Artificial Intelligence (AI) is the ability of machines or computer systems to perform tasks at such a speed, that it usually requires a level of human intelligence that only a few select people have the capability to perform. This includes activities such as learning, decision-making or problem-solving and it is also commonly referred to as Machine Intelligence or Machine Learning.
With this said, and taking into account the recent breakthroughs from systems like ChatGPT and others, how could AI impact the post-trade industry? Post-trade processing could be affected by these breakthroughs due to the recurrent alterations seen across the regulatory landscape and asset classes. These alterations, coupled with human-driven conclusions across processes, invent inefficiencies and can lead to multiple errors.
AI can play a crucial role here in decreasing these inefficiencies and easing decision-making through the use of automation. AI can substantially diminish the necessity for manual interventions, reduce reconciliation requirements, support straight-through processing, and improve operations significantly, here’s how it could influence and improve the post-trade industry:
Increase Efficiency: AI could streamline back-office operations by automating mundane tasks that employees don’t want to do, such as data management and reconciliations. This would reduce operational costs and tremendously increase efficiency within post-trade processing.
Improve Accuracy: If it were to use complex algorithms beyond our current capabilities to analyse data, AI could improve the accuracy of post-trade operations, reducing the risk of error and facilitating quicker settlements.
Enhance Compliance: Regulations such as MiFID II require stricter compliance measures than organisations are currently familiar with and which AI could help facilitate. For example, using AI to generate accurate and timely trade reports that comply with regulatory requirements reduces the risk of non-compliance and potential fines.
Risk Management: AI could help identify potential risks and exposures in trades and portfolios, allowing employees to proactively manage risk and reduce the impact of market volatility.
Decision Making: AI algorithms could use historical data to predict future market trends and movements, helping users identify potential opportunities and make more informed investment decisions.
Overall, the implementation of AI in post-trade operations could lead to increased accuracy, efficiency, and compliance, creating new opportunities for the industry. AI models are progressively being utilised by brokers, traders, and financial institutions to enhance trade execution and post-trade processes, resulting in decreased settlement failures.
Understanding the different approaches between the buy-side and sell-side can help heads of operations, on either side, make informed decisions when it comes to managing risk.
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