Preview Questions Fraud Analyst | Role Specific Skill

Question 1: In fraud detection, how can you use predictive analytics to estimate the likelihood of a customer committing fraud in the future?

Which action should you take?

Choose only one option

By reviewing the customer's relationship with the bank

By analyzing demographic data

By analyzing spending behavior over time

By evaluating historical fraud cases

Question 2: Under the Bank Secrecy Act (BSA), which reporting requirement must be fulfilled when a customer conducts a transaction involving suspicious activity?

Which action should you take?

Choose only one option

File a Customer Due Diligence (CDD) report

File a Suspicious Activity Report (SAR)

File a Currency Transaction Report (CTR)

Submit an annual financial summary

Question 3: When analyzing a large dataset for fraud, which is the best method to reduce false positives while maintaining high detection rates?

Which action should you take?

Choose only one option

Feature engineering and selection

Use of decision trees with pruning

Regularization techniques in predictive models

Cross-validation of classification models

Question 4: In fraud analysis, what is the main benefit of using predictive analytics?

Which action should you take?

Choose only one option

Anticipating fraudulent activities before they happen

Identifying only high-value fraud cases

Generating monthly fraud reports

Detecting fraud only after it has occurred

Question 5: In fraud detection, how can a Fraud Analyst use historical data to predict future fraud risks?

Which action should you take?

Choose only one option

By predicting the likelihood of fraud based solely on customer income levels

By relying only on customer feedback

By randomly sampling previous fraud cases

By identifying patterns and trends in previous fraud cases

Question 6: What is the primary benefit of using machine learning models for fraud detection in terms of analytical thinking?

Which action should you take?

Choose only one option

They only detect low-risk fraud cases

They only require minimal data for analysis

They are fully interpretable and easy to understand

They automatically improve and adapt to new fraud patterns