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AIF-C01 · Question #208

A financial company is developing a fraud detection system that flags potential fraud cases in credit card transactions. Employees will evaluate the flagged fraud cases. The company wants to…

The correct answer is C. Precision. Precision measures the proportion of flagged cases that are truly fraudulent (TP / [TP + FP]). Maximizing precision reduces the number of false positives employees must review, cutting down wasted effort on non-fraudulent cases.

Submitted by kwame.gh· Mar 30, 2026Model Evaluation

Question

A financial company is developing a fraud detection system that flags potential fraud cases in credit card transactions. Employees will evaluate the flagged fraud cases. The company wants to minimize the amount of time the employees spend reviewing flagged fraud cases that are not actually fraudulent. Which evaluation metric meets these requirements?

Options

  • ARecall
  • BAccuracy
  • CPrecision
  • DLift chart

How the community answered

(41 responses)
  • A
    5% (2)
  • B
    2% (1)
  • C
    80% (33)
  • D
    12% (5)

Explanation

Precision measures the proportion of flagged cases that are truly fraudulent (TP / [TP + FP]). Maximizing precision reduces the number of false positives employees must review, cutting down wasted effort on non-fraudulent cases.

Topics

#ML evaluation metrics#Precision metric#Fraud detection ML#False positives

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