Fraud detection MCQs January 8, 2026November 19, 2024 by u930973931_answers 15 min Score: 0 Attempted: 0/15 Subscribe 1. Which of the following is a common technique used for fraud detection in financial transactions? (A) Clustering (B) Anomaly detection (C) Linear regression (D) Decision trees 2. What does the term “fraudulent behavior” refer to in the context of fraud detection? (A) A pattern of behavior that is always legitimate and trustworthy (B) The regular purchase of high-value items (C) A pattern of activity that deviates from normal behavior and is indicative of a potential crime (D) A customer regularly using a service 3. Which of the following is an important step in the fraud detection process? (A) Predicting customer preferences (B) Product recommendations (C) Data collection and cleaning (D) Optimizing website design 4. In fraud detection, what is the primary role of machine learning algorithms? (A) To identify patterns and anomalies in data that may indicate fraudulent behavior (B) To automate transaction approval (C) To generate marketing strategies (D) To forecast future sales 5. Which of the following is a common approach used in credit card fraud detection? (A) Both A and C (B) Using customer demographics for fraud classification (C) Monitoring transaction frequency and amount patterns (D) Predictive modeling to identify high-risk customers 6. What does “anomaly detection” in fraud detection refer to? (A) Finding the most common transactions (B) Identifying transactions that are different from typical patterns (C) Predicting future transactions (D) Identifying fraudulent actors by name 7. Which of the following methods is most commonly used to detect insurance fraud? (A) Analyzing weather patterns (B) Simple random sampling of claims (C) Predictive analytics to identify patterns of fraudulent claims (D) Conducting face-to-face interviews 8. In fraud detection systems, what does real-time monitoring aim to achieve? (A) To predict customer preferences in advance (B) To analyze long-term trends in customer behavior (C) To instantly identify and stop fraudulent transactions as they occur (D) To perform credit score checks 9. In the context of fraud detection, “false positive” refers to: (A) Incorrectly identifying a legitimate transaction as fraudulent (B) Correctly identifying a legitimate transaction as fraudulent (C) Correctly identifying fraudulent transactions (D) Finding no fraudulent transactions when they exist 10. Which of the following is an example of supervised learning in fraud detection? (A) Using historical labeled data to train a model to classify transactions as fraudulent or legitimate (B) Analyzing transaction data to find outliers without prior knowledge of fraud patterns (C) Grouping transactions into clusters without predefined labels (D) Using unsupervised clustering techniques to find potential fraud patterns 11. Which of the following would be considered a challenge in fraud detection systems? (A) High levels of false positives (B) Detecting only simple fraud patterns (C) Having too little data for training (D) Both A and C 12. Which feature is commonly used in fraud detection models for credit card transactions? (A) Customer’s age (B) Transaction time and location (C) Customer’s favorite stores (D) Customer’s income 13. Which of the following is a real-world example of fraud detection in the banking industry? (A) Predicting future stock market trends (B) Offering customer loans based on credit score (C) Monitoring transactions for signs of money laundering or unauthorized withdrawals (D) Recommending savings accounts based on spending habits 14. Social network analysis is sometimes used in fraud detection to: (A) Determine the best time for transactions (B) Analyze market trends (C) Forecast future transactions (D) Identify relationships and patterns among users involved in fraudulent activities 15. What is a key benefit of using machine learning in fraud detection? (A) It guarantees no false positives (B) It does not require any data preprocessing (C) It can adapt and improve over time with new data (D) It only detects simple fraud patterns