Monitoring e-communication is an integral part of an organization’s compliance function, and its efficiency plays a crucial role in mitigating risk. The increasing work-from-home trend has placed employees several miles apart, and more team members use communication mediums outside the business’s regulated avenues. Consequently, observing employees and gauging their behavior has become challenging, and the onus for e-communication surveillance monitoring lies in machine learning technology.
Machine learning has led to the emergence of a proactive form of e-communication surveillance designed to pinpoint potential misconduct before it escalates. Let’s examine the role of machine learning and how it contributes to a modern e-communication surveillance program.
Provides Multi-Channel Coverage
Earlier, emails were the primary form of e-communication between organizations. They were much simpler to use and monitor. However, today, multiple e-communication mediums like Zoom, WhatsApp, Google Meet, etc., have become integral parts of communication platforms, requiring all of them to stay connected with the surveillance program. Machine learning provides modern solutions to monitor these channels and continually adds new sources as they develop.
Facilitates Predictive Analysis
The oldest methods of e-communication surveillance monitoring were primarily lexicon and keyword based. However, considering today’s more comprehensive coverage requirements, they often contain pre-defined words and phrases that are challenging to adjust and generate several false alerts each month. Modern machine learning solutions have moved past the lexicon model and rely more on predictive analysis. AI algorithms apply to the content that filter for the relevant data faster. It enables the system to identify behavior patterns that could potentially head off a breach.
Builds Accurate Risk Models
Machine learning algorithms use advanced surveillance systems to identify non-linear, complex patterns in a data set and build accurate risk models. Whether the employees use personal email addresses or communicate at odd hours, machine learning helps detect risk through real-time, data-driven monitoring.
As the algorithms get exposed to increased datasets, the accuracy of predictions enhances over time, transforming risk management across different business lines. Compliance experts with access to intelligent ML surveillance tools can efficiently stop any threat before it crystallizes into a regulatory breach.
Adopts Out-of-the-Box Compliance Policies
Out-of-the-box compliance policies are an integral part of a robust surveillance monitoring tool. Whether it is market abuse, misconduct, or insider trading, the ML-enabled platform can program and update it as required in the future. It contains in-built workflow templates, tools, and practices to uncover any regulatory breach immediately.
Advanced surveillance monitoring tools create policies that use metadata, relationship mapping, sentiment analysis, and multiple communication mediums. Machine learning algorithms are trained to identify behavioral patterns that identify any misconduct before it escalates. Besides that, the algorithms learn and gain more accuracy over time, ultimately minimizing false positives.
Earlier, e-communication surveillance monitoring teams had gated systems to hand over the investigations to legal teams and assess the case from scratch. Although both teams had overlapping objectives, the transfer process often resulted in a loss of time, information, and money. Machine learning allows legal, compliance, IT, risk management, and HR teams to access the same data through a unified control room. A modern solution sits on top of it to sort and analyze data as it enters the archive. It prevents any data loss while allowing any modifications as required.
Supports Multi-Lingual Coverage
Highly regulated sectors have multinational clients and employees, accepting communication in different languages. Therefore, a surveillance team must have search experts with knowledge of different languages and syntax. Only then can they construct algorithms and identify potential breaches.
Multi-lingual capabilities are crucial for cross-border litigation and communication surveillance across geographies. ML-enabled surveillance platforms craft search terms and filters across communication channels in multiple languages. Sometimes, they translate the data into the compliance team’s desired language.
Enables Voice Surveillance
Voice surveillance is one of the most extensive frontiers of completing a regulatory compliance function. Screening calls is one of the most challenging elements of communication surveillance monitoring, as they tend to be more unpredictable, free-flowing, and surrounded by noisy rooms. Standards call recordings are poor in quality, further deteriorating after compression required for storage. Machine learning comprehensibly captures audio and voice data and makes it more digestible through redaction and transcription. The increasing work-from-home environment has moved communication into employees’ homes. Consequently, monitoring becomes more challenging and carries a higher risk of misconduct. The rising number of communication mediums poses an additional challenge for e-communication surveillance monitoring experts. Solutions supporting machine learning generate more accurate surveillance systems that are easier to use for reviewers and deliver better risk management.
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