Helping get the most from your surveillance
Scientia RegTech works with firms to help reduce false positives and inefficiencies in surveillance processes by reviewing logic, data and running propriety machine learning algorithms to detect any instances of market abuse - actual and near misses.
By joining the dots between risk areas we can also help to identify any gaps in logic and devise more efficient ways to monitor for regulatory transgressions.
Approach
Using your data to help identify risk
Through understanding your risk areas, we can adapt our approach to benchmarking to help strengthen your surveillance systems in the detection of abusive practices and near misses.
Proprietary statistical and machine learning models are used to pick up on abusive behaviours, review your data and processes against FCA Market Watch expectations. This is used to help identify gaps, and support compliance, helping protect you from fines and reputational risks.
01
Review current logic
Review platform rules to identify key alerting criteria and false positive triggers.
02
Data quality check
Fix any mismatches (e.g. incorrectly classified trades, gaps or erroneous price points).
03
Benchmarking
We benchmark your surveillance output against ours for a defined period of time, identifying differences and near misses.
04
Optimisation delivery
Deliver rule tweaks (e.g., dynamic thresholds) and optional integration.