For exchanges and trading venues, the ability to maintain a fair, transparent and safe market is critical to attracting liquidity.
Our SMARTS Market Surveillance solutions provide you with a robust platform to manage cross-market, cross-asset, multi-venue surveillance.
Powerful visualization tools simplify the monitoring process by distilling complex information into a single snapshot that provides you with clear guidance on where to focus an investigation. You can also correlate real-time and historical data with detection patterns to ensure early detection of unusual trading patterns that could be potential breaches of exchange trading rules and practices.
How Can Nasdaq Market Surveillance help you?
Identify of manipulative patterns in real-time with context surrounding irregular activities
Leverage interactive visualizations for clear graphic support of market analysis and easy comparison of potentially abusive scenarios to normal market behavior
Manage the case work flow via a detailed event analysis and arrangement, which can be leveraged and evidentiary support in regulatory cases
Drill down to the order book and replay any transaction for any security during any time period – including a complete audit trail and consolidation across different venues and books
Compare a traders' behavior against their history and their peers to identify uncharacteristic or high risk behavior
Gain market insights
Gain visibility into event behavior and flag risky changes via data and analytics
We are progressing on a broader machine learning strategy and exploring other applications of this technology to strengthen the surveillance process for markets worldwideFind out more
Robust and flexible workflow, supported by market-leading analysis and visualization tools
Ability to load and process billions of messages daily and to identify undesirable behavior via a suite of sophisticated alerts
Operability & Resilience
Proven in a number of massive volume implementations, including those across Nasdaq markets
Our clients speak for themselves:
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