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  5. AI Made to Reduce False Positives, Part 1: Detection Capabilities and Use Cases

AI Made to Reduce False Positives, Part 1: Detection Capabilities and Use Cases

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30 May 2018
Joan McGowan

With a Helping Hand, Artificial Intelligence Can Substantially Reduce False Positives

Key research questions

  • Why the focus on false positives?
  • What is the cause of stubborn high false positive numbers?
  • What technologies make up a viable solution for reducing false positives?

Abstract

Note: A webinar that draws on this report is available here.

Rules-based scenarios have failed to control false positive rates and the compliance process across the financial services industry has become barely manageable. Celent predicts a rapid uptake of AI capabilities over the next couple of years to help financial institutions alleviate their compliance burden, including the reduction of false positives. In Part 1 of this two-report series, Celent looks at the AI use cases to reduce false positives and unearth false negatives, without changing the risk profile of the institution. Part 2 profiles 13 vendors that have deployed, or are exploring, various combinations of AI techniques to help solve the problem of high false positive numbers.

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