Financial institutions are harvesting more data than ever before. Artificial intelligence is key to helping them to manage it.
Ever since people first started trading the drive has been to become faster and get key information more quickly. What’s changed is the technology and the timeframes over which traders are operating. Today, markets can change from second to second which means even a minute’s advantage can be crucial, which is why AI and big data are set to become such vitally important bedfellows.
The age of big data
In the modern high-tech world, data is everywhere and it’s becoming much more valuable. The rise of digital technology sees it come from social media, web interactions, page views and much more. Financial management systems can provide more accurate, real-time, reports on the movement of money. They can give companies a 3D view of their business allowing them to drill down into the nitty-gritty of what the data is telling them. They can see where they are generating the most revenue, the biggest profits and the most dangerous losses. They can identify and resolve threats to their business long before they become an issue.
Technology is becoming more disruptive. The rise of fintech has seen a number of small, agile and high-tech companies presenting a threat to the established status quo. Big banks, weighed down by cumbersome legacy systems and dealing with increasing levels of regulatory scrutiny can find it difficult to respond.
The challenge for companies of all kinds is to find ways to safely capture and analyse this information. For all the benefits on offer from big data banks have often appeared to be slow on the uptake. Part of the problem can be those slow ingrained legacy systems, but they also struggle with issues of security. The more data they store, and the more mobile it becomes between different departments, the greater the risk of data loss.
That loss is very real as cyber criminals are specifically targeting financial institutions. Events such as the hack of credit rating agency Equifax show just how dangerous it can be.
The answer lies in artificial intelligence – sophisticated data analytics systems which learn as they go, can respond autonomously, process thousands of pieces of information a second and become more effective as they progress. These systems can manage both unstructured and structured data completing processes which would take human operatives hours in a few minutes. What comes out at the other end is clear, concise and accessible information.
As the technology improves, it is delivering more proactive data thanks to its ability to learn and evolve as it goes. Banks are using them, for example, in areas such as predictive analytics. Taking vast quantities of market information, a machine can predict future market movements and develop strategies. For example, a bank to take a user’s search trends on the internet or social media use to predict the kind of services they are going to need and recommend the right card for them.
A bright future
This technology is in its early stages but is already showing signs of success. A report from Forrester showed that ‘predictive markets’ are 2.9 times more likely to report revenue growth. The future goals are enormous. For example, think of the money that could have been made by a trader using a predictive analytics model to issue a warning about the 2008 credit crunch.
There are of course obstacles along the path. Developing effective systems is complex and there is a lack of technical staff with the expertise needed. A predictive analytics system will also take time to prove its worth. Even so, with the amount of data surging and AI technology improving rapidly, the possibilities are almost endless.