“Big Data” and “AI” (artificial intelligence) are buzzwords that have been woven into corporate and finance strategy session discussions around the globe for a number of years. They’re more than cool-sounding words; unlike other buzzwords, they have substance, especially when used in partnership with one another.
It is no surprise that data-driven decision making is more reliable than simply going by gut instinct. Most organizations already use in-house or third-party reporting and AI analytics to capture the risks and opportunities found within their own business’s data. The next step is to be able to identify the outside influences that can affect your business to either mitigate risk or uncover new opportunities.
Particularly in finance, where news and rumors can have an immediate impact on the markets, the daily news cycle cannot be ignored. By studying the daily stream of news, you can discover the natural flow in volume and in coverage as events unfold. With such a large collection of daily news sources, it is easy to see anomalies in the patterns. Could this be a unique breaking news event? An unexpected opportunity? An unplanned setback? Isolating these news events is enormously useful and potentially very valuable for companies prepared to take action.
One example of AI and Big Data working together in news can be found in our NewsEdge Trading Impact codes. As you may know, NewsEdge aggregates news from over 17,500 licenses and web-based sources. We employ over 200 different Trading Impact codes that map to market move events such as bankruptcy announcements, mergers and acquisitions, and natural disasters. All of these events act as triggers that could impact a company, an industry or the markets as a whole. Companies with strategies in place to act on these codes can benefit from their preparation.
We have several patented algorithms that we use to add value to the news services we provide our customers. In fact, we were awarded our latest patent on unsupervised learning on Feb 26. However, many financial companies are developing algorithms of their own. These are often proprietary and instrumental to their trading and investment strategies, but this is not limited to finance. That is where an archive for modeling and testing accuracy comes into play. Working with a large volume of actual news events speeds up the modeling and validation process of your AI systems.
News is one of many examples of Big Data that is still relevant and gaining importance for those companies investing in AI to push their business forward. Progress and innovation sometimes feel like a race. Those in the lead are companies quick to adapt, diligent in their preparations and ultimately the most successful. Where are you in the race?