By 2025, big data is expected to generate $119 billion in global revenue, and IoT technology alone is predicted to save companies $1 trillion by 2020. Needless to say, it’s having a significant impact in the business world.
And when it comes to finance in particular, big data is set to make some significant waves.
According to Investopedia, the rapidly growing volume of data is becoming a significant challenge for financial institutions. As they explain:
“Along with vast historical data, banking and capital markets need to actively manage ticker data. Likewise, investment banks and asset management firms use voluminous data to make sound investment decisions. Insurance and retirement firms can access past policy and claims information for active risk management.”
However, thanks to AI and algorithmic trading, these huge quantities of data are still manageable. What’s more, trades made using automated algorithms are actually more efficient than those made manually, as machine learning technology is able to track the best possible rates and time for deals, as well as avoid common human errors.
So, while big data may have originally posed a potential problem in finance, it has actually led to the creation of its own solution – plus a revolutionary way of trading.
“Institutions can more effectively curtail algorithms to incorporate massive amounts of data, leveraging large volumes of historical data to backtest strategies, thus creating less risky investments,” Investopedia says. “This helps users identify useful data to keep as well as low value data to discard.”
Fintech is already using these algorithm-driven systems through what they are calling “robo-advisors”. Robo-advisors are financially beneficial to businesses, not only because they cost less than a human advisor, but because they are able to offer advice to customers with even minimal savings (which would not normally produce a decent enough ROI to be worth a company’s time). This means more business for companies offering financial services, and more opportunities for people whose incomes might previously have limited the sort of investment/financial opportunities they have access to.
While the promise of a better success rate in executing deals does sound enticing, it does not entirely negate the problems presented by big data having a broader influence in the finance sector.
As with most things data-related, one of the primary concerns is privacy. Algorithm programs are capable of cyphering information from social media, online activity, emails, and even people’s health records. Obviously, this poses some dilemmas in terms of what sort of data is ethical to use, and who would be put at risk if it was sought after.
Another big issue is with the methods of data analysis being used. Due to the extreme volume of data being collected for financial purposes, more sophisticated techniques are required to analyse them in order to garner reliable results.
“In particular,” Investopedia says, “critics overrate signal to noise as patterns of spurious correlations, representing statistically robust results purely by chance.”
Moreover, big data-based algorithms rely on economic theory and historical data to predict viable investment opportunities, and therefore tend to favour long-term payoffs. As a result, obtaining accurate solutions for short-term investments is still not consistently possible from AI-driven financial tools, and manual solutions are probably the superior option for now.
Then, of course, there’s the worry that big data will eventually put investors out of a job. However, while this is definitely something to bear in mind, it’s not necessarily the job-killer that many may assume it to be. In fact, big data in finance should be seen as a prompt for finance experts to evolve: to broaden their horizons and learn to use BI and analytics tools to manage funds and investments.
To go back to the robo-advisors: even they – despite their success – are not entirely trusted.
“Robo-advisors are a potential solution to the complexities of financial decision-making,” particularly in retirement planning, said Jill E. Fisch, law professor at the University of Pennsylvania. “But at the same time, there’s a lot we don’t know about robo-advisors — exactly how they work and how effective a solution they’re going to be.”
Ultimately, then, the finance world has the potential to evolve greatly through big data – but probably not in a way that will oust human workers from their jobs. Much like in any other industry, big data will encourage adaptation and improvement, and bolster the standard of services offered.