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Level Up Your Insurance Data Analysis | Insurance blog



Leading insurance companies are reinventing their product and customer engagement strategies to meet customers’ changing needs in real time. To make it work, they need both customer data from connected devices and IoT devices and advanced data analysis.

The insurance industry has always been data-driven. Risk models and actuarial analyzes have, and will continue to be, essential for how the industry allocates capital and assesses / prices risk.

The need to develop data analysis is more about adapting to new customer behaviors and expectations. The ever-increasing volume of customer-generated data coming from “everything’s internet” is driving the demand for insurance companies to collect and use it in new ways.

Customers are looking for new and better solutions

In all industries, we see companies that deliver relevant offers in real time through advanced data analysis that wins the market. Customers are willing to share their data when it is used to deliver value back to them.

Insurers that develop their analysis capabilities are better positioned to offer this type of customer relevance. They can provide continuous support to customers at every point of contact – from insurance to policy service to claims.

3 levels of insurance industry data analysis

1. Descriptive analysis routinely combined with automation solutions to guarantee risks and process requirements. Such analyzes are based on specific data attributes from past and present, historical risk models and current market conditions.

2. Predictive analysis Allow insurers to look into the future and, using behavioral models, better understand how a customer is likely to react to potential risks. As more customer data is entered into the model, the more complete the individual risk profile and the more accurate the predictions become.

3. Prescriptive analysis is how insurance companies begin to create strategies to help the customer reduce and manage risks. It requires large-scale real-time optimization of customer data and the insurer’s product portfolio to present a contextualized real-time recommendation in the present.

Build trust through responsible use of customer data

From the pandemic to climate change, customers are experiencing increased uncertainty about their safety and well-being. They also question whether their data will be used responsibly – but they are willing to share it in exchange for value.

The use of customer data to generate relevant real-time usage and behavioral offers that help customers reduce, manage and recover from losses can help insurance companies build customer confidence. This is the value that advanced data analysis can deliver both to the insurance customer and to the insurer.


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Disclaimer: This content is provided for general information purposes only and is not intended to be used in consultation with our professional advisors.


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