Improve Trade Surveillance Efficiency for Optimised Output
Reduce high false positive count with a better understanding of key triggers and how to more effectively identify market abuse in your business
Approach
Nimble with pointed focus
Regulatory compliance requires an understanding of risks and to have appropriate measures in place to mitigate accordingly. Scientia RegTech specialises in reviewing Trade Surveillance logic & benchmarking output by using techniques rooted in statistical analysis and machine learning to analyse trade data & order data, identify gaps, and enhance logic performance.
This health check and optimisation aims to deliver measurable results, saving time and resources, helping your firm to focus on serving clients.
Different firms, different challenges and different solutions.
No one size fits all, but the four steps below give rich insight to the challenge and next steps.
Logic Review
There could be many reasons for surveillance platforms generating high false positives (e.g., use overly broad rules or flagging all trades using static cancellation_rates > 0.6 or volume > 500), wasting excessive hours per week on manual reviews and causing trading delays.
Our process starts with a deep dive into your platform's rule logic, analysing alert triggers across asset classes (FX, equities, fixed income) to identify inefficiencies, like static thresholds missing context (e.g., market volatility). This is key to help identify alert triggers and more importantly what might be causing excessive false positives.
Sometimes it can be helpful to (re) build your rules in an isolated coding environment with synthetic data to play back trade flow and identify bottle neck areas in the logic.
Data Review
Essential to drive algorithms for meaningful output is quality data, which includes:
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Is there data present?
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Is the data valid?
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Are there any contingencies?
Poor data quality—such as asset class mismatches (e.g., equity trades tagged as FX) or incomplete fields (e.g., missing timestamps or prices )—could contribute to false positives/negatives and misleading output. Our process involves reviewing data exported from source (e.g., an OMS or trade feeds) and comparing to data being fed into the surveillance platform, checking for consistency, completeness, and correct formatting (e.g., unique identifier alignment).
Benchmarking
Missed detections of attempted abuse (e.g., low-volume or price change) risk fines, as platforms often fail to flag subtle patterns due to rigid rules. Our process runs a sample period of your data through a three-step logic:
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Indicators (statistical analysis)
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Full ML surveillance algorithm
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Near-miss analysis
Against your platform’s output.
The three step logic has been tested using trades synthetically derived from previous well documented cases of market abuse and is designed to highlight actual and attempted market abuse. This is a key step in identifying solutions and potential improvements to logic.
Optimising & Next Steps
Once your surveillance logic and data (quality/ quantity) have been reviewed, and your alert output compared to our three-step analysis (indications, full algorithm, near-miss detection), we gain clear insights into your system's strengths and gaps—what's working, what isn't, data robustness, and missed alerts. This helps inform the overall state of your trade surveillance, enabling us to collaborate on prioritised next steps, such as threshold tuning, algorithm enhancements, or targeted advisory to reduce false positives and strengthen FCA/ESMA compliance.
While we have knowledge of algorithm design and analytical methods, we don't approach this with one size fits all, but aim to work around your needs.
The goal is to support your team in optimising efficiency while minimising risks affordably.
Business Impact
A pragmatic approach to navigating regulation
Help Avoid Costly Fines: Mitigate risks of FCA penalties for MAR breaches.
Save Valuable Time: Help reduce false positives, saving time for compliance and front office teams .
Enhance Detection: Identify missed market abuse risks with advanced analytics, strengthening compliance.
Cost-Effective: Avoid needing a team of management consultants with high daily burn rate.
Joining the dots
Different firms often do business in slightly different ways which increases emphasis on understanding information flows, colleague interaction and joining the dots.
Utilising a nimble approach and by avoiding rigid algorithms, we can readily adapt our logic around your business model to help you monitor for abusive behaviours.
Data points
01
Trade Data
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Trade ID, timestamp, instrument (e.g., equities, derivatives), price, volume, counterparty, trader ID.
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Covers executed trades across all asset classes (e.g., equities, fixed income).
02
Order Data
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Order ID, timestamp, instrument, bid/ask price, volume, order type (e.g., limit, market), status (e.g., placed, canceled).
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Includes pre-trade and canceled orders to detect manipulation like spoofing.
03
Price Data
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Market price feeds (e.g., intraday, closing prices) for relevant instruments.
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Ensures accurate benchmarking of trade and order data for anomaly detection.
04
Reference & Meta Data
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Instrument metadata (e.g., ISIN, asset class), deal and client reference.
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Supports context for compliance checks (e.g., insider dealing).
We are comfortable with compliance and numbers
10 Million +
We are comfortable with large data sets and spanning multiple millions of rows of trades.
MNPI
We understand that not all information flows the same way across different organisations, and that risk manifests differently too.
Evolve
We keep an eye on Market Watch and other guidance to adapt our approach accordingly.
