FIS Modern Banking Platform
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Patrick Donahue | Data Solutions Group, FIS
December 07, 2020
AI and machine learning are expected to add trillions of dollars in business value in the years to come through increased revenue, improved product development, and operational efficiency. Investment in AI startups and technology is increasing rapidly. And yet, questions about how to use artificial intelligence, let alone what aspects should be implemented first, can leave financial services firms paralyzed while at the same time feeling the anxiety that they not be left behind.
The topic of defining AI alone could fill volumes, but it really depends on domain and context. For financial institutions, a working definition could flow from what AI does uniquely well and what can best positively impact a bank’s competitiveness in the marketplace, which is the power to make meaningful predictions. Predictions are inputs to decision-making.
This kind of AI is enabled by machine learning, which is a unique methodology and technical infrastructure that allows algorithms to work through vast amounts of data and find connections that the human brain would never be able to process. ML can “think” in many dimensions in parallel, processing millions of variables at once, where the human brain can only handle a few at a time at most.
This kind of processing allows AI to find new market segment opportunities hiding in your data sets, and allow you to sell the right product, to the right customer, at the right time, at the right frequency, and in the right manner. It also allows us to flip the servicing model on its head. Imagine if it was possible to predict that an ATM would require service in the next two days.
So, how do we hop on this AI train? What are best practices that help me succeed quickly and more often than my competitors?
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