High-profile and costly market abuse incidents such as the LIBOR and foreign exchange (FX) trading scandals drove global expenditure on trader surveillance up to an estimated US$758 million in 2017, as financial institutions (FIs) continued to allocate increasing resources to detect and prevent rogue activity. As part of this response, FIs have deployed systems for monitoring trades, emails and voice calls, but they can often be siloed and may not tell the complete story of a trader's behaviour unless examined together. With budgets under pressure, investment banks, asset managers and other FIs are now asking whether the same, or better, surveillance controls are possible through more efficient means.
One such powerful opportunity for organizations to innovate is the incorporation of automation, machine learning and artificial intelligence (AI) into trader surveillance technologies.
Effective surveillance is a vital tool in the fight against market abuse, but it can be difficult to get right. It is a complex undertaking; the line between good behaviour and abuse is often nuanced and there's a great deal of noise for compliance and front-office teams to sift through. New technologies are disrupting the market with the ability to recognize patterns and analyze data from a growing number of sources. This is creating a perfect storm for FIs looking to overhaul their approach and disrupt financial crime in the capital markets.
Keeping pace with regulation
In a recent Chartis and EY report, more than 70% of FIs stated that they were in the process of upgrading their trader surveillance systems, laying strong foundations for the latest innovative solutions. The investment is primarily a response to regulators' demand for more detailed contextual information related to trades. However, this presents an opportunity beyond simply supporting compliance with regulations. Contextual analysis of trading activity is also emerging as good market practice. It can enable FIs to better integrate the monitoring of traditional electronic communications and trade data, as well as wider data sources such as orders and telephone voice data to provide a truly holistic approach to surveillance.
With the implementation of the Markets in Financial Instruments Directive (MiFID II) widening the reach of market abuse regulation, many FIs have been expanding their surveillance programs to cover a broader range of asset classes, such as fixed income, commodities and other over-the-counter products. However, to date, the tendency has been to invest in order to keep pace with asset class expansion and regulatory change using legacy technology. Across these new asset classes, systems originating from equities and regulated investment exchange assets are struggling under the weight of an increasingly diverse set of trading environments and idiosyncratic reporting requirements — as well as managing the sheer volume, variety and velocity of data involved.
Rolling out traditional rule-based surveillance siloes across this wider network is a high-cost endeavour with known limitations, and gaining confidence in new, more efficient techniques is complex, takes time and relies on data-readiness. When making the shift from a rules-based system to a pattern-based surveillance system, FIs must weigh-up data complexities and ownership, the difficulties of governance between different lines of defense, and defensibility with regulators.
Upgrading systems will not happen overnight
In light of these complexities, an overnight transition is unlikely to be an attractive option to risk-averse FIs and regulators. Making a gradual move that demonstrates measurable benefits in both effectiveness and efficiency is crucial at every stage. If FIs can build a transition plan from the current, well-understood logic systems of the past to intelligent risk management solutions of the future, they have an opportunity to demonstrate greater market integrity to the regulators, as well as making substantial cost reductions for the business. Key to this will be an ability to robustly measure and evidence performance improvements to business stakeholders and external parties across both false positives and false negatives of challenger techniques.
Firms focusing on the development of internal systems and policies should look beyond the compliance requirements and create more ambitious, sustainable solutions of value to their business. In a principle-based regulatory jurisdiction, such as the UK, those that remain focused on meeting the minimum compliance requirements constantly reinvent themselves to meet the latest guidance and expectations.
An opportunity to innovate
Trader surveillance systems currently generate thousands of alerts per day, drastically reducing the effectiveness of surveillance teams. Indeed, costs are rising as FIs expand their compliance teams to monitor the larger volume of alerts.
Innovation, however, does not have to mean starting from scratch. Through the use of analytics and technology, it is possible to consolidate and supplement disparate trading data and alerts from legacy systems with new sources of data — such as metadata, voice data, social networking data, physical access data, financial data, HR data, biometric data and other means of identifying behavioral patterns and material risks.
Looking to the future, the key will be to enhance efficiency by applying smart technologies so compliance teams can focus on deep data-driven investigations. This will ensure that surveillance resources reduce costs while maintaining confidence in meeting regulators' reporting requirements. Now is the time for FIs to recognize the benefits that technology can bring to their businesses, and to put the industry on an improved, sustainable footing that can both satisfy regulators and disrupt financial crime. The price of not innovating is simply too dear.
Glenn Perachio, Ernst & Young (UK) LLP Partner and EY Financial Crime Market Abuse and Trader Surveillance Solution Leader. The views reflected in this article are those of the author and do not necessarily reflect the views of the global EY organization or its member firms.