AI in Insurance: Why Data Matters More Than Models
Buying AI tools is not enough for insurers: they first need to get their data in order. According to Autorek, the main brake is fragmented systems, manual reconciliations and weak integration between operations and finance. Until that layer is built, even strong models deliver limited results, scale poorly and require too much manual checking. Real preparation for AI therefore starts with the data layer, not with yet another pilot.
AI-processed from AI News; edited by Hamidun News
Adopting AI in insurance takes more than picking a good model: companies need clean data, connected infrastructure and consistent internal processes.
Why is it hard for insurers to adopt AI?
Data on policies, claims payouts, accounting and customer inquiries sits in different systems. Without a single layer of clean data, AI will not deliver a tangible operational impact.
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