Kaleidoscope Overview

Kaleidoscope helps insurers build better predictive models using industry-wide claims data. You train your own models on Guidewire Industry Intel data alongside your internal data, without either party exposing record-level data to the other.

Why Kaleidoscope

Claims models hit a predictive ceiling when your internal data runs out. This is most common in:

• Thin segments, where you have too few claims to model reliably

• New markets or lines of business, where you have little or no history

• Low-frequency events, where even a large book has few examples

Third-party data products add new attributes (columns) to the claims you already have. They can’t add claims you don’t have. Kaleidoscope adds multi-carrier claims volume (rows) to fill those gaps, giving your models more predictive power.

Existing Industry Intel models show the kinds of problems where pooled data helps most, such as claims triage, assignment, subrogation, and litigation avoidance, and workers’ compensation severity.

How it works

Kaleidoscope uses federated learning: the analytics code moves to where the data lives, instead of the data moving to the code. Only model updates and aggregate results are exchanged.

Your engagement involves two workspaces:

• Guidewire workspace hosts the Industry Intel datasets. Guidewire prepares these datasets for your use case and connects them through a Guidewire task runner.

• Your workspace is a private environment for your organization. You sign in here, register your own data (if you are using it), and run sessions.

Guidewire registers the Industry Intel datasets in the Guidewire workspace, then connects them to your workspace. Connected datasets appear on your Datasets page with a Partner Dataset label, and you can include them in your training sessions alongside your own data. When you run a session, each dataset is processed by the task runner that hosts it, and the results are combined into a single model.

Your connection to an Industry Intel dataset is private. Other carriers can’t see that you are connected, and you can’t see their connections.

Because the Industry Intel data adds rows (more claims) with the same kinds of columns as your data, most Kaleidoscope work uses Horizontal Federated Learning (HFL).

Privacy and data protection

• Record-level Industry Intel data is never exposed to you or to any party outside Guidewire.

• Record-level data from your organization is never exposed to Guidewire or to any party outside your organization.

• Other carriers can’t see your data or connect to your workspace.

Where the computation runs

How you connect your own data determines where processing happens. Choose the option that fits your IT policy:

Option	Where your data lives	Where compute runs	What you set up
No task runner	You only use Industry Intel data	Guidewire	Nothing
Hosted task runner	Your S3 bucket or Snowflake	Guidewire, with read-only access to your data	A bucket policy or Snowflake access token
Edge task runner	Your AWS, Azure, or GCP cloud, or on-premises	Your environment	IAM roles or service principals, then a task runner

If your policy requires that analytics code runs inside your own environment, use an edge task runner.

Ways to work

• The workspace UI is where you manage users, register and validate datasets, and review sessions and results.

• The Python SDK is where data scientists and actuaries run analysis and modelling, usually from a Jupyter notebook. Guidewire provides notebooks prepared for your use case as attachments on the connected Industry Intel datasets.

Who does what

Person in your organization	Workspace role	Typical tasks
Technical administrator	Administrator	Invites users, assigns roles, registers task runners
Data engineer	Data Custodian	Registers and validates your datasets
Data scientist or actuary	Model Builder	Runs analysis and training sessions, reviews results

For details of each role, see Invite additional users.

With your permission, Guidewire data scientists can also run work on your behalf. They work through your workspace, so their sessions are visible to you.

What you keep

What you can take away from an engagement, such as a trained model or its predictions, depends on your agreement with Guidewire. Confirm this with your Guidewire contact before you start.

Next steps

Follow Getting Started to sign in and run your first session.