Despite astronomical sums being spent by banks on surveillance – almost US$ 740m by 15 surveyed Tier I and Tier II banks alone in the first two years after MAR came into effect in the UK – electronic surveillance is still in its infancy, and gaps in efficacy and performance mean that there is appetite for further spending, development and automation.
Many banks currently operate with sub-optimal communications surveillance technology – the large number of false positives flagged by surveillance systems suggests inefficiency and the lack of automation and need for human intervention in analysing the large communications data sets places an overly heavy burden on surveillance teams.
The potential for large fines and penalties arising from regulatory breaches is driving industry professionals to look to new technologies – machine learning and other artificial intelligence – to help them address these pressing issues.
Taking Stock – Time for a New Approach
The need for effective surveillance in financial services is driven not only by the enhanced global regulation of the past decade – Dodd Frank, MiFID II and MAR amongst others – but also more fundamentally by the desire to avoid the market abuse that these regulations address as well as the resulting financial and reputational damage. Whilst it cannot be guaranteed that the big banking scandals of past years could have been avoided with more effective surveillance procedures in place, it is clear that many of the high profile unauthorised trading and market abuse cases would have been considerably harder to perpetuate had there been effective tools in place.