Leveling-up your insurance coverage knowledge analytics | Insurance coverage Weblog
Leading insurance companies are reinventing their product and customer engagement strategies to meet the evolving needs of customers in real-time. To make it work, they need both customer data from connected and IoT devices and advanced data analytics.
The insurance industry has always been data-driven. Risk models and actuarial analytics have, and will continue to be, essential to how the industry allocates capital as well as assesses/prices risk.
The need to evolve data analytics is more about adapting to new customer behaviors and expectations. The ever-increasing volume of customer-generated data coming from the “internet of everything” is driving demand for insurers to collect and use it in new ways.
Customers seek new and better solutions
Across every industry, we see companies that deliver relevant offers in real-time through advanced data analytics winning in the market. Customers are willing to share their data when it is used to deliver value back to them.
Insurers that mature their analytics capabilities are better positioned to offer this kind of customer relevance. They can provide continuous support to customers at every touchpoint—from underwriting to policy servicing to claims.
3 levels of insurance industry data analytics
1. Descriptive analytics are routinely combined with automation solutions to underwrite risk and process claims. Such analytics are based on specific data attributes from the past and present, historic risk models, and current market conditions.
2. Predictive analytics allow insurers to look into the future and, using behavioral models, better understand how a customer is likely to respond to potential risks. As more customer data feed into the model, the more complete the individual risk profile and more accurate the predictions become.
3. Prescriptive analytics are how insurers start creating strategies to help the customer mitigate and manage risk. That requires large-scale, real-time optimization of customer data and the insurer’s product portfolio to present a contextualized real-time recommendation at the moment.
Building trust through responsible use of customer data
From the pandemic to climate change, customers face heightened 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 behavior-based offers that help customers mitigate, manage, and recover from loss can help insurers build trust with customers. That’s the value advanced data analytics can deliver both to the insurance customer and to the insurer.
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