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.
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Insight details
Banking, >>Retail & Business Banking, >>Corporate Banking, Risk, >>Financial Services Risk