That Understands Behavior
Neurensic Score employs a unique approach to trade surveillance that utilizes artificial intelligence.
We use pattern recognition based on machine learning to identify behaviors that pose the greatest regulatory risk to each firm. Our detection system trains itself to recognize high-risk activity from actual regulatory cases and investigations.
This self-adaptive methodology allows Neurensic to learn as it becomes exposed to new data. In this way, the system improves its accuracy even as markets and regulatory trends evolve over time.
Neurensic helps compliance staff reduce cost, eliminate risk, and improve the workflow efficiency of their teams.
Most importantly, our tools provide the entire industry with a unified standard that allows organizations to be proactive with their surveillance strategy.
Neurensic uses artificial intelligence to recognize patterns of trading that are similar to regulatory inquiries and investigations.
The system aggregates order message data into “clusters” of related intent. This makes it easy to understand overall trader behavior, rather than sorting through individual order actions.
We generate a “risk score” for each cluster, that represents the similarity of new activity to known activity that has drawn negative regulatory attention in the past.
At the cluster level, it becomes easy to visualize the overall compliance risk of any cross-section of a firm, including trader, product, or sales group. Our toolset lets users prioritize the most significant threats to their organization so they can take corrective action.
Our cluster scorecard and market replay functionality lets compliance professionals easily visualize trader activity and its impact on other market participants.
This methodology provides complete transparency into firm compliance risk and trader behavior.
Neurensic implementation is designed to be fast and easy, so
customers can get up and running in minutes or hours, rather than months.
We provide custom pricing options for all organizations, including FCMs, global banks, trading firms, and regulators. Our model works for any budget, operation size, or compliance timeline.
SCore Surveillance Model Overviews
The Spoofing similarity model identifies various forms of market abuse that involve false or misleading order activity known as spoofing.
Spoofing with Layering
- Spoofing with Vacuuming
Collapsing of Layers
Abusive Messaging Detection
The Abusive Messaging Detection model identifies patterns of disruptive or excessive order activity.
Microburst Quote Stuffing
Momentum Ignition Detection
The Momentum Ignition Detection model identifies behavior designed to initiate rapid market movement at the expense of other participants.
Igniters and Directional Manipulation
Suspicious Price Movement Detection
The Suspicious Price Movement model identifies executions during unusual market movements which may indicate abusive behavior or erroneous trades.
Pinging and Phishing Detection
The Pinging and Phishing Detection model detects activity designed to take advantage of hidden volume at the expense of slower market participants.
Pinging and Phishing
Wash Trade Detection
The Wash Trade Detection model identifies executions with no change in beneficial ownership.
Wash Sales and Churning
Cross Trade Detection
The Cross Trade Detection model identifies potential cross trades without sufficient delay between order entries.
Closing Period Abuse Detection
The Closing Period Abuse Detection model identifies orders placed with the intent to manipulate market price at or near the close.
- Closing Period Abuse