Preview Questions Fraud Analyst | Role Specific Skill

Question 1: Under which regulation do financial institutions need to assess and report the risk of their customers engaging in terrorism financing or money laundering?

Which action should you take?

Choose only one option

E-Sign Act

USA PATRIOT Act

MiFID II (Markets in Financial Instruments Directive)

Financial Services and Markets Act 2000

Question 2: How would you identify risks from multiple low-value fraudulent claims that are masked by legitimate transactions?

Which action should you take?

Choose only one option

Using data mining techniques for anomaly detection

Aggregating transaction patterns across accounts

Reviewing transaction volume and frequency

Employing a risk matrix to identify trends

Question 3: What is the most effective way to identify an increased risk of fraud based on changes in transaction patterns across multiple accounts?

Which action should you take?

Choose only one option

Use a decision tree to categorize accounts

Identify outliers in transaction sizes

Conduct cluster analysis on transaction types

Track changes in account holder behavior

Question 4: What would be an efficient way to detect fraud when analyzing transaction frequency across different geographical locations?

Which action should you take?

Choose only one option

Correlation analysis of transaction timings

Cluster analysis of IP addresses

Analysis of transaction velocity across regions

Multi-dimensional scaling analysis

Question 5: How does the use of predictive analytics improve fraud detection in financial services?

Which action should you take?

Choose only one option

By monitoring only high-risk customers

By estimating the likelihood of fraud occurring based on patterns

By guaranteeing that no fraud will occur in the future

By automating all fraud detection tasks

Question 6: Which anomaly detection method uses historical transaction data to identify unusual behavior?

Which action should you take?

Choose only one option

Linear Regression

Statistical Outlier Detection

Collaborative Filtering

Neural Networks