Machine learning risk checks in Radar are used to identify which payments?

Machine learning risk checks in Radar are used to identify which payments?

• A. Low-risk payments
• B. All payments
• C. High-risk payments
• D. Recurring payments

Answer: (C)

Machine learning risk checks in Radar are designed to identify payments that may be fraudulent. The models evaluate many signals for each transaction—things like device information, card history, geographic patterns, transaction velocity, and merchant history—and assign a risk score. When a payment scores high, Radar flags it as high risk so you can block it or require extra verification. This focus helps prevent fraud while keeping legitimate customers moving smoothly. While low-risk payments and recurring payments can also be evaluated, the primary purpose of the ML risk checks is to detect high-risk transactions that need extra scrutiny.

Machine learning risk checks in Radar are designed to identify payments that may be fraudulent. The models evaluate many signals for each transaction—things like device information, card history, geographic patterns, transaction velocity, and merchant history—and assign a risk score. When a payment scores high, Radar flags it as high risk so you can block it or require extra verification. This focus helps prevent fraud while keeping legitimate customers moving smoothly. While low-risk payments and recurring payments can also be evaluated, the primary purpose of the ML risk checks is to detect high-risk transactions that need extra scrutiny.