350-201 Question 49
Select 3A financial company is experiencing a surge in fraudulent transactions and is looking to implement a cybersecurity solution. They want to use AI-powered data analytics to detect anomalies in real-time and prevent fraudulent activities. Which AI-powered techniques would best address their needs?
- A
Supervised machine learning models trained on labeled historical data of fraudulent and legitimate transactions
- B
Unsupervised machine learning techniques to identify anomalies in transaction patterns
- C
Natural Language Processing (NLP) models to analyze user reviews related to fraudulent activities
- D
AI-based predictive analytics to forecast future fraudulent transaction trends
- E
Signature-based detection methods to compare current transactions against known attack patterns
Show answer and explanation
Correct answers: A, B, D
Explanation
The best approach to address the company's needs involves a combination of supervised machine learning, unsupervised machine learning, and predictive analytics. These AI-powered techniques can analyze historical data for patterns, detect anomalies in real-time, and forecast potential fraudulent activity, enabling comprehensive fraud prevention. Signature-based detection and NLP are not appropriate for this specific scenario due to their limitations in addressing the stated requirements.
- A. Correct.
Supervised machine learning is highly effective in identifying fraudulent patterns when trained on labeled historical data, making it suitable for this scenario.
- B. Correct.
Unsupervised machine learning can identify unusual patterns in transactions, even if the system has no prior knowledge of what constitutes fraud, making it a valuable technique.
- C. Incorrect.
While NLP is powerful for analyzing textual data, it does not address the need for real-time fraud detection in transaction datasets.
- D. Correct.
Predictive analytics can help forecast potential fraud trends based on historical data, aiding in proactive prevention.
- E. Incorrect.
Signature-based detection is not AI-powered and is less effective against new or evolving fraudulent activities, making it unsuitable for this use case.