Al is the buzzword on everyone’s lips right now and with good reason—according to a recent survey by Finextra/OpenText of financial industry professionals, more than half of the responders felt that Al was mainstream now or would be within two years. Al is already in use in many areas of finance from bank apps to fraud detection, but these are a fraction of the possible applications we can foresee and there are many we haven’t imagined yet. If we’re going to make the leap to using Al effectively in the financial business sector executive sponsorship is going to play an important role; in the same survey, 39% of responders put it in the first or second position. As leaders, it’s time to do more than talk about Al; it’s time to understand Al and some of its applications.
While we still don’t know the full potential of Al, we seem to be well aware of its pitfalls. Al triggers our fears that as people, we are going to be replaced by machines that will not only tell us what is best but also do it for us without asking. Let’s take a step back and remember that there’s a big difference between automating
tasks—even enormous ones that people could not accomplish in a lifetime—
and applying intelligence to complex situations. Simply using computers to
process huge amounts of data are not artificial intelligence. When a computer is programmed to learn from a process and adapt, then we can consider it to be venturing into the realm of intelligence. Computers can’t do that by themselves, though, they have to be programmed by people to do it. For results to be applied proactively or profitably, people need to oversee the process, the data, and the decision-making
Al functions most potently as a tool to enhance people’s performances capabilities.
It’s not likely that anyone reading this can imagine life without cars. We take cars for granted as tools that move us from one place to another, at higher
speeds than we are capable of by foot or by bicycle. It might be hard to believe now, but when cars first appeared, there were numerous concerns about adopting these new vehicles. The accuracy of drivers was questioned;
horse-drawn carriages relied not only on a driver but also on the horses’ innate abilities to avert danger. Speed was a concern; so much so, that car were limited to lower speeds per hour than even bicycles. All new technologies trigger both excitement and fear until
we have adapted to the benefits and mitigated the downsides. Instead of feeling threatened by the power of Al, we can embrace the possibilities of a new method of managing the copious data we accumulate and acting on that data in ways that augment our capabilities.
How can Al be used in quantifying market sentiment?
Understanding market se ” ent can be a key element of invest – -.1e ability to tad it in real time and see the long view can give you an edge in a competitive
market. The sentiment is often viewed as emotional, or as a contrarian indicator, so it might seem counterintuitive to apply machine learning to get a clearer picture of it. This is precisely why Al is useful—it removes the emotional and reactionary filters humans can use when trying to read market sentiment, while still engaging the largest possible representative sample of available data. Unlike people, Al is not humiliated by
making mistakes, but adapts instantly to feedback on outcomes: it learns, applies and delivers improvements
Information overload is a major challenge for financial professionals, one that might be insurmountable without the benefits of artificial intelligence and machine or deep learning. Instead of looking at the anecdotal evidence of just a few advisors or trend-predictors or the statistics in several articles from a few sources, we can use Al to find and process all the available data—in the financial news sector, this can be up to 200,000 articles per day—in a
constantly updating cycle, giving us an instant and thorough picture of where market sentiment lies. In a market driven by information flow, having the ability to process and analyze that much information to get a strong picture of sentiment and act on what you see is invaluable.
Augmented Language Intelligence (ALI) is the term we have developed to describe the process of integrating
natural-language processing (NLP) and machine learning to cull and sort the available data and augment our abilities to analyse key factors in sentiment including global and local events,
shifts in government policy, leadership changes, accidents and other reported variables. Using ALI to process financial
news yields a here-to-fore impossible overview of the market and allows drilling down into specific news events, regions, time periods, and companies.
By design, ALI strives to increase people’s capacity for command over information. ALI does not replace our involvement in decision-making but allows informed decision-making that accesses the broadest possible range of data for financial professionals
to interpret and use in their market engagement. It may even be possible that ALI will uncover new applications of sentiment or new ways of using trends that we have not had access to previously, due to data overload or our inability to comprehensively analyze the information available.
Test-driving Al in the financial sector makes good sense. We don’t have to go straight from horse-drawn carriages to driverless cars; we can ease this
technology in as we get up to speed with its benefits and learn how to protect against any downsides. Imagine what our use of Al will look like in a hundred years—with strong engagement, innovation, and decision-making, that’s what we can start building now.