The arrival of big data is empowering a new generation of AI technologies.
These are concepts which had existed in theory, but until now were impossible to put into practice because there simply wasn’t enough data to power the algorithms required to make complex calculations.
Now, though, there is.
The ability to access large quantities of agile data is sparking a rapid development in technology. What’s more, cloud data storage allows increased capacity and flexible access to enable the analysis of agile data in real-time.
However, these vast volumes are creating challenges.
Managing structured and unstructured data is an enormous challenge – one which is simply too much for human analysts. Machine learning can work much more quickly than human beings, processing vast amounts of complex data in a fraction of the time it would take a human.
In doing so it makes data more useful. Previously, statisticians would have had to work with samples of data. Now they can load all the data into the system and let it lead the way. It’s an enormous bonus for financial institutions as this data can paint a much more vivid picture than ever before.
Putting data to work
Every part of the decision-making process in financial services – from making a trade to analysing risk – relies on data.
AI algorithms can sift through that data and understand what it is, and isn’t, saying. For example, investment are using AI to study asset patterns and trading prices to execute strategies. In doing so, it reduces (and in some cases eliminates) the need for human intervention in the trading process. As a side effect it also minimises the influence of human discretion, which in itself creates controversy.
Until now, trading algorithms have always been impaired by their inability to think intelligently in the same way that a human operative can. They can see the data, but not necessarily make judgements about the health of a stock. Machine learning empowers those algorithms with an enhanced human element. They will able to learn as they go and, in theory, become better and more effective.
In short, big data and AI is leading to superior decision-making which improves performance and profitability. In doing so it increases the visibility of data and helps financial institutions achieve much greater levels of transparency and reporting accuracy.
The technology has the potential to impact almost every area of the financial services industry, but that’s not to say the technology is mature. However, both AI and big data are mutually sustaining. The more one develops, the more it empowers the other.