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When we talk about GenAI in banking,
there's a fine line on how to deploy it.

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Because, on the one hand side,
it's a new paradigm.

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On the other, the interactions that banks have
with their customers

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typically need 100 percent accuracy.

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And there, the new technology presents
a bit of a gap.

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The current opportunity for GenAI

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in banking is less about
consumer facing tools.

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It's more about how to synthesize

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a bank's existing wealth of information,
to make better informed decisions,

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provide very personalized client interaction,

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and to significantly automate
many core banking processes.

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The financial sector knows a lot about its customers,

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but it's also subject to strict regulation

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and always under scrutiny for standards
of accuracy, security and privacy.

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GenAI models are fairly easy to build,
but in finance,

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the challenge is to apply them
with the most impact and the least risk.

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That growing list of use cases
includes chatbots and knowledge interface

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to support call center
and in-branch agents,

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reducing call handling
and training requirements.

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AI generated personalized
and targeted content

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at scale to drive more efficient
marketing campaigns.

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Determining creditworthiness,

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GenAI can synthesize all kinds of data – structured, unstructured – from a range of sources

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preparing loan decisions with greater accuracy,

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surfacing information for relationship
managers and financial advisors,

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maximizing personalized interactions,

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custom software development as a co-pilot for coders,

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and onboarding new customers
and automating paperwork.

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Especially when it comes to personal finance,

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you are dealing with very
sensitive information.

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Banks will have to assess
the quality of the GenAI,

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the sources and reasons,
and decide how they want to use it.

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GenAI will bring a new source of insight
that can automate and document

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the core processes of a bank.

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That's where it will make a real difference.