How AI Is Transforming Banks & Banking

Scaling AI Lynn Heidmann

Data has always been the foundation of the banking industry. What has changed in recent years, of course, is the amount of data available and the speed at which it is processed as well as the need to quickly respond to market changes.

brown financial numbers on a wall

New technology gives banks the power to collect, store, and analyze exponentially more information than was imaginable not too long ago. In the wake of fintech, banks already know that to succeed in today’s ecosystem, they must use this wealth data at a massive scale to continuously innovate.

Global business information provider IHS Markit predicts that the business value of artificial intelligence (AI) in banking will reach $300 billion by 2030."

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Yet most struggle amidst complexities of the data itself, regulations, and more, to get AI initiatives off the ground. But they don’t have to — here at Dataiku, we work with banks every day to increase their AI maturity. So we know that today’s banks seeing success with AI initiatives:

  1. Bring the idea of “doing AI” down from a pedestal and instead break it down into what it really means for them (which isn’t always a sexy app or sleek chatbot).
  2. Realize that Enterprise AI is a journey — a series of steps and gradual competencies to work up to over the next several years. One of those steps is the gradual improvement of messy internal processes so that entire teams or divisions take steps toward using data and AI to work smartly, efficiently, and within regulatory standards.
  3. Get started now, because waiting a few more years to dive in will mean pushing the timeline of the Enterprise AI journey even further, while competition from other more agile companies (whether fintech, GAFA — Google, Apple, Facebook, Amazon — or traditional players) moves in.
  4. Build on foundations. Many banks today are intimidated by the idea of AI even though they’ve already been doing it — or at least some of it — for years (and in some cases, decades). Quants, algorithmic traders, risk analysts, fraud analysts, pricing teams, the list goes on — these people and teams already form the building blocks of an Enterprise AI strategy. Successful banks build upon this already-existing framework.

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