AI Made to Reduce False Positives, Part 1: Detection Capabilities and Use Cases
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.
Subscription required
Access to this content requires a Celent research subscription.Subscribers should sign in to access this research.If you are not a subscriber, register now or contact us to find out more about our subscription options.