Trading in the financial sector is extremely complex. New asset classes could disrupt traditional trading processes, the most recent example being cryptocurrencies. New trade techniques and asset classes increase the burden on financial institutions, which then need to mitigate trading risks such as scalping, manipulation, fraud and corruption.
This requires more than manually reviewing trade data and identifying anomalies, and this is where the use of AI for trade surveillance comes in.
The limitations of rule-based systems
The introduction of new platforms for trading such as mobile-first and mobile-only platforms has increased the number of traders, and the introduction of new asset classes such as cryptocurrencies has made trading more complex.
There is more freedom allowed when trading on smartphones and remote devices, but the increasing number of remote traders causes difficulty for trading platform providers and financial institutions. The rule-based systems they previously used to identify suspicious activity or anomalies cannot handle the increasing complexity of trading data.
A recent survey shows that the ratio of actual suspicious trades to identified suspicious trades can be as low as 0.01%. With traditional rule-based systems not effective anymore, financial institutions are moving to AI-led solutions. AI/ML algorithms can identify anomalies in trading data.
Even if they may not know the rules of trade surveillance, they would be able identify when something is wrong. They are also able to learn and improve with time to easily differentiate between suspicious and legal trades.
Ensuring regulatory compliance with AI-led systems
Compliance regulations for financial institutions keep changing. Local and state governments often develop new rules for individual traders and financial institutions. Financial institutions using traditional tools may, therefore, need to work hard to adopt a new compliance norm, feeding every compliance requirement manually into their trade-monitoring systems.
This requires a significant amount of time, while AI-led monitoring systems could automatically adapt to changing regulations.
Effective compliance management ensures a financial institution does not have to pay penalties or tarnish its reputation due to non-compliance. AI-led monitoring systems would facilitate this. Regulators monitor trades and financial institutions and could also rely on AI-led trade surveillance systems to generate better results.
Monitoring all data types
Financial institutions do not always receive structured transactional data, and they also need to keep an eye on communication data, as individual traders could collaborate to engage in market abuse. Regulators and financial institutions must also monitor market data, historical data, entity data and log data to identify threats.
Traditional monitoring tools would not be able to keep track of all these different types of data and could adopt AI-led surveillance systems to convert unstructured data and analyse different data types.
Proactive trade monitoring
A financial institution needs to prevent fraud and trade abuse. An AI-led monitoring system would generate alerts in real time on a hint of trade abuse or suspicious activity.
