Digital Communications Governance Platform: What you need to look for 

Surveillance teams have many challenges for effective monitoring, especially within the last 5 years. Alerts are higher than ever, and relevance lower than ever. And on top of that, the fines keep piling up. 

Operating models and policies are undergoing their most comprehensive change since the introduction of electronic trading. For compliance functions, it is clear the technology that carried firms through the post-LIBOR scandal years has reached the end of its cycle. Yet the complexity needed in these new solutions can leave leaders’ heads spinning when choosing a new platform.  

What your surveillance solution should include 

Adapting to new technology can be challenging, especially for busy compliance staff who may not have time to learn an entirely new operating system with complex functionalities. Your digital communications governance (DCG) solution should be easy to use and include: 

• Multi-channel coverage 
• Machine learning & predictive analytics 
• Clean data  
• Machine learning applied to risk 
• Security requirements 
• Data governance and management 

Multi-channel coverage 

Back when email was the only form of electronic communication, it was much simpler to design a solution that could legitimately claim to handle all your eComms needs. Today, numerous chat channels exist that older solutions cannot connect with. WhatsApp, Microsoft Teams, and Zoom are three of the most wanted datasets that require ingesting, but there are also many others you may want to request from the vendor. 

Machine learning & predictive analytics 

Older communication surveillance solutions are almost entirely lexicon-based. Given the scope of coverage required today, they are often bloated with pre-defined packs of trigger words and phrases that can be difficult to adjust and often generate tens of thousands of alerts per month. 

In contrast, modern solutions leverage both lexicons and predictive data analytics to identify behavioral patterns, offering a proactive approach to compliance. 

For compliance teams dealing with massive content volumes, ML algorithms outperform traditional methods, swiftly filtering relevant information. While some legacy solutions claim AI capabilities, true advanced surveillance tools deploy behavioral analytics. These algorithms scan communication data for behavioral changes, creating profiles, scorecards, and helping detect potential risks early on. 

Clean data 

Good data management is the heart of effective surveillance; without accurate and comprehensive archive connections, there can be no impactful analysis. 

Dirty data, or bad data, can derail ML algorithms trained on communication and trade data, and hinder their ability to perform (think “garbage in, garbage out”). One of the main roadblocks to a clean dataset is where legacy systems are siloed, and information is split between departments that do not talk to each other. 

Machine learning applied to risk 

Advanced surveillance solutions use ML algorithms to build more accurate risk models by identifying complex, nonlinear patterns within large datasets. From traders using personal email addresses and communicating at strange times to the latency of their responses, risk detection has progressed from outdated, qualitative, manual controls to data-driven, real-time monitoring. 

The accuracy of the ML predictions also grows over time when exposed to more data. When applied to enterprise data – everything flowing through the firm – the tools can help transform the management of risks across multiple business lines. 

Security requirements 

Security concerns regarding Software as a Service (SaaS) deployments are often less about the technology than the business’s attitude towards corporate governance and risk. Internal battles over the future direction of operating systems are commonplace. 

As risk-averse as your business may be, leaders need to know that the SaaS vendors and cloud infrastructure providers they partner with are just as committed to security as they are. Questions you should be asking pertain to the cloud vendor, with Microsoft Azure, AWS, and Google Cloud currently the preferred platforms for large corporations. 

Data governance & management 

Given the exponential increase in data generated every day, communications archiving (also known more broadly as data management) is a red-hot issue. If your company cannot grasp data management, you will be unable to apply the necessary analytics and monitoring. Bad data handling will also overload your compliance team and dull even the sharpest data analytics software. 

It is critical to understand how a communications surveillance system will plug into your archive or provide data management, and how it will filter the various formats of data (text and chat logs, PowerPoint presentations, images, audio and video files, etc.) contained within. 

Choosing the right digital compliance governance platform  

Download the full eBook to learn more about further capabilities to consider, such as native format chat and workflow configuration, as well as a step-by-step guide to deciding what platform to implement and making the case for leadership to invest.