Federated Learning in Insurance: What You Need to Know 2025

Federated learning is a way for computers to learn together without sharing their private data.

In insurance, this means companies can improve their models by learning from many different sources, but without ever exposing customer details. This keeps data safe and private, which is very important when dealing with sensitive information like claims or personal details.

Instead of sending data to a central place, federated learning sends updates about what the data taught each computer. Those updates get combined to create smarter models for things like fraud detection or risk assessment.

Here's how it works in simple steps:

• Each insurer trains a model on their own data locally.

• Only the learned improvements, not the raw data, are shared.

• These improvements get combined to update a global model.

• The updated model is sent back to all insurers to keep improving.

This approach fits well in insurance because it respects data rules and helps companies collaborate without risks. It also allows many small datasets to work together to improve overall performance.

For those interested in how it applies technically, federated learning involves multiple rounds where models locally optimize based on unique, private datasets and then send encrypted updates to a central server for aggregation. This reduces privacy risks compared to traditional data pooling.

Plus, techniques such as differential privacy and secure multi-party computation are often integrated to add extra layers of security.

Federated learning is built into Strada’s Voice AI platform, allowing it to continuously improve conversational intelligence across insurers without ever sharing sensitive customer data.

Strada’s security foundation includes SOC 2 Type II certification, data isolation by customer, and strict training data privacy, meaning your data is never used to improve another customer’s results, and nothing sent to language model providers is stored or reused.