Big data was once the sole province of large corporations that had the resources necessary to slog through mountains of data to even attempt to turn it into actionable intelligence. As artificial intelligence has gained significant ground, however, automated programs are now more capable of churning through massive amounts of data in order to provide more intelligent solutions in a range of sectors.

Industries that can benefit from this the most are those that have to take into account a dizzying array of variables that might be constantly in flux. These can include sectors like health care, real estate, and finance. While artificial intelligence and machine learning have much to offer the world of finance, the shift towards data-driven solutions has been slow. Still, as technology continues to advance at a rapid rate, the integration of data-driven financial services seems more likely.

Here are three ways financial services can benefit from data-driven services and the challenges of each.

 

Variable Analysis

One of the challenges of assessing any market that is in flux is accounting for dozens, if not thousands, of variables that can affect the market. Machine learning and AI can absorb thousands of different data sets to extrapolate the most likely outcomes given a wide range of potential variables. Having access to this data is one thing, but possessing the time and ability to process and analyze the data is another matter. Through automated programs, those in the financial industry will be able to identify how they can integrate conclusions from mass data analysis into current systems.

 

Quantifiable Analysis

Because machine learning and AI are capable of extrapolating information from an almost limitless number of sources, that actually makes the outcomes more difficult to trust. When experienced financiers understand exactly what information is being evaluated to make specific recommendations, they are likely to trust those recommendations more. Assigning specific rules and conditions to automated programs can help ensure accurate analyses are taking place. Likewise, granting access to initial data can help financial advisers validate and accept the automated conclusions. Without requiring human analysts to conduct meticulous, tedious work in order to understand a massive amount of data, financial businesses can integrate analysts’ services elsewhere so they can be more beneficial and productive for the company’s growth.

 

Language Barriers

Language does far more than just hinder or enable us to communicate with one another. Language also shapes our view of the world. Ironically, even the world of finance is both hindered and influenced by linguistic differences. Most algorithms are programmed by individuals who speak the language of coding far more fluently than the language of finance. This can create difficulties when “old school financiers” try and merge with millennials who have been raised in a technological world. Advancements in technology will soon break down cultural barriers which can also wreak havoc in the world of international finance. By equalizing the field and limiting unnecessary confusion or miscommunication, automated systems can help individuals of all ages and backgrounds understand data analyses.

 

As we shift toward a data-driven industry, it is important to recognize that the technology being integrated is far from perfect. Implementing automated programs to organize and analyze data will not be without issues. However, becoming more data-driven and automated will allow for less wasted time and better applications of existing human talent.