Predictive analytics in insurance: top benefits, use cases, real world examples

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<abstract displayLabel="Summary">The document analyses the role of predictive analytics in the insurance sector, highlighting how it transforms risk management, the customer experience and operational efficiency. It explains how predictive models work, their benefits, use cases and applications, including fraud detection, price optimisation, churn prediction and service personalisation. It also addresses the steps involved in implementing predictive analytics, challenges such as data quality, skills shortages and regulatory compliance, and proposes change management strategies. Finally, it presents real-world examples of insurers applying these technologies and future trends such as hyper-personalisation, ethical AI and the integration of embedded insurance

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<note type="statement of responsibility">Parth Pandya</note>
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