Compliance teams at financial institutions are challenged by the ever-increasing volume of communications that must be reviewed to meet their regulatory requirements. Reviewers need an easy-to-use application that allows them to identify violations and risks, and take action as quickly and efficiently as possible. At the same time, Supervision applications must provide the policy sophistication necessary to greatly reduce false-positive search results, and the flexibility to customize workflows and escalation processes for individual groups of supervised participants.

Reviewers also need to plow through a dramatic increase in the number and variety of communication types, such as instant messaging, texts, social media posts and collaboration platforms. Breaking up and converting all of these dynamic communications to the ‘lowest common denominator’ format of email is no longer an effective strategy for maintaining efficient review.

Large financial institutions must go beyond traditional, lexicon-based monitoring of the communications of regulated users. These organizations are seeking to leverage new artificial intelligence (AI) and machine learning (ML)-based technologies, and incorporate more context, metadata and other data sources to surveil a broader population. However, AI-based surveillance platforms can be very expensive to deploy and require significant, iterative processes to identify risks.

What is needed is ‘Superveillance.’ This process allows organizations to rapidly process the huge volume of communications to identify known risks and meet daily regulatory requirements, while integrating with AI-based platforms for deeper analysis and identification of unknown risks.

A Superveillance system requires an integration that allows seamless transfer of relevant subsets of communications and metadata between the supervision and surveillance platforms – including bi-directional transfer and metadata enrichment. An integrated Superveillance strategy provides the ability to correlate an ad hoc set of communications with data from trading systems, newsfeeds and voice capture systems. This enables heightened supervision of high-risk employees and the ability to incorporate sentiment analysis as part of an AI-based risk score within the supervision application. It also allows for organizations to examine activity and trends over a broader set of participants.