As AI adoption accelerates in the insurance industry, many firms risk focusing too narrowly on underwriting and claims. True transformation lies in using AI across the full customer journey, supported by strong data governance, ethical design, and integra
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As the insurance industry moves increasingly rapidly toward
a digital-first future, the conversation around AI has shifted dramatically.
It’s no longer a question of if, but of how and where we
integrate it into our operations. However, in this rush to embrace a technology
that undoubtedly offers many benefits, we need to ask whether we risk missing
the fundamentals.
In recent research calls, I’ve heard some concerns that much of AI deployment within the insurance sector is narrowly focused on areas such as underwriting and claims, which essentially represent the start and end points
of the customer journey.
While these are certainly key areas where AI can drive
efficiency and precision, what’s often overlooked is the broader,
interconnected customer journey that involves a wealth of additional interaction,
insight, and opportunity.
AI’s potential for the personalization of the customer
experience – to engage and deliver value – is where the real transformation
happens.
While elements of customer experience inevitably bleed into
underwriting and claims, much of it operates independently – across digital
servicing, engagement models, and behavioral analytics.
Trying to force-fit it into legacy categories risks diluting
the depth of insight that could be generated. That said, the structural
approach to these themes are discussed and explored matters.
The purpose of CeFPro’s research calls is to ensure we are
up to date on current thinking as we plan events, conferences, and content that
will best serve to equip those working at the heart of risk management to do
their jobs as effectively as possible.
In the context of AI, specifically, and our events in
general, we should be seeking to tackle the cross-functional enablers of AI:
data governance, ethical design, and ecosystem integration.
The reality, of course, is that these do not always fit
neatly into a business unit but are nevertheless essential to everyone’s
success.
It’s in this space that the risk management community and
other financial services professionals can discuss common challenges and
foundational capabilities, such as building robust data strategies or
determining the right AI application for a problem.
Data is, unsurprisingly, the backbone of that conversation.
The industry has long been data-driven, but the volume, variety, and velocity
of today’s data have introduced new complexities into the mix.
The fact we need data won’t change. The issue now is in
working out how we need to think about it.
Historical data is often biased, incomplete, and reflective
of past assumptions about customers that may no longer be relevant.
