When we talk about GenAI in banking, there's a fine line on how to deploy it.
Because, on the one hand side, it's a new paradigm.
On the other, the interactions that banks have with their customers
typically need 100 percent accuracy.
And there, the new technology presents a bit of a gap.
The current opportunity for GenAI
in banking is less about consumer facing tools.
It's more about how to synthesize
a bank's existing wealth of information, to make better informed decisions,
provide very personalized client interaction,
and to significantly automate many core banking processes.
The financial sector knows a lot about its customers,
but it's also subject to strict regulation
and always under scrutiny for standards of accuracy, security and privacy.
GenAI models are fairly easy to build, but in finance,
the challenge is to apply them with the most impact and the least risk.
That growing list of use cases includes chatbots and knowledge interface
to support call center and in-branch agents,
reducing call handling and training requirements.
AI generated personalized and targeted content
at scale to drive more efficient marketing campaigns.
Determining creditworthiness,
GenAI can synthesize all kinds of data – structured, unstructured – from a range of sources
preparing loan decisions with greater accuracy,
surfacing information for relationship managers and financial advisors,
maximizing personalized interactions,
custom software development as a co-pilot for coders,
and onboarding new customers and automating paperwork.
Especially when it comes to personal finance,
you are dealing with very sensitive information.
Banks will have to assess the quality of the GenAI,
the sources and reasons, and decide how they want to use it.
GenAI will bring a new source of insight that can automate and document
the core processes of a bank.
That's where it will make a real difference.