Powering the trade lifecycle with Generative AI
Gen AI poised to revolutionise trading landscape
Generative artificial intelligence (AI) describes the process whereby a machine scours the internet to create unique text - or other outputs - in answer to a query or instruction. This emerging technology is driving debate in every industry, but its potential to enhance trading and investment banking has, recently, prompted a great deal of concern and elation. Aside from bringing several generic advantages to a financial firm, such as reduced costs and improved business efficiencies, generative AI can, specifically, help to optimise the entire trade lifecycle. GreySpark observes seven key ways in which AI can do this:
Analysis of Text, Including News and Social Media Sentiment: using advanced transformer models such as GPT-3, AI-powered sentiment analysis has become more accurate and efficient, enabling traders to make better-informed decisions based on real-time data.
FX Trading Signal Predictions: : including AI techniques in the process improves human decision making and risk management and enables traders to optimise their trading strategies. The strong dependency on trustworthy and reliable data has created opportunities for market data providers. For instance, Refinitiv has recently collaborated with Bank of China to enhance Eikon’s FX trading signal prediction application.
Order Flow and Market Impact Predictions: AI-driven execution algorithms can help prevent slippage and minimise the costs of execution. Using Generative AI, a trading team can make more trades in a shorter amount of time and increase their profits.
Identification of Counterparty Risk: Generative AI platforms monitor counterparties in near-real-time to quickly identify and respond to potential risks. For example, Generative AI algorithms can monitor news articles and press releases to determine a counterparty’s creditworthiness, which will be relevant for counterparty risk management. Ultimately, Generative AI predictive analysis capabilities could be transformative for assessing risk levels associated with different counterparties based on historical data and real-time market conditions.
Completing Trade Data for Settlement: By analysing historic patterns, market trends, and transaction data, AI tools can detect potential irregularities that may result in settlement failures. Generative AI leverages sophisticated machine learning methods to identify anomalies in trade records and evaluate the associated risks within a context-search framework.
Reduction in Rekeying: Generative AI streamlines trade confirmation documentation by automatically populating templates with trade details, ensuring accuracy and efficiency. This technology optimises the documentation process, making it ideal for legal and compliance purposes.
Post-trade Automation: Generative AI leverages machine learning and advanced algorithms to mitigate settlement failures. It automates and optimises processes, reducing manual errors, detecting anomalies, ensuring precise trade matching and improving operational efficiency. Its predictive analytical capabilities can provide insights into potential failures, enabling proactive measures to mitigate the risks.
More to follow.