Banking
ARTIFICIAL INTELLIGENCE IN BANKING: WHERE TO START?
Published by Gbaf News
Posted on September 2, 2017
4 min readLast updated: January 21, 2026
Global Banking and Finance Review
Company
Published by Gbaf News
Posted on September 2, 2017
4 min readLast updated: January 21, 2026
Artificial Intelligence takes many forms in banking, but most firms have yet to begin implementing the technology. Learn where AI can add value to your bank and which specific technologies you should evaluate first.
Artificial Intelligence (AI) is extraordinarily popular when judged by today’s banking headlines, but those headlines have outpaced today’s practical banking reality. Relatively few banks have begun production or even full-blown research at this stage, so Celent seeks to provide an introduction and path forward for banks studying this hot topic. We take a pragmatic approach that defines AI in banking as technology that makes inferences and decisions that used to require direct human involvement. A series of fundamental and interrelated technologies around machine learning and natural language underpin all of AI. Crucially, AI is not just automation or better technology. It is not faster processing, bigger data sets, or even thousands of rules rigidly applied. These advances have yielded powerful results, but they’re performing old tasks better. That AI can respond to ambiguous real-world inputs probabilistically is one of its critical features. Building off the fundamental technologies to apply them in a banking context yields four main AI applications today: analytics; bots; robotic process automation (RPA); and report generation.
We’ve been hearing a lot about artificial intelligence, and the headlines are full of it, so we wanted to investigate what’s really happening today. Celent’s recent survey of bankers showed that there’s very little being done in the way of actual AI projects, so we wanted to give bankers a way to think about AI, its impact on the business, and what they should do next. AI is a term that’s being misused a lot. True AI has great potential, but firms need to think carefully about where to invest their scarce resources. As we drew the landscape, we concluded that there’s a surprise technology that’s often overlooked but that can provide great value with relatively little effort
Artificial Intelligence takes many forms in banking, but most firms have yet to begin implementing the technology. Learn where AI can add value to your bank and which specific technologies you should evaluate first.
Artificial Intelligence (AI) is extraordinarily popular when judged by today’s banking headlines, but those headlines have outpaced today’s practical banking reality. Relatively few banks have begun production or even full-blown research at this stage, so Celent seeks to provide an introduction and path forward for banks studying this hot topic. We take a pragmatic approach that defines AI in banking as technology that makes inferences and decisions that used to require direct human involvement. A series of fundamental and interrelated technologies around machine learning and natural language underpin all of AI. Crucially, AI is not just automation or better technology. It is not faster processing, bigger data sets, or even thousands of rules rigidly applied. These advances have yielded powerful results, but they’re performing old tasks better. That AI can respond to ambiguous real-world inputs probabilistically is one of its critical features. Building off the fundamental technologies to apply them in a banking context yields four main AI applications today: analytics; bots; robotic process automation (RPA); and report generation.
We’ve been hearing a lot about artificial intelligence, and the headlines are full of it, so we wanted to investigate what’s really happening today. Celent’s recent survey of bankers showed that there’s very little being done in the way of actual AI projects, so we wanted to give bankers a way to think about AI, its impact on the business, and what they should do next. AI is a term that’s being misused a lot. True AI has great potential, but firms need to think carefully about where to invest their scarce resources. As we drew the landscape, we concluded that there’s a surprise technology that’s often overlooked but that can provide great value with relatively little effort
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