The financial sector plays a huge functional role in the economy and this sector alone contributed 7.6% of the United States’ GDP in 2019. With a reach extending to both businesses and individuals, each firm in the financial sector has an overabundance of data. To turn this massive data dump into useful insights, each firm will have to operationalize an AI initiative that can address general privacy concerns and handle subjective labeling conventions and architectures. According to a recent WEF survey, 64% of firms in this sector are planning to use AI to generate new revenue and upscale their services. On the other hand, 16% of firms have already retrofitted their systems to operationalize an AI initiative. The gap here is apparent; while most companies want to integrate AI into their enterprise solutions, only a handful are actually ready to do so. 

But why does this gap exist? The WEF report aforementioned, found that the greatest barrier is an access to talent. So most firms are preparing themselves to operationalize AI, but they do not have a team that can create, maintain, and debug the systems. Similarly, a recent study by the Economist Intelligence Unit found that 86% of financial firms have planned to operationalize and incorporate AI based projects in the next five years. In that time, we can expect to see financial firms push to onboard more data scientists and data architects. With such demand, data science stands to remain the sexiest job of the 21st century.

Keep an eye out for The Data Standard’s full report on The State of Data Science 2020!