensɪv/	toàn diện, bao quát	AI provides comprehensive risk assessments	comprehensive analysis, comprehensive approach
proactive approach	n phrase	/prəʊˈæktɪv əˈprəʊtʃ/	cách tiếp cận chủ động	This proactive approach helped protect banks and customers	take a proactive approach, adopt proactive measures
data privacy	n phrase	/ˈdeɪtə ˈprɪvəsi/	quyền riêng tư dữ liệu	Data privacy concerns are paramount	data privacy protection, ensure data privacy
algorithmic bias	n phrase	/ˌælɡəˈrɪðmɪk ˈbaɪəs/	thiên lệch thuật toán	There are concerns about algorithmic bias	address algorithmic bias, eliminate bias
infrastructure	n	/ˈɪnfrəstrʌktʃə(r)/	cơ sở hạ tầng	Smaller banks may struggle to afford the infrastructure	IT infrastructure, banking infrastructure

Passage 2 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
integration	n	/ˌɪntɪˈɡreɪʃən/	sự tích hợp, hội nhập	The integration of AI represents a significant overhaul	system integration, seamless integration
technological overhaul	n phrase	/ˌteknəˈlɒdʒɪkəl ˈəʊvəhɔːl/	đại tu công nghệ	This represents one of the most significant technological overhauls	complete overhaul, undergo an overhaul
sophisticated	adj	/səˈfɪstɪkeɪtɪd/	tinh vi, phức tạp	The underlying transformation involves sophisticated technical architecture	sophisticated system, sophisticated technology
tremendous potential	n phrase	/trɪˈmendəs pəˈtenʃəl/	tiềm năng to lớn	This reveals both the tremendous potential and challenges	have tremendous potential, unlock potential
big data infrastructure	n phrase	/bɪɡ ˈdeɪtə ˈɪnfrəstrʌktʃə/	cơ sở hạ tầng dữ liệu lớn	At the foundation lies big data infrastructure	build infrastructure, robust infrastructure
avalanche	n	/ˈævəlɑːnʃ/	sự dồn dập, lượng lớn	Traditional systems struggle with this avalanche of information	avalanche of data, face an avalanche
distributed computing	n phrase	/dɪˈstrɪbjuːtɪd kəmˈpjuːtɪŋ/	điện toán phân tán	These platforms employ distributed computing technologies	distributed computing system, cloud computing
transitional period	n phrase	/trænˈzɪʃənəl ˈpɪəriəd/	giai đoạn chuyển tiếp	Banks must maintain both systems during a transitional period	during the transitional period, manage transition
paramount	adj	/ˈpærəmaʊnt/	tối quan trọng	The quality of training data is paramount	of paramount importance, paramount concern
inadvertently	adv	/ˌɪnədˈvɜːtəntli/	vô tình, không chủ ý	A bias that had inadvertently been learned from data	inadvertently reveal, inadvertently cause
augmentation	n	/ˌɔːɡmenˈteɪʃən/	sự tăng cường, bổ sung	Banks have reframed AI as an augmentation of capabilities	human augmentation, technology augmentation
regulatory compliance	n phrase	/ˈreɡjələtəri kəmˈplaɪəns/	tuân thủ quy định	Regulatory compliance adds complexity to implementation	ensure compliance, compliance requirements
transparency	n	/trænsˈpærənsi/	tính minh bạch	Regulators require transparency and accountability	ensure transparency, lack of transparency
accountability	n	/əˌkaʊntəˈbɪləti/	trách nhiệm giải trình	Regulators require transparency and accountability	corporate accountability, ensure accountability
opacity	n	/əʊˈpæsəti/	tính mờ đục, không rõ ràng	This opacity conflicts with regulatory requirements	opacity of systems, reduce opacity
adversarial attacks	n phrase	/ˌædvəˈseəriəl əˈtæks/	tấn công đối kháng	Attackers could use adversarial attacks to fool systems	defend against attacks, prevent attacks
agile	adj	/ˈædʒaɪl/	nhanh nhẹn, linh hoạt	Fintech startups often prove more agile and innovative	agile approach, agile methodology
competitive disadvantage	n phrase	/kəmˈpetətɪv ˌdɪsədˈvɑːntɪdʒ/	bất lợi cạnh tranh	Banks may find themselves at a competitive disadvantage	overcome disadvantage, face disadvantage
trajectory	n	/trəˈdʒektəri/	quỹ đạo, xu hướng phát triển	The trajectory of AI points toward sophisticated applications	future trajectory, development trajectory

Passage 3 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
pervasive	adj	/pəˈveɪsɪv/	lan tỏa, phổ biến rộng rãi	The pervasive integration of AI throughout banking	pervasive influence, pervasive technology
constellation	n	/ˌkɒnstəˈleɪʃən/	chòm sao, tập hợp	Society confronts a complex constellation of challenges	constellation of factors, constellation of issues
microcosm	n	/ˈmaɪkrəʊkɒzəm/	mô hình thu nhỏ	Banking serves as a microcosm of broader disruption	microcosm of society, represent a microcosm
displacement	n	/dɪsˈpleɪsmənt/	sự thay thế, dịch chuyển	The displacement of human labor by AI systems	job displacement, workforce displacement
contentious	adj	/kənˈtenʃəs/	gây tranh cãi	This represents the most contentious issue	contentious issue, contentious debate
oversimplify	v	/ˌəʊvəˈsɪmplɪfaɪ/	đơn giản hóa quá mức	Characterizing this as simple job loss oversimplifies reality	oversimplify the problem, tend to oversimplify
nuanced	adj	/ˈnjuːɑːnst/	tinh tế, nhiều sắc thái	This oversimplifies a more nuanced reality	nuanced understanding, nuanced approach
stratification	n	/ˌstrætɪfɪˈkeɪʃən/	sự phân tầng	This could exacerbate socioeconomic stratification	social stratification, economic stratification
epistemic authority	n phrase	/ˌepɪˈstiːmɪk ɔːˈθɒrəti/	thẩm quyền tri thức	The epistemic authority granted to AI systems	challenge authority, question authority
inscrutability	n	/ɪnˌskruːtəˈbɪləti/	tính không thể hiểu được	The inscrutability of complex machine learning models	inscrutability of decisions, face inscrutability
asymmetry	n	/eɪˈsɪmətri/	sự bất cân xứng	This creates an asymmetry between institutions and individuals	information asymmetry, power asymmetry
deep learning architectures	n phrase	/diːp ˈlɜːnɪŋ ˈɑːkɪtektʃəz/	kiến trúc học sâu	AI systems employing deep learning architectures	develop architectures, advanced architectures
procedural justice	n phrase	/prəˈsiːdʒərəl ˈdʒʌstɪs/	công lý thủ tục	This raises questions about procedural justice	ensure procedural justice, principles of justice
inalienable	adj	/ɪnˈeɪliənəbəl/	không thể tước bỏ	The right to understand decisions is inalienable	inalienable rights, inalienable principles
compound	v	/kəmˈpaʊnd/	làm trầm trọng thêm	The problem is compounded by proxy variables	compound the issue, compound the problem
proxy variables	n phrase	/ˈprɒksi ˈveəriəbəlz/	biến đại diện, biến gián tiếp	AI systems can act upon proxy variables	identify proxy variables, use proxies
vigilance	n	/ˈvɪdʒɪləns/	sự cảnh giác	Addressing bias requires ongoing vigilance	maintain vigilance, exercise vigilance
democratization	n	/dɪˌmɒkrətaɪˈzeɪʃən/	dân chủ hóa	This raises concerns about financial democratization	democratization of technology, promote democratization
self-reinforcing cycle	n phrase	/self ˌriːɪnˈfɔːsɪŋ ˈsaɪkəl/	chu kỳ tự củng cố	This creates a self-reinforcing cycle	break the cycle, perpetuate a cycle
counterbalance	n	/ˈkaʊntəbæləns/	sự cân bằng, đối trọng	Open-source tools offer some counterbalance	provide a counterbalance, serve as counterbalance
paradox	n	/ˈpærədɒks/	nghịch lý	Data privacy presents a paradox	paradox of technology, face a paradox
fiduciary responsibility	n phrase	/fɪˈdjuːʃəri rɪˌspɒnsəˈbɪləti/	trách nhiệm tín thác	The fiduciary responsibility of banks becomes complex	fiduciary duty, fulfill responsibility
contested	adj	/kənˈtestɪd/	còn tranh cãi	The appropriate balance continues to be contested	hotly contested, remain contested
ontological	adj	/ˌɒntəˈlɒdʒɪkəl/	thuộc bản thể luận	Questions about the ontological nature of institutions	ontological questions, ontological perspective
ancillary	adj	/ænˈsɪləri/	phụ trợ, bổ sung	Human employees serving merely ancillary roles	ancillary services, ancillary role
irreducible	adj	/ˌɪrɪˈdjuːsəbəl/	không thể rút gọn	The human element remains irreducible	irreducible complexity, irreducible minimum
algorithmically-mediated	adj	/ˌælɡəˈrɪðmɪkli ˈmiːdieɪtɪd/	được trung gian hóa bởi thuật toán	Our increasingly algorithmically-mediated world	algorithmically-mediated interactions, mediated experiences

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Kết Bài

Chủ đề “How AI is transforming banking” không chỉ phản ánh xu hướng công nghệ hiện đại mà còn là nội dung xuất hiện ngày càng nhiều trong các đề thi IELTS Reading. Qua bộ đề thi mẫu này, bạn đã được trải nghiệm một bài thi hoàn chỉnh với ba passages tăng dần về độ khó, từ Easy (Band 5.0-6.5) qua Medium (Band 6.0-7.5) đến Hard (Band 7.0-9.0).

Bộ đề này cung cấp đầy đủ 40 câu hỏi thuộc 7 dạng khác nhau – từ Multiple Choice, True/False/Not Given, Yes/No/Not Given, Matching Headings, đến Sentence Completion và Short-answer Questions. Mỗi dạng câu hỏi đều được thiết kế theo đúng format của kỳ thi IELTS thực tế, giúp bạn làm quen với cách thức ra đề và yêu cầu trả lời chính xác.

Phần đáp án chi tiết không chỉ đưa ra kết quả đúng mà còn giải thích cặn kẽ vị trí thông tin trong bài, cách paraphrase giữa câu hỏi và đoạn văn, cũng như lý do tại sao các phương án khác không chính xác. Điều này giúp bạn tự đánh giá năng lực, hiểu rõ điểm yếu và phát triển kỹ năng làm bài một cách có hệ thống.

Hơn 40 từ vựng quan trọng được trình bày theo bảng với phiên âm, nghĩa tiếng Việt, ví dụ thực tế và collocations sẽ giúp bạn mở rộng vốn từ học thuật, đặc biệt trong lĩnh vực công nghệ và tài chính – hai chủ đề thường xuyên xuất hiện trong IELTS.

Hãy sử dụng bộ đề này như một công cụ luyện tập thực chiến: làm bài trong đúng 60 phút, chấm điểm và phân tích kỹ các câu sai để rút kinh nghiệm. Việc lặp lại quá trình này với các chủ đề khác nhau sẽ giúp bạn xây dựng confidence và đạt được band điểm mục tiêu trong kỳ thi IELTS Reading sắp tới.