AI struggles to scale when customer identity is stitched together
Insurance organisations are investing heavily in AI, automation and advanced analytics. But many initiatives stall or underperform because they’re built on customer data that was never designed to be trusted as a whole.
This eBook explores the FrankensteinCustomer problem – and why fragmented customer identity continues to undermine AI in insurance.
AI doesn’t fail in isolation. It reflects the quality of the data behind it.
Across insurance, customer identities are spread across policies, claims, brokers, digital channels and partners. Each system holds a version of the truth, shaped by its own needs. When those versions are stitched together, decisions become harder to explain and confidence erodes.
This isn’t a tooling issue. It’s a foundation issue.
The Frankenstein Customer
The Frankenstein Customer is a way of describing a familiar reality: a customer record assembled from fragments, duplicates and inconsistencies that appears complete, but breaks down under scrutiny.
Go deeper into how this plays out across underwriting, claims, fraud, analytics and governance.
Stibo Systems works with insurers to help establish and maintain customer truth across complex, data-rich environments.
Through its Customer Experience Data Cloud (CXDC), we support organisations looking to govern customer identity, connect fragmented records and build a more reliable foundation for analytics and automation.
*More insights coming soon: Look out for additional perspectives, tools, and assets from the Frankenstein Customer campaign on this page.
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Looking beyond the problem?
If fragmented customer identity is limiting your AI, automation, or governance initiatives, a conversation can help clarify what a trusted customer foundation could look like in practice.
