A financial services corporate client completed a review of over 2 million documents as part of an internal investigation of potential insider trading among multiple custodians within its global network of traders. While senior management was convinced of an issue, the 4-week investigation using traditional eDiscovery tools, including a review team of 30 lawyers, did not uncover any evidence of wrongdoing. Riskcovery\u00ae, a patented, AI-powered, contextual analytics platform, changed the outcome by accurately identifying seemingly disconnected patterns of communication and behavior.<\/p>\n<\/div>
Using an insider trading taxonomy defined with the client as key search input, the platform ingested the same documents, including emails and text messages from the prior internal investigation.<\/p>\n<\/div>
Fewer than 75 documents that had previously gone unnoticed were highlighted as potentially responsive.<\/p>\n
The childrens\u2019 birthday parties that weren\u2019t<\/strong><\/em> – The AI-powered solution detected irregular patterns of communication between two traders ostensibly discussing the timing of multiple birthday parties and the size of appropriate birthday gifts. Out of context, human reviewers disregarded the documents as unrelated to the insider trading investigation. By using contextual analysis, however, the pattern of speech was identified as similar to that used to describe insider trading. Once flagged, investigators traced the birthday party dates and gift amounts to specific stock trades made by the employees \u2013 who also, it should be mentioned, did not have children. The insider trading was proactively self-reported to the proper authorities before it resulted in litigation or regulatory penalties.<\/p>\n<\/div>