Are Risk and Surveillance finally coming together?

By Alex Lamb, Head of Americas

Scila takes a look at the integration of real-time market risk management, trade surveillance, and anti-money laundering (AML) monitoring in the context of global markets monitoring.

Trader behaviour and risk exposure are linked – how bringing these together can speed up detection of different risks, enabling timely control and accurate reporting. Essential for this are: Real-time and high-quality alerts, intelligent evaluations, supported by well organised and accurate data. 

Introduction

In the dynamic world of global markets, the need for robust risk management, trade surveillance, and anti-money laundering (AML) monitoring has never been more critical. Traditionally, these functions operated in isolation, with different specialists and systems. Market risk management focuses on assessing and mitigating risks associated with market fluctuations, while trade surveillance primarily looks back (T+1) at trading behaviours after the fact. AML monitoring aims to detect suspicious activities related to money laundering and illicit financial flows. However, with the evolving landscape of all markets, these functions have started to converge. This convergence is driven by the common thread of data, the increasing need for real-time insights, and the imperative to align applications and procedures across an organisation.

Common Data as a Foundation

The foundation of effective integration between market risk management, trade surveillance, and AML monitoring lies in data. In this data-driven era, financial institutions have access to a vast amount of information, ranging from market prices and trading volumes to customer transactions and account activities. This data is the lifeblood of all three functions, but not always available in the same format.

For market risk management, timely and accurate market as well as private (the firm’s) data is essential. It enables real-time monitoring of portfolio exposures, stress testing, and the calculation of metrics like Value at Risk (VaR) and Initial Margin. To achieve near-real-time alerts, this data must be continuously evaluated, and potential outliers must be identified promptly.

Trade surveillance, on the other hand, relies on historical trading data. It looks back at the behaviours of market participants to detect irregularities, market manipulation, and abusive trading practices. While this data may not typically require real-time processing, it must be comprehensive, well-organised, and accurate to generate meaningful insights.

AML monitoring draws from cash and customer transactions as well as market data. It involves tracking cash flows, order flows, and assessing patterns that could indicate money laundering or risky transactions. Here, the emphasis is on detecting unusual and potentially illicit activities, which require high-quality data and advanced analytics.

Real-Time Alerts and Continuous Evaluation

One of the key differentiators between market risk management and trade surveillance today is the timing of their alerts. Market risk management demands near-real-time alerts to respond swiftly to market developments.

Stress testing, for instance, involves subjecting portfolios to hypothetical extreme scenarios to assess potential losses. In a rapidly changing market, such stress tests must be conducted frequently, and the results must be evaluated in real time. Any outliers or indications of significant risk must trigger immediate alerts.

Similarly, Value at Risk (VaR) calculations, which estimate potential losses based on historical data, require constant monitoring and alerting. Any breach of pre-defined VaR limits should be flagged without delay.

Initial margin estimations, particularly in the context of derivatives trading, need to be updated in real time to ensure that sufficient collateral is available to cover potential losses. Alerts should be generated if there is a risk of margin shortfalls.

In contrast, trade surveillance until recently has operated somewhat retrospectively, looking back at trading activities after they have occurred. The focus here has been on behaviour analysis rather than real-time risk assessment. Trade surveillance systems analyse historical trade data in conjunction with market order books at the time of execution to identify patterns of manipulation or abusive practices. Alerts are typically generated on a T+1 basis or longer, allowing for a comprehensive analysis of trading behaviours. The move to identifying real-time events is already underway, with systems that are capable of monitoring ‘intra-day’ abuse being demanded by firms that are conscious of the connection between market risk and risky behaviours. 

Anti-Money Laundering (AML) Monitoring

The convergence of market risk management and trade surveillance is further complicated by the addition of AML monitoring. AML monitoring is concerned with detecting and preventing money laundering activities within the financial system. It involves monitoring cash flows, transaction patterns, and customer behaviours.

To integrate AML monitoring effectively, the same high-quality data used for market risk management and trade surveillance must be leveraged. AML monitoring relies on detecting suspicious patterns in customer transactions. This includes large, rapid movements of funds, complex transaction chains, and unusual customer behaviours. To achieve this, AML systems require access to comprehensive and up-to-date transaction data.

Additionally, AML monitoring must work in conjunction with both market risk management and trade surveillance. It should be able to correlate AML alerts with trading activities. For example, if a customer’s transactions raise suspicions of money laundering, the system should cross-reference this with their trading activities to identify any potentially related market manipulation. The ability to observe all three in real-time is a true advantage.

High Quality Alerts supported by High Quality Data

The effectiveness of alerts generated by market risk management, trade surveillance, and AML monitoring hinges on the quality of the underlying data. Garbage in, garbage out is a fundamental principle in data analytics. To ensure that alerts are meaningful and actionable, financial institutions must invest in data quality, or select a vendor with a reputation for its ability to ensure this high level of data quality.

This begins with data validation. Market data must be validated for accuracy and completeness. Inaccurate market prices or missing trading data can lead to erroneous risk assessments. Trade data must be cleansed of errors and anomalies to enable accurate behaviour analysis.

For AML monitoring, transaction data is of paramount importance. Inconsistent or incomplete transaction data can hinder the detection of money laundering patterns. Data validation and cleansing procedures must be rigorous to maintain data quality.

Consistency in and across User Applications

An often-overlooked aspect of integrating these functions is the consistency of user applications. Market risk managers, trade surveillance analysts, and AML investigators all rely on software tools to carry out their tasks. To streamline operations and ensure a cohesive approach, these applications should be similar and consistent with each other.

This consistency extends to user interfaces, workflows, and reporting capabilities. Users should not need to switch between disparate systems to perform their duties. Instead, they should have access to a unified platform that integrates market data, trade data, and transaction data seamlessly. Unifying these platforms and functions makes more sense if they are all real-time or near real-time.

Alignment with Organisational Procedures

Integration isn’t just about technology; it also involves aligning these functions with an organisation’s procedures manual. Clear assignment of actions and responsibilities within the integrated system is crucial. This ensures that when an alert is triggered, there is a well-defined process for how it is handled.

For example, if a market risk alert indicates a breach of risk limits, the system should automatically assign responsibilities for risk mitigation. It might trigger the allocation of additional collateral or the adjustment of trading positions. Likewise, if a trade surveillance alert suggests market manipulation, the system should initiate an investigation process that involves relevant teams and individuals, not only in the sphere of compliance with trading regulations, but also in risk management. Is the bad behaviour linked to trading performance? Are there other accounts that haven’t been alerted from the trade surveillance aspect, but have risk changes that mirror or are close to mirror financial impact and timing? Could these be transactions that point to money laundering?

In the context of AML monitoring, as indicated above, alerts related to suspicious transactions should trigger a predefined set of actions. This could involve freezing accounts, reporting to regulatory authorities, and conducting internal investigations.

Regulatory Considerations

The integration of market risk management, trade surveillance, and AML monitoring must also consider regulatory requirements. Financial markets are heavily regulated, and compliance is non-negotiable. Each of these functions has its set of regulatory guidelines and reporting obligations. Failing to meet these can be very expensive.

Ensuring that the integrated system complies with these regulations is imperative. It involves aligning alerting mechanisms with reporting requirements and maintaining a robust audit trail. Additionally, it requires staying abreast of evolving regulations and adapting the integrated system accordingly.

Conclusion

In the rapidly evolving landscape of international markets, the convergence of market risk management, trade surveillance, and AML monitoring is a logical evolution. It is driven by the common reliance on data, the need for real-time insights in ALL areas, and the imperative to align applications and procedures. The integration of these functions enhances the ability of all institutions to manage risk, detect market abuses, and combat financial crimes rapidly and effectively.

However, successful integration of these real-time systems is not a simple task. It requires a foundation of high-quality data, the implementation of real-time alerts and continuous evaluation, the incorporation of AML monitoring, and consistency of user applications. Moreover, it necessitates alignment with organisational procedures and a keen eye on regulatory compliance.

In this dynamic environment, where markets transcend borders and technology evolves rapidly, the ability to monitor and immediately manage risk while combating financial crimes is paramount. The integrated approach discussed here is a strategic move towards achieving these goals, ultimately ensuring the stability and integrity of the global markets system.