Chapter 22: Artificial Intelligence (CAIIB – Paper 3)

1. Who is considered the father of Artificial Intelligence?

• A. Alan Turing
• B. John McCarthy
• C. John McCarthy
• D. Marvin Minsky

John McCarthy coined the term "Artificial Intelligence" in 1956 and is considered one of the pioneers of AI.

2. Which of the following is NOT an application of Artificial Intelligence in banking?

• A. Chatbots for customer service
• B. Manual ledger balancing
• C. Fraud detection using pattern recognition
• D. Credit scoring using predictive analytics

Manual ledger balancing is a traditional process and does not involve AI. All other options are AI applications in banking.

3. When did the field of Artificial Intelligence officially begin as a research discipline?

• A. 1940
• B. 1965
• C. 1975
• D. 1956

The field of AI officially started as a research discipline in 1956 at the Dartmouth Conference organized by John McCarthy and colleagues.

4. Which AI technique is commonly used for detecting fraudulent transactions in banks?

• A. Machine Learning and Pattern Recognition
• B. Expert Systems for document management
• C. Natural Language Processing for chatbots
• D. Robotic Process Automation for cash handling

Machine Learning algorithms analyze transaction patterns to identify anomalies, making them effective for fraud detection.

5. Which of the following is a milestone in the history of AI?

• A. Launch of Windows 95
• B. Introduction of internet banking
• C. IBM’s Deep Blue defeating world chess champion Garry Kasparov
• D. Creation of Google Maps

In 1997, IBM’s Deep Blue defeated Garry Kasparov, marking a significant milestone in AI’s capability to outperform humans in complex tasks.

6. Which AI application is commonly used by banks for personalized customer recommendations?

• A. Robotic Process Automation (RPA)
• B. Machine Learning-based Predictive Analytics
• C. Optical Character Recognition (OCR)
• D. Automated Teller Machines (ATM)

Machine Learning models analyze customer transaction history and behavior to provide personalized product recommendations.

7. How does AI help banks in credit risk assessment?

• A. By manually checking credit history
• B. By sending reminder calls to customers
• C. By analyzing large datasets to predict default probabilities
• D. By issuing cheques automatically

AI algorithms analyze historical data, financial statements, and behavioral patterns to estimate the probability of default, improving credit risk assessment.

8. Which AI technology is primarily used in chatbots for banking customer support?

• A. Natural Language Processing (NLP)
• B. Image Recognition
• C. Blockchain
• D. Robotic Process Automation (RPA)

NLP enables chatbots to understand and respond to customer queries in natural language, improving customer service efficiency.

9. In banking, AI-driven fraud detection mainly relies on which of the following?

• A. Manual reconciliation of accounts
• B. Customer feedback forms
• C. Traditional rule-based audits only
• D. Machine Learning algorithms analyzing transaction patterns

AI fraud detection uses machine learning to identify unusual patterns in transactions that may indicate fraud, making it more accurate and faster than traditional methods.

10. Which AI application is used for automating repetitive banking tasks like KYC verification and account opening?

• A. Predictive Analytics
• B. Robotic Process Automation (RPA)
• C. Neural Networks
• D. Speech Recognition

RPA automates repetitive, rule-based tasks such as KYC verification, account opening, and document processing, increasing efficiency and reducing errors.

11. What is one of the main future scopes of AI in banking?

• A. Replacing all bank employees immediately
• B. Eliminating the need for customer service
• C. Enhancing decision-making through predictive analytics
• D. Completely removing human oversight

The future of AI in banking focuses on assisting humans in decision-making, risk management, and providing predictive insights rather than replacing humans entirely.

12. Neural Networks are primarily used in banking for:

• A. Physical security of bank premises
• B. Pattern recognition and predictive modeling
• C. Manual ledger entry
• D. Automated cash counting

Neural Networks are used to detect complex patterns in data, enabling applications like fraud detection, credit scoring, and forecasting financial trends.

13. Which of the following is a future application of AI in wealth management?

• A. Automated portfolio optimization based on client risk profiles
• B. Manual calculation of returns by financial advisors
• C. Physical safekeeping of documents
• D. Traditional telephonic banking only

AI can analyze large datasets and optimize client portfolios automatically based on their risk tolerance, market conditions, and investment goals.

14. Which of the following best describes a Neural Network?

• A. A network of physical bank branches
• B. A rule-based accounting software
• C. A type of blockchain network
• D. A computational model inspired by the human brain to process data patterns

Neural Networks mimic the way the human brain processes information, allowing systems to recognize patterns and make predictions from data.

15. How can AI and Neural Networks improve regulatory compliance in banking?

• A. By eliminating the need for audits entirely
• B. By manually checking all transactions
• C. By monitoring transactions and detecting anomalies automatically
• D. By replacing compliance officers completely

AI can automatically analyze transaction patterns, flag suspicious activities, and ensure adherence to regulatory requirements, enhancing compliance efficiency.

16. What is the primary focus of Control Theory in a banking context?

• A. Human resource management only
• B. Maintaining system stability and achieving desired financial outcomes
• C. Marketing and customer acquisition
• D. Manual ledger entry

Control Theory focuses on designing systems that maintain stability and meet desired objectives, such as liquidity management, risk control, and operational efficiency in banks.

17. Cybernetics in banking is primarily concerned with:

• A. Physical security of branches
• B. Manual account reconciliation
• C. Feedback, control, and communication in financial systems
• D. Printing currency notes

Cybernetics deals with understanding and designing systems that use feedback loops to maintain control, stability, and effective communication within banking operations.

18. Which of the following best defines a Rational Agent in AI?

• A. An entity that perceives its environment and acts to maximize its performance
• B. A manual accountant in the bank
• C. A traditional banking branch
• D. A marketing agent for bank products

In AI, a rational agent perceives its environment and chooses actions that maximize its expected performance, making it useful for decision-making in finance.

19. Which of the following is an example of a Rational Agent application in banking?

• A. Manual passbook updating
• B. Cash counting by tellers
• C. Physical document filing
• D. Automated loan approval system analyzing creditworthiness

Rational agents can automate decision-making, such as approving loans based on credit scores and risk analysis, optimizing performance without human bias.

20. Feedback loops in Cybernetics are important because they:

• A. Increase manual workload
• B. Allow systems to adjust and maintain stability
• C. Are used only for marketing campaigns
• D. Replace all bank employees

Feedback loops provide information about system performance, allowing automatic adjustments to achieve desired outcomes, essential in control and cybernetic systems.

21. In AI, "Motion and Manipulation" primarily refers to:

• A. Employee movement within the bank
• B. Customer behavior analysis
• C. Robotics systems moving and interacting with objects
• D. Financial transaction sequencing

Motion and Manipulation in AI refers to robots or automated systems moving and manipulating objects, such as cash handling robots or automated document sorting systems in banking.

22. Which banking process can benefit from AI-driven motion and manipulation technologies?

• A. Customer service calls
• B. Automated cash counting and sorting
• C. Loan approval decision-making
• D. Marketing campaign planning

Motion and Manipulation technologies are used in banks for automating repetitive physical tasks such as cash sorting, coin counting, and document handling, reducing errors and saving time.

23. What is a key AI technology enabling motion and manipulation in robotics?

• A. Natural Language Processing
• B. Predictive Analytics
• C. Expert Systems
• D. Computer Vision and Sensor-based control

Computer Vision and sensor-based controls allow robots to perceive their environment, detect objects, and manipulate them accurately for tasks such as cash handling.

24. How does AI-based manipulation improve banking efficiency?

• A. By replacing financial decision-making
• B. By handling only digital transactions
• C. By automating repetitive physical tasks and reducing manual errors
• D. By eliminating customer interaction entirely

Motion and manipulation AI systems automate physical tasks such as cash sorting or document handling, improving speed, accuracy, and operational efficiency in banks.

25. Which of the following is a potential future application of motion and manipulation AI in banking?

• A. Generating financial reports manually
• B. Fully automated cash vault handling and teller-less branches
• C. Customer relationship management via phone calls
• D. Manual cheque processing

In the future, AI-enabled robotic systems may handle cash vault operations and enable teller-less branches, reducing human intervention and increasing security and efficiency.

26. Which of the following is a core technique of AI used for decision-making in banking?

• A. Manual ledger balancing
• B. Expert Systems
• C. Traditional telephonic banking
• D. Printing currency notes

Expert Systems use rules and knowledge bases to make decisions or provide recommendations, widely applied in credit evaluation, risk management, and customer support.

27. Which AI technique involves learning patterns from historical data to make predictions?

• A. Machine Learning
• B. Robotic Process Automation
• C. Natural Language Processing
• D. Cybernetics

Machine Learning algorithms analyze historical data to detect patterns and make accurate predictions for credit scoring, fraud detection, and customer behavior analysis.

28. Which tool is commonly used for processing human language in banking AI applications?

• A. Neural Networks for robotics
• B. Machine Learning for stock prediction
• C. Natural Language Processing (NLP)
• D. Expert Systems for decision rules

NLP enables AI systems to understand and respond to human language, making it essential for chatbots, virtual assistants, and automated customer support in banks.

29. Robotic Process Automation (RPA) in banking is mainly used for:

• A. Predictive analytics of market trends
• B. Automating repetitive rule-based tasks
• C. Generating AI-based investment advice
• D. Customer sentiment analysis

RPA automates repetitive and rule-based processes like account opening, KYC verification, and transaction processing, increasing efficiency and reducing errors.

30. What is the primary purpose of Knowledge Representation in AI banking systems?

• A. Physical cash management
• B. Customer relationship management manually
• C. Fraud detection using sensors
• D. Structuring information for reasoning and decision-making

Knowledge Representation involves organizing information in a form that AI systems can use to reason, make decisions, and provide recommendations in banking operations.

31. Why is morality important in Artificial Intelligence systems in banking?

• A. To ensure faster transaction processing
• B. To ensure ethical decision-making and fairness
• C. To reduce manual workload only
• D. To replace human staff completely

Morality in AI ensures that banking systems act ethically, maintain fairness, avoid discrimination, and comply with legal and regulatory standards.

32. Which of the following is a moral concern when using AI in finance?

• A. Faster customer onboarding
• B. Automated cash counting
• C. Bias in credit scoring algorithms
• D. Robotic Process Automation for KYC

AI systems can inherit biases from historical data, which may lead to unfair treatment in credit decisions, loan approvals, or customer profiling.

33. What is a key principle to ensure moral AI in banking?

• A. Transparency and explainability of AI decisions
• B. Reducing transaction time
• C. Automating branch operations
• D. Replacing customer service staff

Transparency and explainability allow stakeholders to understand AI decisions, ensuring ethical and accountable practices in banking operations.

34. Which AI-related ethical challenge is crucial in financial decision-making?

• A. Automating repetitive tasks
• B. Preventing discriminatory outcomes in lending and investment
• C. Faster cheque processing
• D. Robotic cash handling

Ethical AI ensures that lending, investment, and credit decisions are fair, unbiased, and comply with anti-discrimination laws and regulations.

35. How can banks ensure AI systems act morally and ethically?

• A. By relying solely on historical data
• B. By automating all human decisions
• C. By avoiding AI usage entirely
• D. By implementing ethical guidelines, continuous monitoring, and bias mitigation

Banks can ensure moral AI by following ethical frameworks, monitoring AI outputs, and applying bias detection and correction mechanisms in all AI-driven processes.

36. What is the purpose of AI governance in banking?

• A. To automate all banking operations
• B. To ensure AI systems operate safely, ethically, and comply with regulations
• C. To replace human decision-makers entirely
• D. To monitor physical security only

AI governance ensures that AI tools in banks follow ethical standards, legal regulations, and maintain accountability in decision-making processes.

37. Which regulatory body provides guidelines for AI and ethical use in financial institutions?

• A. Food Safety and Standards Authority of India (FSSAI)
• B. Telecom Regulatory Authority of India (TRAI)
• C. Reserve Bank of India (RBI) and SEBI
• D. Ministry of Education

RBI and SEBI provide guidance and regulatory frameworks to ensure safe, ethical, and compliant deployment of AI technologies in banking and finance.

38. How can banks mitigate bias in AI systems?

• A. By ignoring historical data
• B. By automating all human decisions
• C. By outsourcing all AI systems
• D. By monitoring AI outputs, testing datasets, and correcting discriminatory patterns

Bias can be mitigated by continuously monitoring AI decisions, auditing training data, and implementing corrective measures to ensure fairness.

39. Which future AI trend in banking focuses on creating human-like reasoning and understanding?

• A. Robotic Process Automation (RPA)
• B. Generative AI and cognitive agents
• C. Manual ledger maintenance
• D. Optical Character Recognition (OCR)

Generative AI and cognitive agents can simulate human reasoning, generate insights, and provide advanced decision-making support in finance.

40. What is a key consideration for future AI deployment in banking?

• A. Eliminating all human oversight
• B. Reducing AI usage to manual tasks only
• C. Balancing automation, ethical standards, and regulatory compliance
• D. Ignoring data privacy regulations

Future AI deployment must integrate automation with ethical guidelines and regulatory compliance to ensure safe, fair, and efficient banking operations.