Reinsurance Analytics in 2025: What You Need to Know

Reinsurance analytics is the process of analyzing data related to reinsurance to make better decisions.

It helps insurance companies understand their risks and costs when they buy reinsurance, which is insurance for insurers. By using reinsurance analytics, companies can spot patterns or problems faster and improve how they manage contracts and claims. This makes the whole insurance system more stable and efficient.

At a deeper level, reinsurance analytics involves collecting and processing large sets of data from different sources, such as claims history, market trends, and policy details. It uses statistical models and software tools to evaluate the likelihood of losses and optimize reinsurance coverage.

For example, companies might use analytics to decide how much risk to keep on their books versus how much to pass on to reinsurers.

Some common tasks in reinsurance analytics include:

• Trend analysis of claims and losses

• Risk assessment for new policies

• Pricing and negotiation support

• Monitoring the performance of reinsurance treaties

Using tools like Strada, which provides voice AI and workflow automation purpose-built for insurance, can make reinsurance analytics more efficient and actionable. Strada’s platform captures real-time insights during calls, extracts key data, and automatically completes post-call tasks through AI-powered workflows and reporting.

This allows analysts and underwriters to save time, reduce manual errors, and gain deeper, data-driven insights from every customer interaction.

In the real world, reinsurance analytics might help an insurer quickly identify if a new hurricane season will lead to higher claims. They can then adjust their reinsurance coverage or prices accordingly, rather than waiting until after losses happen. This kind of informed decision-making protects both insurers and customers.