One Simple Chart: primary applications of AI in Financial Services

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In a recent post, I highlighted technologies are most important to large banks using results from a BankDirector survey. That survey hinted that Data and AI are high priorities within large banks, and Automation and Data Analytics emerged as important areas to the respondents of that survey.

Another recent survey from Goodwin pooled “responses from more than 700 senior decision makers across the accounting, banking, financial services, investment and insurance industries in the United States, United Kingdom and continental Europe”. This survey included a question on how financial services companies are using AI technologies:

Source: Goodwin’s Fintech 2020, A Global Survey on the State of Financial Technology

About a quarter of all respondents indicated that they use Predictive Analytics or Data Mining / Analytics, with respondents from the US and UK leading the way. Banks have long used these techniques, SAS has been a particularly popular software platform within banks, and I’m actually surprised these percentages aren’t higher! Meanwhile, about one in seven (15%) of respondents signaled that they are using AI for Asset Management / Robo Advisory, a new class of tools that manage assets with minimal human intervention.

One application that isn’t on the list is fraud detection. I know from speaking with enterprise software startups in data and machine learning that financial services are keen users of AI for fraud detection. This is particularly true for tools that are able to handle graph data and graph algorithms.

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