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  5. AI Made to Reduce False Positives, Part 2: Vendor Spectrum

AI Made to Reduce False Positives, Part 2: Vendor Spectrum

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2 July 2018
Joan McGowan

AI Machine-made for the Reduction of False Positives

Key research questions

  • What can AI promise?
  • Why is AI so well-suited to spotting suspicious activities and reducing false positives?
  • How do you win over the regulators?

Abstract

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

The 13 vendors profiled in the report offer a form of advanced data analysis and machine learning techniques for the reduction of false positives. Some vendors focus on access to news content, watchlists, and unstructured data, where others focus on intelligent automation, robotic process automation, or more advanced segmentation analysis. Notably fewer vendors are developing natural language processing and natural language generation techniques. Celent believes the implementation of narrative generation tools are low risk and low cost, and that these tools are suitable for the parsing, analysis, and construction of negative news content and regulatory filing narratives, as well as the generation of suspicious activity reports.

The vendors covered in the report are Arachnys, Ayasdi, Brighterion, FICO, IBM, Intel Saffron, LexisNexis Risk Solutions, NICE Actimize, Oracle, Pelican, Regulatory DataCorp (RDC), SAS, and ThetaRay.

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