Mở Bài
Trí tuệ nhân tạo (AI) đang tạo ra cuộc cách mạng trong ngành dịch vụ tài chính toàn cầu, từ ngân hàng số đến quản lý rủi ro và dịch vụ khách hàng. Chủ đề “The Role Of AI In Improving Financial Services” xuất hiện ngày càng thường xuyên trong IELTS Reading, đặc biệt trong các kỳ thi từ năm 2020 trở lại đây, khi công nghệ AI trở thành xu hướng không thể phủ nhận.
Bài viết này cung cấp cho bạn một bộ đề thi IELTS Reading hoàn chỉnh với 3 passages tăng dần độ khó, giúp bạn làm quen với cấu trúc thi thật và rèn luyện kỹ năng đọc hiểu hiệu quả. Bạn sẽ được trải nghiệm 40 câu hỏi đa dạng dạng – từ True/False/Not Given, Multiple Choice đến Matching Headings và Summary Completion – đúng như trong đề thi chính thức.
Mỗi passage đi kèm đáp án chi tiết với giải thích cụ thể về vị trí thông tin, cách paraphrase và chiến lược làm bài. Bạn cũng sẽ học được hơn 40 từ vựng quan trọng liên quan đến công nghệ tài chính, AI và kinh tế số – những từ vựng thường xuyên xuất hiện trong IELTS Academic.
Đề thi này phù hợp cho học viên từ band 5.0 trở lên, giúp bạn tự đánh giá năng lực và chuẩn bị tốt nhất cho kỳ thi IELTS sắp tới.
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.
Phân bổ thời gian khuyến nghị:
- Passage 1: 15-17 phút (độ khó Easy)
- Passage 2: 18-20 phút (độ khó Medium)
- Passage 3: 23-25 phút (độ khó Hard)
Hãy dành 2-3 phút cuối để chuyển đáp án lên Answer Sheet, đảm bảo không có lỗi chính tả hoặc sót câu.
Các Dạng Câu Hỏi Trong Đề Này
Đề thi mẫu này bao gồm 7 dạng câu hỏi phổ biến trong IELTS Reading:
- Multiple Choice – Câu hỏi trắc nghiệm
- True/False/Not Given – Xác định thông tin đúng/sai/không được đề cập
- Yes/No/Not Given – Xác định ý kiến tác giả
- Matching Headings – Nối tiêu đề với đoạn văn
- Sentence Completion – Hoàn thành câu
- Summary Completion – Hoàn thành đoạn tóm tắt
- Short-answer Questions – Câu hỏi trả lời ngắn
Tổng quan về vai trò của AI trong cải thiện dịch vụ tài chính cho đề thi IELTS Reading
IELTS Reading Practice Test
PASSAGE 1 – AI Chatbots Transform Customer Service in Banking
Độ khó: Easy (Band 5.0-6.5)
Thời gian đề xuất: 15-17 phút
The integration of artificial intelligence into banking customer service has revolutionized the way financial institutions interact with their clients. Over the past five years, AI-powered chatbots have become increasingly sophisticated, moving beyond simple automated responses to provide personalized assistance that rivals human customer service representatives.
Traditional banking customer service typically involved long waiting times, limited operating hours, and inconsistent service quality. Customers often had to visit physical branches or wait on hold for extended periods to resolve simple queries. However, the emergence of AI chatbots has fundamentally changed this landscape. These digital assistants are available 24/7, can handle multiple conversations simultaneously, and provide instant responses to common banking questions.
Major banks worldwide have embraced this technology with remarkable results. For instance, Bank of America’s virtual assistant “Erica” has handled over one billion client requests since its launch in 2018. The chatbot helps customers check balances, pay bills, schedule appointments, and even provides financial guidance based on spending patterns. Similarly, HSBC’s chatbot can process requests in multiple languages, serving the bank’s diverse international customer base effectively.
The implementation of AI chatbots has delivered substantial cost savings for financial institutions. According to industry research, a single chatbot interaction costs approximately $0.50-$0.70, compared to $5-$12 for a human customer service call. This dramatic reduction in operational expenses allows banks to allocate resources more efficiently while maintaining high service standards. Moreover, chatbots eliminate human errors in routine transactions and ensure consistent application of banking policies.
From a customer perspective, AI chatbots offer several compelling advantages. The most obvious benefit is convenience – customers can resolve issues instantly without scheduling appointments or waiting in queues. Chatbots also provide a non-judgmental environment where users feel comfortable asking basic financial questions they might hesitate to ask human representatives. Additionally, these systems learn from each interaction, continuously improving their ability to understand customer needs and provide relevant solutions.
However, the technology is not without limitations. Complex financial situations still require human expertise and empathy. Chatbots may struggle with nuanced problems, unusual requests, or situations involving emotional distress. Most banks therefore implement a hybrid model, where chatbots handle routine inquiries and seamlessly transfer complicated cases to human advisors. This approach combines the efficiency of automation with the irreplaceable value of human judgment.
The future of AI in banking customer service looks promising. Natural Language Processing (NLP) technology continues to advance, enabling chatbots to understand context, detect emotion, and engage in more natural conversations. Some institutions are experimenting with voice-activated banking assistants that integrate with smart home devices, allowing customers to check account information or make payments through simple voice commands. As these technologies mature, the boundary between human and AI-powered service will become increasingly blurred.
Privacy and security concerns remain paramount as banks deploy AI chatbots. Financial institutions must ensure that these systems comply with strict data protection regulations and employ robust encryption to safeguard sensitive customer information. Customers need assurance that their financial data remains secure when interacting with AI systems, and banks must be transparent about how chatbots collect, store, and use personal information.
Training and education represent another critical aspect of successful chatbot implementation. Bank employees must learn to work alongside AI systems, understanding when to let chatbots handle interactions and when to intervene personally. Customers also need guidance on how to use these tools effectively and what types of requests are best suited for chatbot assistance versus human support.
Questions 1-5: Multiple Choice
Choose the correct letter, A, B, C, or D.
1. According to the passage, what is the main advantage of AI chatbots over traditional banking customer service?
A. They are more intelligent than human staff
B. They can work continuously without breaks
C. They completely replace human employees
D. They are cheaper to develop than training staff
2. The passage mentions Bank of America’s “Erica” as an example of:
A. a failed experiment in AI banking
B. a successful virtual banking assistant
C. the first chatbot ever created
D. a system that only works in English
3. What does the author suggest about complex financial problems?
A. They can all be solved by advanced AI
B. They are becoming less common in banking
C. They still need human advisors to handle
D. They should be avoided by customers
4. According to the passage, one psychological benefit of chatbots is that:
A. they make customers feel more important
B. they work faster than human representatives
C. customers feel less embarrassed asking basic questions
D. they provide more detailed financial advice
5. What does the passage indicate about the future of banking chatbots?
A. They will completely replace human customer service
B. They will become more natural and conversational
C. They will only be used for simple transactions
D. They will become less popular due to security concerns
Questions 6-10: 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 chatbot interactions cost less than one-tenth of the price of human customer service calls.
7. All major banks worldwide now use AI chatbots for customer service.
8. Chatbots eliminate all types of human errors in banking transactions.
9. Most banks use a system where chatbots and humans work together.
10. Voice-activated banking assistants are currently available in all banks.
Questions 11-13: Sentence Completion
Complete the sentences below.
Choose NO MORE THAN TWO WORDS from the passage for each answer.
11. Banks must ensure their AI systems follow strict __ to protect customer information.
12. Financial institutions need to use __ to keep customer data safe from unauthorized access.
13. Both bank staff and customers require __ to use chatbot systems effectively.
PASSAGE 2 – Machine Learning Algorithms in Fraud Detection
Độ khó: Medium (Band 6.0-7.5)
Thời gian đề xuất: 18-20 phút
Financial fraud represents one of the most persistent and costly challenges facing the banking industry, with global losses exceeding $30 billion annually. Traditional rule-based detection systems, which rely on predefined patterns and thresholds, have proven increasingly inadequate against sophisticated cybercriminals who constantly evolve their tactics. The advent of machine learning (ML) algorithms has ushered in a new era of fraud detection, offering dynamic, adaptive solutions that can identify suspicious activities with unprecedented accuracy and speed.
Conventional fraud detection mechanisms operate on static rules established by security experts. For example, a transaction might be flagged if it exceeds a certain amount, originates from a high-risk country, or deviates from the customer’s typical spending pattern. While straightforward to implement, these systems generate numerous false positives – legitimate transactions incorrectly identified as fraudulent – which frustrate customers and burden fraud investigation teams. Moreover, rule-based systems cannot detect novel fraud patterns they haven’t been explicitly programmed to recognize, leaving significant vulnerabilities.
Machine learning algorithms, particularly supervised learning models, have demonstrated remarkable effectiveness in addressing these limitations. These systems are trained on vast datasets containing millions of historical transactions labeled as either legitimate or fraudulent. Through this training process, ML algorithms learn to identify subtle patterns and correlations that human analysts might overlook. A gradient boosting model, for instance, might discover that specific combinations of transaction timing, merchant categories, and geographical locations strongly indicate fraudulent activity, even when each factor individually appears innocuous.
The real-time processing capability of ML-based fraud detection represents a paradigm shift from reactive to proactive security. Traditional systems often detected fraud only after significant damage had occurred, sometimes days or weeks after the initial breach. Modern ML algorithms analyze transactions instantaneously as they occur, calculating risk scores within milliseconds and blocking suspicious activities before completion. This immediacy dramatically reduces potential losses and enhances customer protection.
Deep learning, a sophisticated subset of machine learning, has further advanced fraud detection capabilities. Neural networks with multiple hidden layers can process unstructured data such as email content, voice patterns in phone calls, and even behavioral biometrics like typing rhythm and mouse movement patterns. These technologies create comprehensive user profiles that make impersonation extremely difficult. If a fraudster gains access to a customer’s login credentials but exhibits different behavioral patterns, the system can detect the anomaly and require additional authentication.
One particularly innovative application involves recurrent neural networks (RNNs) that analyze sequential patterns in customer behavior over time. Unlike traditional methods that evaluate each transaction in isolation, RNNs consider the temporal context – how current activity relates to past behavior and typical sequences. This approach proves especially effective in identifying account takeover fraud, where criminals gradually escalate their activities to avoid detection. The RNN might notice that a series of seemingly innocent actions – changing contact information, adding a new payee, and subsequently initiating a large transfer – collectively indicate a coordinated attack.
The synergy between human expertise and machine intelligence has emerged as the most effective fraud prevention strategy. While ML algorithms excel at processing massive data volumes and identifying patterns, human investigators provide contextual understanding, intuition, and the ability to recognize entirely unprecedented fraud schemes. Leading financial institutions therefore implement augmented intelligence systems where ML algorithms handle initial screening and prioritization, while human experts focus on the most complex and ambiguous cases. This collaboration maximizes the strengths of both approaches while mitigating their respective weaknesses.
Nevertheless, implementing ML-based fraud detection presents significant challenges. The scarcity of labeled fraud data creates training obstacles – fraudulent transactions typically represent less than 0.1% of all activities, creating a severe class imbalance that can bias models toward simply classifying everything as legitimate. Data scientists employ various techniques to address this, including synthetic data generation, anomaly detection approaches that don’t require labeled fraud examples, and ensemble methods that combine multiple models to improve accuracy.
Model interpretability poses another critical concern, particularly regarding regulatory compliance. When an ML algorithm denies a transaction, financial institutions must often explain the decision to customers and regulators. However, complex models like deep neural networks function as “black boxes,” making it difficult to articulate why specific predictions were made. Researchers are actively developing explainable AI techniques that provide transparent, understandable justifications for algorithmic decisions without sacrificing predictive performance.
The evolutionary nature of fraud requires continuous model updates and retraining. Fraudsters adapt their strategies in response to detection systems, rendering static models obsolete. Financial institutions must therefore establish robust infrastructure for monitoring model performance, detecting concept drift when patterns change, and rapidly deploying updated models. Some organizations employ online learning systems that continuously incorporate new data and adapt in real-time, maintaining effectiveness against emerging threats.
Looking ahead, the integration of AI in fraud detection will likely expand beyond individual institutions to industry-wide collaborative networks. Federated learning enables multiple banks to collectively train ML models on their combined data while keeping individual customer information private and secure. This approach harnesses the power of broader datasets to identify cross-institutional fraud patterns while respecting privacy regulations and competitive considerations. As these technologies mature, the financial industry moves closer to creating a comprehensive, adaptive ecosystem that stays perpetually ahead of criminal innovation.
Hệ thống machine learning phát hiện gian lận tài chính trong IELTS Reading
Questions 14-18: Yes/No/Not Given
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
14. Rule-based fraud detection systems are completely ineffective against modern cybercriminals.
15. Machine learning algorithms can identify fraud patterns that human analysts might miss.
16. Deep learning systems can analyze customers’ typing patterns to detect fraud.
17. Recurrent neural networks are more expensive to implement than traditional systems.
18. The best fraud prevention strategy combines machine learning with human expertise.
Questions 19-23: Matching Headings
The passage has eleven paragraphs, A-K.
Choose the correct heading for paragraphs B-F from the list of headings below.
Write the correct number, i-ix.
List of Headings:
i. The limitations of traditional fraud detection methods
ii. Future collaboration between financial institutions
iii. Training challenges with imbalanced datasets
iv. Real-time transaction analysis capabilities
v. Deep learning applications in behavioral analysis
vi. The cost of implementing machine learning systems
vii. How supervised learning models work in fraud detection
viii. Sequential pattern recognition for account takeover
ix. Customer complaints about false fraud alerts
19. Paragraph B
20. Paragraph C
21. Paragraph D
22. Paragraph E
23. Paragraph F
Questions 24-26: Summary Completion
Complete the summary below.
Choose NO MORE THAN TWO WORDS from the passage for each answer.
Machine learning algorithms have transformed fraud detection in financial services. Unlike traditional systems, ML models can process transactions (24)__ as they happen, calculating risk scores within milliseconds. One significant challenge is the (25)__ of fraud data, as fraudulent transactions represent less than 0.1% of all activities. Another concern is **(26)__, which is important for explaining algorithmic decisions to customers and regulators.
PASSAGE 3 – AI-Driven Algorithmic Trading and Market Stability
Độ khó: Hard (Band 7.0-9.0)
Thời gian đề xuất: 23-25 phút
The proliferation of artificial intelligence in financial markets has fundamentally reconstituted the landscape of securities trading, with algorithmic systems now accounting for approximately 60-73% of equity trading volume in developed markets. This paradigmatic transformation has generated intense scholarly debate regarding its implications for market efficiency, liquidity, and systemic stability. While proponents extol AI-driven trading for enhancing price discovery and reducing transaction costs, critics harbor concerns about amplified volatility, potential market manipulation, and the emergence of unforeseen systemic risks that could precipitate financial crises of unprecedented magnitude.
Algorithmic trading encompasses a broad spectrum of strategies executed by computer programs with minimal human intervention. At its most fundamental level, high-frequency trading (HFT) firms deploy sophisticated algorithms that execute thousands of transactions per second, capitalizing on minuscule price discrepancies across different markets or securities. These systems leverage cutting-edge technology including co-location services – placing servers in physical proximity to exchange computers to minimize latency – and direct data feeds that provide microsecond advantages over conventional market participants. The ultra-short holding periods, sometimes measured in milliseconds, distinguish HFT from traditional investment strategies and raise profound questions about the nature of market participation and price formation.
Machine learning algorithms have exponentially enhanced the sophistication of trading strategies by enabling systems to identify complex, non-linear patterns in vast datasets that transcend human analytical capabilities. Reinforcement learning models, for instance, can optimize trading strategies through continuous interaction with market environments, learning from outcomes to progressively refine their decision-making processes. These systems analyze multitudinous data sources including historical prices, macroeconomic indicators, sentiment analysis from news articles and social media, satellite imagery of retail parking lots, and even meteorological data to forecast commodity prices. The multidimensional analysis generates trading signals of remarkable granularity and predictive power.
The impact of AI-driven trading on market microstructure presents a nuanced picture with both beneficial and deleterious effects. Empirical research indicates that algorithmic trading has generally improved market liquidity by narrowing bid-ask spreads – the difference between buying and selling prices – and increasing the depth of order books. Enhanced liquidity facilitates more efficient capital allocation and reduces trading costs for all market participants, including institutional investors and retail traders. Furthermore, algorithmic systems contribute to faster price discovery, the process by which markets incorporate new information into security prices, thereby improving informational efficiency.
However, the concentration of trading activity within nanosecond timeframes has generated concerns about artificial liquidity that evaporates during periods of market stress. The “Flash Crash” of May 6, 2010, when major U.S. stock indices plummeted nearly 10% within minutes before rapidly recovering, exemplified the potential fragility introduced by algorithmic trading. Post-incident analysis revealed that the crash was exacerbated by HFT algorithms simultaneously withdrawing liquidity and executing aggressive selling strategies in response to initial price declines. This event underscored the possibility of cascading failures where algorithmic systems interact in unanticipated ways, creating feedback loops that amplify market disruptions rather than dampening them.
The regulatory landscape has struggled to keep pace with technological innovations in algorithmic trading. Traditional securities regulations were designed for human traders making deliberative decisions over hours or days, not algorithms executing thousands of trades per second. Regulatory arbitrage emerges as firms exploit discrepancies between jurisdictions with varying oversight stringency. Moreover, the opacity of proprietary algorithms – closely guarded as trade secrets – impedes regulatory scrutiny. Regulators cannot easily determine whether an algorithm might exhibit destabilizing behavior under specific market conditions without access to its underlying code and logic.
Market manipulation concerns have intensified with the advent of AI-driven trading. Sophisticated algorithms can potentially engage in practices like “spoofing” – placing orders with the intent to cancel them before execution to create false impressions of supply or demand – at speeds and scales imperceptible to human monitors. Machine learning systems might autonomously discover and exploit manipulation strategies without explicit programming, learning that certain deceptive practices enhance profitability. Detecting such behavior requires equally sophisticated surveillance systems, creating an escalating technological arms race between market participants and regulators.
The democratization of algorithmic trading through retail-oriented platforms presents additional challenges and opportunities. Fintech startups now offer AI-powered trading tools to individual investors, theoretically leveling the playing field against institutional players. However, this proliferation raises questions about investor protection when sophisticated algorithms are deployed by users who may not fully comprehend their operational mechanics or risk characteristics. The gamification of trading through user-friendly applications, combined with AI-driven personalized recommendations, may inadvertently encourage excessive risk-taking or herd behavior among retail investors.
From a systemic risk perspective, the increasing homogeneity of algorithmic strategies poses a latent threat to financial stability. If numerous algorithms are trained on similar data using similar machine learning techniques, they may develop correlated trading behaviors. During market stress, these systems might simultaneously execute identical strategies – such as rapid selling of specific securities – creating synchronized market movements of devastating magnitude. The 2020 market turbulence during the COVID-19 pandemic onset provided glimpses of such synchronization, though the full ramifications remain subjects of ongoing research.
Ethical considerations surrounding AI in trading extend beyond regulatory compliance to fundamental questions about market fairness and social utility. Critics argue that HFT extracts profits through technological superiority rather than informational advantage or capital allocation efficiency, essentially imposing a “speed tax” on other market participants. The substantial infrastructure investments required for competitive algorithmic trading – estimated at hundreds of millions of dollars for leading firms – create formidable barriers to entry that concentrate market power among a small number of players. This concentration potentially undermines the idealized notion of competitive markets with numerous participants.
Looking forward, the trajectory of AI in financial trading appears inexorable, with continuing advances in computational power, data availability, and algorithmic sophistication. Quantum computing may soon enable exponentially faster calculations, further compressing trading timeframes and potentially introducing qualitatively new market dynamics. Explainable AI initiatives aim to increase algorithmic transparency, potentially ameliorating regulatory challenges, though commercial incentives for secrecy remain powerful. The optimal regulatory framework likely involves adaptive policies that maintain flexibility in response to rapid technological evolution while establishing robust safeguards against systemic risks. Ultimately, harnessing the benefits of AI-driven trading while mitigating its risks requires collaborative efforts among technologists, financial institutions, regulators, and academics to develop comprehensive understanding of these complex, emergent systems that now form the backbone of global capital markets.
AI trong giao dịch thuật toán và sự ổn định thị trường tài chính IELTS
Questions 27-31: Multiple Choice
Choose the correct letter, A, B, C, or D.
27. According to the passage, what proportion of equity trading in developed markets is now conducted by algorithmic systems?
A. Approximately 30-40%
B. Around 50-60%
C. Between 60-73%
D. Over 80%
28. The term “co-location” refers to:
A. trading from multiple geographic locations
B. placing computer servers near exchange computers
C. collaborating with other trading firms
D. using cloud-based trading systems
29. The Flash Crash of 2010 demonstrated that:
A. algorithmic trading always stabilizes markets
B. human traders react faster than algorithms
C. algorithms can interact in unexpected ways during crises
D. regulatory oversight successfully prevented major losses
30. What does the passage suggest about market manipulation by AI systems?
A. It is impossible with current technology
B. It only occurs when explicitly programmed
C. Algorithms might learn manipulative strategies independently
D. Regulators can easily detect all forms of manipulation
31. The passage indicates that the concentration of algorithmic trading capabilities:
A. promotes market democracy
B. reduces overall market efficiency
C. creates barriers to entry for new participants
D. has been eliminated by fintech startups
Questions 32-36: Matching Features
Match each statement (32-36) with the correct consequence (A-H).
Write the correct letter, A-H.
Consequences:
A. Improved price discovery
B. Cascading market failures
C. Reduced transaction costs
D. Regulatory arbitrage
E. Investor protection concerns
F. Synchronized market movements
G. Enhanced market transparency
H. Increased market volatility
32. Narrower bid-ask spreads due to algorithmic trading
33. Algorithms withdrawing liquidity simultaneously during stress
34. Different regulatory standards across jurisdictions
35. Retail investors using AI tools they don’t fully understand
36. Multiple algorithms trained on similar datasets
Questions 37-40: Short-answer Questions
Answer the questions below.
Choose NO MORE THAN THREE WORDS AND/OR A NUMBER from the passage for each answer.
37. What type of learning model can optimize trading strategies through continuous interaction with markets?
38. What practice involves placing orders with the intention to cancel them to create false market impressions?
39. What future technology might enable exponentially faster trading calculations?
40. What type of initiatives aim to increase the transparency of algorithmic systems?
Answer Keys – Đáp Án
PASSAGE 1: Questions 1-13
- B
- B
- C
- C
- B
- FALSE
- NOT GIVEN
- FALSE
- TRUE
- NOT GIVEN
- data protection regulations
- robust encryption
- training/education
PASSAGE 2: Questions 14-26
- NO
- YES
- YES
- NOT GIVEN
- YES
- i
- vii
- iv
- v
- viii
- instantaneously/in real-time
- scarcity
- model interpretability
PASSAGE 3: Questions 27-40
- C
- B
- C
- C
- C
- C
- B
- D
- E
- F
- reinforcement learning (models)
- spoofing
- quantum computing
- explainable AI (initiatives)
Giải Thích Đáp Án Chi Tiết
Passage 1 – Giải Thích
Câu 1: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: main advantage, AI chatbots, traditional banking customer service
- Vị trí trong bài: Đoạn 1 và 2
- Giải thích: Đoạn 2 nói rõ “These digital assistants are available 24/7” – chatbots có thể hoạt động liên tục không nghỉ. Đáp án A sai vì bài không nói chatbots thông minh hơn con người. C sai vì bài đề cập hybrid model (kết hợp). D không được nhắc đến.
Câu 2: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: Bank of America, Erica
- Vị trí trong bài: Đoạn 3, câu 2-3
- Giải thích: Bài viết nói “Erica has handled over one billion client requests since its launch” – một ví dụ thành công. Các đáp án khác mâu thuẫn với thông tin trong bài.
Câu 3: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: complex financial problems
- Vị trí trong bài: Đoạn 6, câu 2
- Giải thích: “Complex financial situations still require human expertise and empathy” – vấn đề phức tạp vẫn cần chuyên gia con người.
Câu 4: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: psychological benefit
- Vị trí trong bài: Đoạn 5, câu 3
- Giải thích: “Chatbots also provide a non-judgmental environment where users feel comfortable asking basic financial questions they might hesitate to ask human representatives” – được paraphrase thành “less embarrassed”.
Câu 5: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: future, banking chatbots
- Vị trí trong bài: Đoạn 7, câu 2
- Giải thích: “Natural Language Processing technology continues to advance, enabling chatbots to understand context, detect emotion, and engage in more natural conversations” – ngụ ý chatbots sẽ tự nhiên hơn.
Câu 6: FALSE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: cost, less than one-tenth
- Vị trí trong bài: Đoạn 4, câu 2
- Giải thích: Bài nói “$0.50-$0.70 compared to $5-$12” – chỉ khoảng 1/10 đến 1/6, không phải “less than one-tenth” (dưới 1/10).
Câu 7: NOT GIVEN
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: all major banks, AI chatbots
- Vị trí trong bài: Không có thông tin cụ thể
- Giải thích: Bài chỉ nói “Major banks worldwide have embraced” nhưng không khẳng định TẤT CẢ các ngân hàng lớn.
Câu 8: FALSE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: eliminate all types, human errors
- Vị trí trong bài: Đoạn 4, câu 3
- Giải thích: Bài nói “eliminate human errors in routine transactions” – chỉ trong giao dịch thông thường, không phải TẤT CẢ các loại lỗi.
Câu 9: TRUE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: chatbots and humans work together
- Vị trí trong bài: Đoạn 6, câu 4
- Giải thích: “Most banks therefore implement a hybrid model” – hầu hết ngân hàng sử dụng mô hình kết hợp.
Câu 10: NOT GIVEN
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: voice-activated, currently available, all banks
- Vị trí trong bài: Đoạn 7
- Giải thích: Bài nói “Some institutions are experimenting” – một số tổ chức đang thử nghiệm, không phải tất cả đã triển khai.
Câu 11: data protection regulations
- Dạng câu hỏi: Sentence Completion
- Từ khóa: strict, protect customer information
- Vị trí trong bài: Đoạn 8, câu 2
- Giải thích: “these systems comply with strict data protection regulations”
Câu 12: robust encryption
- Dạng câu hỏi: Sentence Completion
- Từ khóa: keep customer data safe
- Vị trí trong bài: Đoạn 8, câu 2
- Giải thích: “employ robust encryption to safeguard sensitive customer information”
Câu 13: training/education
- Dạng câu hỏi: Sentence Completion
- Từ khóa: require, use chatbot systems effectively
- Vị trí trong bài: Đoạn 9, câu 1-2
- Giải thích: “Training and education represent another critical aspect” và “Customers also need guidance”
Passage 2 – Giải Thích
Câu 14: NO
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: completely ineffective, rule-based systems
- Vị trí trong bài: Đoạn B
- Giải thích: Bài nói rule-based systems “increasingly inadequate” (ngày càng không đầy đủ) chứ không phải “completely ineffective” (hoàn toàn không hiệu quả).
Câu 15: YES
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: identify patterns, human analysts might miss
- Vị trí trong bài: Đoạn C, câu 3
- Giải thích: “ML algorithms learn to identify subtle patterns and correlations that human analysts might overlook”
Câu 16: YES
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: deep learning, typing patterns, detect fraud
- Vị trí trong bài: Đoạn E, câu 2
- Giải thích: “behavioral biometrics like typing rhythm” – deep learning có thể phân tích nhịp gõ phím.
Câu 17: NOT GIVEN
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: more expensive, RNNs
- Vị trí trong bài: Không có thông tin
- Giải thích: Bài không so sánh chi phí triển khai giữa RNN và hệ thống truyền thống.
Câu 18: YES
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: best strategy, combines ML with human expertise
- Vị trí trong bài: Đoạn G, câu 1
- Giải thích: “The synergy between human expertise and machine intelligence has emerged as the most effective fraud prevention strategy”
Câu 19: i
- Dạng câu hỏi: Matching Headings
- Paragraph B: Đoạn này nói về các hạn chế của rule-based systems – “false positives”, không phát hiện được “novel fraud patterns”.
Câu 20: vii
- Dạng câu hỏi: Matching Headings
- Paragraph C: Đoạn này giải thích cách supervised learning models hoạt động – “trained on vast datasets”, “learn to identify subtle patterns”.
Câu 21: iv
- Dạng câu hỏi: Matching Headings
- Paragraph D: Đoạn này tập trung vào “real-time processing capability” – “analyze transactions instantaneously”, “within milliseconds”.
Câu 22: v
- Dạng câu hỏi: Matching Headings
- Paragraph E: Đoạn này nói về deep learning và “behavioral biometrics” – typing rhythm, mouse movement patterns.
Câu 23: viii
- Dạng câu hỏi: Matching Headings
- Paragraph F: Đoạn này nói về RNNs phân tích “sequential patterns” để phát hiện “account takeover fraud”.
Câu 24: instantaneously / in real-time
- Dạng câu hỏi: Summary Completion
- Từ khóa: process transactions, as they happen
- Vị trí trong bài: Đoạn D, câu 2
- Giải thích: “analyze transactions instantaneously as they occur”
Câu 25: scarcity
- Dạng câu hỏi: Summary Completion
- Từ khóa: challenge, fraud data
- Vị trí trong bài: Đoạn H, câu 2
- Giải thích: “The scarcity of labeled fraud data creates training obstacles”
Câu 26: model interpretability
- Dạng câu hỏi: Summary Completion
- Từ khóa: explaining decisions, customers and regulators
- Vị trí trong bài: Đoạn I, câu 1
- Giải thích: “Model interpretability poses another critical concern, particularly regarding regulatory compliance”
Passage 3 – Giải Thích
Câu 27: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: proportion, equity trading, developed markets
- Vị trí trong bài: Đoạn A, câu 1
- Giải thích: “algorithmic systems now accounting for approximately 60-73% of equity trading volume”
Câu 28: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: co-location
- Vị trí trong bài: Đoạn B, câu 3
- Giải thích: “co-location services – placing servers in physical proximity to exchange computers”
Câu 29: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: Flash Crash 2010
- Vị trí trong bài: Đoạn E, câu 3
- Giải thích: “Post-incident analysis revealed… HFT algorithms… interact in unanticipated ways” – tương tác theo cách không lường trước.
Câu 30: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: market manipulation, AI systems
- Vị trí trong bài: Đoạn G, câu 2
- Giải thích: “Machine learning systems might autonomously discover and exploit manipulation strategies without explicit programming”
Câu 31: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: concentration, algorithmic trading capabilities
- Vị trí trong bài: Đoạn J, câu 3
- Giải thích: “substantial infrastructure investments… create formidable barriers to entry”
Câu 32: C
- Dạng câu hỏi: Matching Features
- Vị trí trong bài: Đoạn D, câu 2
- Giải thích: “narrowing bid-ask spreads” dẫn đến “reduces trading costs” – giảm chi phí giao dịch.
Câu 33: B
- Dạng câu hỏi: Matching Features
- Vị trí trong bài: Đoạn E, câu 3
- Giải thích: “HFT algorithms simultaneously withdrawing liquidity” gây ra “cascading failures” – thất bại dây chuyền.
Câu 34: D
- Dạng câu hỏi: Matching Features
- Vị trí trong bài: Đoạn F, câu 3
- Giải thích: “Regulatory arbitrage emerges as firms exploit discrepancies between jurisdictions” – chênh lệch tiêu chuẩn quản lý.
Câu 35: E
- Dạng câu hỏi: Matching Features
- Vị trí trong bài: Đoạn H, câu 2
- Giải thích: “raises questions about investor protection when… users may not fully comprehend” – quan ngại bảo vệ nhà đầu tư.
Câu 36: F
- Dạng câu hỏi: Matching Features
- Vị trí trong bài: Đoạn I, câu 2
- Giải thích: “numerous algorithms… trained on similar data… may develop correlated trading behaviors… synchronized market movements”
Câu 37: reinforcement learning (models)
- Dạng câu hỏi: Short-answer Questions
- Từ khóa: optimize trading strategies, continuous interaction
- Vị trí trong bài: Đoạn C, câu 2
- Giải thích: “Reinforcement learning models… can optimize trading strategies through continuous interaction with market environments”
Câu 38: spoofing
- Dạng câu hỏi: Short-answer Questions
- Từ khóa: placing orders, intention to cancel, false impressions
- Vị trí trong bài: Đoạn G, câu 2
- Giải thích: “spoofing – placing orders with the intent to cancel them before execution to create false impressions”
Câu 39: quantum computing
- Dạng câu hỏi: Short-answer Questions
- Từ khóa: future technology, exponentially faster calculations
- Vị trí trong bài: Đoạn K, câu 2
- Giải thích: “Quantum computing may soon enable exponentially faster calculations”
Câu 40: explainable AI (initiatives)
- Dạng câu hỏi: Short-answer Questions
- Từ khóa: increase transparency, algorithmic systems
- Vị trí trong bài: Đoạn K, câu 3
- Giải thích: “Explainable AI initiatives aim to increase algorithmic transparency”
Từ Vựng Quan Trọng Theo Passage
Passage 1 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| integration | n | /ˌɪntɪˈɡreɪʃn/ | sự tích hợp, hội nhập | The integration of AI into banking | system integration, full integration |
| revolutionize | v | /ˌrevəˈluːʃənaɪz/ | cách mạng hóa | AI has revolutionized customer service | revolutionize the industry |
| sophisticated | adj | /səˈfɪstɪkeɪtɪd/ | tinh vi, phức tạp | increasingly sophisticated chatbots | sophisticated technology |
| automated | adj | /ˈɔːtəmeɪtɪd/ | tự động hóa | automated responses | automated system |
| embrace | v | /ɪmˈbreɪs/ | chấp nhận, đón nhận | banks have embraced this technology | embrace change |
| substantial | adj | /səbˈstænʃl/ | đáng kể, lớn lao | substantial cost savings | substantial improvement |
| compelling | adj | /kəmˈpelɪŋ/ | hấp dẫn, thuyết phục | compelling advantages | compelling reason |
| seamlessly | adv | /ˈsiːmləsli/ | liền mạch, trơn tru | seamlessly transfer cases | work seamlessly |
| irreplaceable | adj | /ˌɪrɪˈpleɪsəbl/ | không thể thay thế | irreplaceable value of human judgment | irreplaceable asset |
| robust | adj | /rəʊˈbʌst/ | mạnh mẽ, vững chắc | robust encryption | robust system |
| paramount | adj | /ˈpærəmaʊnt/ | quan trọng nhất | privacy concerns remain paramount | of paramount importance |
Passage 2 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| persistent | adj | /pəˈsɪstənt/ | dai dẳng, bền bỉ | persistent challenges | persistent problem |
| advent | n | /ˈædvent/ | sự ra đời, xuất hiện | the advent of machine learning | the advent of technology |
| usher in | phrasal v | /ˈʌʃər ɪn/ | mở ra (kỷ nguyên mới) | has ushered in a new era | usher in a new age |
| vulnerability | n | /ˌvʌlnərəˈbɪləti/ | điểm yếu, lỗ hổng | leaving significant vulnerabilities | security vulnerability |
| correlation | n | /ˌkɒrəˈleɪʃn/ | mối tương quan | identify correlations | strong correlation |
| paradigm shift | n | /ˈpærədaɪm ʃɪft/ | sự thay đổi mô hình | represents a paradigm shift | undergo a paradigm shift |
| proactive | adj | /prəʊˈæktɪv/ | chủ động, tích cực | from reactive to proactive security | proactive approach |
| instantaneously | adv | /ˌɪnstənˈteɪniəsli/ | ngay lập tức | analyze transactions instantaneously | respond instantaneously |
| anomaly | n | /əˈnɒməli/ | bất thường, dị thường | detect the anomaly | detect anomalies |
| synergy | n | /ˈsɪnədʒi/ | sự hiệp lực | synergy between human and AI | create synergy |
| scarcity | n | /ˈskeəsəti/ | sự khan hiếm | scarcity of labeled fraud data | data scarcity |
| interpretability | n | /ɪnˌtɜːprɪtəˈbɪləti/ | tính diễn giải được | model interpretability | improve interpretability |
| black box | n | /blæk bɒks/ | hộp đen (khó hiểu) | function as black boxes | black box system |
| concept drift | n | /ˈkɒnsept drɪft/ | sự trôi khái niệm | detecting concept drift | monitor concept drift |
| federated learning | n | /ˈfedəreɪtɪd ˈlɜːnɪŋ/ | học liên kết | Federated learning enables | federated learning approach |
Passage 3 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| proliferation | n | /prəˌlɪfəˈreɪʃn/ | sự gia tăng nhanh | proliferation of AI | nuclear proliferation |
| paradigmatic | adj | /ˌpærədɪɡˈmætɪk/ | mang tính mô hình | paradigmatic transformation | paradigmatic example |
| extol | v | /ɪkˈstəʊl/ | ca ngợi, tán dương | proponents extol AI trading | extol the virtues |
| harbor concerns | phrase | /ˈhɑːbə kənˈsɜːnz/ | ôm lo lắng | critics harbor concerns | harbor doubts |
| capitalize on | phrasal v | /ˈkæpɪtəlaɪz ɒn/ | tận dụng | capitalizing on price discrepancies | capitalize on opportunities |
| leverage | v | /ˈliːvərɪdʒ/ | tận dụng, khai thác | leverage cutting-edge technology | leverage resources |
| latency | n | /ˈleɪtənsi/ | độ trễ | minimize latency | reduce latency |
| transcend | v | /trænˈsend/ | vượt qua | transcend human capabilities | transcend boundaries |
| multitudinous | adj | /ˌmʌltɪˈtjuːdɪnəs/ | vô số, rất nhiều | multitudinous data sources | multitudinous possibilities |
| granularity | n | /ˌɡrænjuˈlærəti/ | độ chi tiết | remarkable granularity | data granularity |
| microstructure | n | /ˈmaɪkrəʊˌstrʌktʃə/ | vi cấu trúc | market microstructure | microstructure analysis |
| deleterious | adj | /ˌdeləˈtɪəriəs/ | có hại | deleterious effects | deleterious impact |
| exacerbate | v | /ɪɡˈzæsəbeɪt/ | làm trầm trọng thêm | was exacerbated by algorithms | exacerbate the problem |
| opacity | n | /əʊˈpæsəti/ | tính mờ đục, khó hiểu | opacity of algorithms | regulatory opacity |
| impede | v | /ɪmˈpiːd/ | cản trở | impedes regulatory scrutiny | impede progress |
| imperceptible | adj | /ˌɪmpəˈseptəbl/ | không thể nhận ra | imperceptible to human monitors | almost imperceptible |
| homogeneity | n | /ˌhɒmədʒəˈniːəti/ | tính đồng nhất | increasing homogeneity | genetic homogeneity |
| latent threat | n | /ˈleɪtənt θret/ | mối đe dọa tiềm ẩn | poses a latent threat | latent danger |
| inexorable | adj | /ɪnˈeksərəbl/ | không thể ngăn cản | trajectory appears inexorable | inexorable progress |
| ameliorate | v | /əˈmiːliəreɪt/ | cải thiện | potentially ameliorating | ameliorate conditions |
Kết Bài
Chủ đề “The role of AI in improving financial services” không chỉ phản ánh xu hướng công nghệ hiện đại mà còn là một trong những chủ đề thường xuyên xuất hiện trong IELTS Reading. Qua bộ đề thi mẫu này, bạn đã được trải nghiệm đầy đủ ba cấp độ khó – từ bài đọc cơ bản về chatbots trong dịch vụ khách hàng, đến phân tích trung bình về machine learning phát hiện gian lận, và cuối cùng là bài đọc học thuật phức tạp về giao dịch thuật toán.
Ba passages với tổng cộng 40 câu hỏi đa dạng đã giúp bạn làm quen với các dạng bài thường gặp: Multiple Choice, True/False/Not Given, Yes/No/Not Given, Matching Headings, Sentence Completion, Summary Completion và Short-answer Questions. Mỗi dạng câu hỏi đòi hỏi kỹ năng đọc hiểu khác nhau – từ tìm thông tin chi tiết, phân biệt ý kiến và sự thật, đến khả năng paraphrase và suy luận.
Phần đáp án chi tiết không chỉ cung cấp đáp án đúng mà còn giải thích rõ ràng vị trí thông tin, cách paraphrase giữa câu hỏi và passage, giúp bạn hiểu sâu hơn về chiến lược làm bài. Hơn 40 từ vựng quan trọng được tổng hợp kèm phiên âm, nghĩa và cách sử dụng sẽ giúp bạn nâng cao vốn từ vựng học thuật.
Hãy sử dụng bộ đề này như một công cụ tự học hiệu quả: làm bài trong điều kiện giống thi thật (60 phút), sau đó đối chiếu đáp án và học từ những sai lầm. Việc luyện tập đều đặn với các đề thi chất lượng như thế này sẽ giúp bạn tự tin hơn và đạt band điểm mong muốn trong kỳ thi IELTS sắp tới. Chúc bạn học tập hiệu quả và thành công!
