With advancements in machine learning technology, more firms are using behavioral analytics to understand how employees engage and communicate with customers and with each other. Detecting patterns of engagement while processing enormous quantities of data requires context and software platforms able to discern sentiment, emotion and intent. Such deeper understanding of communication subtleties can help compliance officers more accurately predict future behavior and guard against market abuse.
Harald Collet, Global Head of Bloomberg Vault, recently spoke to InformationWeek’s Lisa Morgan about the role of emotional analytics in bridging the gap between human and machine, and its utility in preventing market abuse. Firms that proactively monitor communications through automatic real-time policies and employ emotional analytics are better equipped to mitigate risk and meet changing regulatory requirements. This surveillance and review should apply both to written correspondence exchanged via social media and communication platforms and to voice conversations.
“If you look at sentiment or emotion alone, it essentially flags a lot of false positives. What will make emotional analytics more helpful is to have a stronger analytics model behind it, so you can see whether it’s normal or not,” Collet explained.
“From our side, we’re looking for strain in the voice or anger. In a trading floor situation that’s not so helpful because there’s a lot of banter, so we think emotional analytics will be the most helpful in text-based conversation where we apply other facets,” said Collet. “[That way,] we can say these two people have not had emotion and now there is emotion. Is that a natural progression of the relationship or an outlier?” he added.
Read the full InformationWeek article for additional insights from Collet and other industry experts on the evolution of emotional analytics.