Innovating for Dollars: AI / ML / Deep Learning / Cognitive for Financial Institutions

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When Letterman was still hosting the Late Show, from time to time he’d say: “And if all THAT weren’t enough…and by golly, don’t you think it ought to be,” and would go on to mention something else that was noteworthy about the show or a guest.

Bear with me, I’ll go back to that in a moment, and it will tie into our discussion…which is about AI. AI is forecast to drive GDP gains of $15.7 trillion globally by 2030.* No organization can afford to miss out on its share of those trillions in opportunity. Adoption is low thus far, however. In Stratecast’s 2017 Big Data and Analytics Survey, only 20% of organizations said they are considering, planning, implementing, or using it. We think one part of it is market confusion; another is organizations not seeing the link between AI and revenue—both growth and retention.

Let’s first dispel some of the confusion surrounding AI. My practice analyzes big data and analytics (BDA), and here is where AI fits at a functional level: the Business Process and Strategic Analytics (BPSA) area of the BDA market. BPSA represents about one-quarter of the overall BDA market, which we assessed at more than $53 billion in 2017, and which we forecast to grow to nearly $68 billion by 2019.

Now let’s talk about other linkages between AI and money. Ever heard of Alexa and Siri? Amazon? Facebook, Google, Netflix, Tesla, and Uber? These and a multitude of others are making money right now by applying AI to their businesses. But wait, you say, what about financial institutions? Well, how about one of the largest online financial trading services in the world? It has used AI to reduce support costs by 80%. Or consider how, with hundreds of forms and more than 350 online apps, associates at a top-10 Wall Street investment firm were spending an average of 20–30 minutes looking for each form or app. The firm is now saving an estimated $32 million annually by applying AI to those and related processes. Even these examples, however, reflect a financial services market that is only beginning to scratch the surface of all the ways AI can benefit financial institutions. Customer-facing applications of AI include learning customer patterns and motivations to help guide them toward better financial decisions. In the back office, AI can, similarly, guide a financial institution’s own investment decisions. AI can automate tasks in many areas including underwriting, reconciliation, the development of risk models, and basic handling of incoming data and queries.

Sounds good, right? But delivering on the promise of AI requires a vision for applying smart analytics to the business. Nowhere is that concept more fitting than when talking about Tableau. Tableau provides the foundation underpinning the adoption of new and emerging technologies with an enterprise platform that covers all the bases in governance and security to help financial service companies guard against security breaches and ensure privacy compliance. Tableau offers rapid performance against massive datasets, an effect now accelerated by Hyper, its fast main-memory database system designed for simultaneous OLTP and OLAP processing (transactions and analysis in a single system) without compromising performance. Powerful, self-service analytics drive innovation, encouraging employees to discover opportunities for new products and services, contributing to customer and revenue growth—and enabling them to quickly run scenarios to assess the impacts of new business models such as blockchain.

Back to Letterman: “And if all THAT weren’t enough…and by golly, don’t you think it ought to be,” Tableau also has a vision for smart analytics that transcends AI-supporting BDA firepower with some pretty impressive AI building blocks. On the NL front, Tableau acquired Cleargraph and is combining Cleargraph’s NLP capabilities with Tableau’s existing Eviza natural language interface; and the company has partnerships with Automated Insights and Narrative Science to add NLG capabilities to the mix. In development are Tableau’s new Recommendations Engine, which will enable discovery, help users reuse the work of others, and leverage knowledge of their communities; Model Automation, which will offer smart defaults, saving time and providing ease of use; and Automated Discovery, to help customers discover hidden insights and answer more complex questions.

Planning on revenue growth? Failure to harness the power of AI could be a showstopper. Tableau has the content and the connections to ensure that the show will go on.

*PwC, AI to drive GDP gains of $15.7 trillion with productivity, personalization improvements, available here

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