By Richard Timperlake, SVP, EMEA
In finance, small delays can have a big impact. Delays of mere seconds can have huge financial ramifications – which makes access to real-time data incredibly important.
The rise of generative AI (GenAI) has brought this into focus, catalysing a real push towards harnessing the power of real-time data. Being able to feed an intelligent, reactive AI with data that paints a to-the-second picture of your organisation promises transformative potential, from boosting prediction accuracy to enabling new risk management initiatives.
Buying time
The first real-time financial data came from the humble ticker tape machine, which transmitted stock prices over telegraph lines. This revolutionary approach enabled traders to receive updates within mere minutes and take action accordingly.
This is positively relaxed by today’s standards, with data now arriving in milliseconds. Both human and digital traders can react instantaneously to market changes, taking advantage of brief windows of opportunity and swiftly adjusting strategies to reduce risk. Even the aggregation of news, from both outlets and social media, empowers rapid-fire decision-making – often faster than you can shout “Sold!”
New advancements in GenAI analytics can take advantage of these speeds, delivering more accurate predictions and insights to traders – while also refining algorithms to be more adaptive in real-time scenarios. Financial firms are ramping up investment into R&D, talent, and infrastructure to remain competitive as a result and Deloitte suggests that enterprise spend on GenAI will increase by 30% over 2024.
Similarly, there’s a noticeable shift in AI’s use on the trading floor — from handling lower-risk tasks like compliance and marketing to influencing higher-risk areas, such as investment decisions and opportunity identification.
According to The Financial Times, JP Morgan is expanding its use of a GenAI tool called “Moneyball,” designed to highlight questionable decisions made by portfolio managers, thus aiding in correcting biases and enhancing decision-making processes. Similarly, Voya Investment Management reports reliable outcomes from their virtual analyst, which tracks stocks for potential risks.
However, as GenAI tools evolve, it’s becoming increasingly critical for leaders to make smart investments in data integration and management to maximise the benefits.
You get out what you put in
For starters, despite a suite of impressive capabilities, GenAI does still rely upon access to high-quality data. As the old adage in the data world says, “garbage in, garbage out”; analysis can only be as good as the data that it’s based on. Feeding AI inaccurate, incomplete, or outdated data leads to flawed outputs, which can negatively affect trading strategies and decisions.
If we take a moment to imagine a risk management system that isn’t able to process real-time risk insights, instead sitting on serious concerns for hours or even days at a time before responding. The consequences here are as predictable as they are dire.
Fortunately, Data Streaming Platforms (DSPs) play a pivotal role in eliminating risk here. DSPs are critical to enabling the best possible performance from GenAI models as they enable the continuous capture, organisation, and examination of data in real time.
Built to handle large volumes of real-time data, DSPs ensure that AI models are continuously trained on up-to-date information. This dynamic adaptability helps applications uncover emerging trends or detect anomalies in real time, all while seamlessly integrating with existing systems. This enhances the overall dataset and drives more informed trading decisions.
Futuregazing – GenAI on the trading floor
Real-time data streaming gives businesses the ability to accommodate ever-changing conditions more quickly than ever before. Over half (51%) of IT leaders across various industries said that real-time data streaming allows their organisations to remain agile.
As GenAI’s analytical capabilities become ever-more normalised within financial organisations, it’s evident that real-time data streams are the lifeblood of these systems. GenAI models thrive upon continuously updated, real-time data, as one colleague at Confluent aptly stated, “Data streaming is the central nervous system for data, while AI is the brain.”
Optimists envision a future in which real-time, AI-powered trading methods help to maintain more streamlined, stable financial markets. The transparency provided by real-time data analysis could improve trust in financial institutions by giving participants greater insight into market dynamics and decision-making processes. Such a shift could democratise access to investment tools, enhancing financial literacy and enabling more individuals to grow their wealth.
It’s important to remember, of course, that the adoption of real-time streaming and GenAI in trading does represent new ground for many regulatory bodies – not to say an ethical conundrum for legislators. Answers to these concerns will emerge as the industry evolves, which makes collaboration between regulators, financial institutions, and technologists incredibly important if we’re to establish proper ethical standards and legal frameworks.
While there will undoubtedly be hurdles along the way, the power of data streaming equips organisations to make the most informed decisions — right when it matters most.
