Prediction Lab provides an end‑to‑end predictive modeling platform tailored for insurance professionals. The solution automates the full modeling lifecycle—data integration, AI‑assisted cleaning, versioned workflows, and audit‑ready results—while allowing actuaries to retain full control and collaborate in real time. It connects directly to data warehouses or cloud storage and offers curated external datasets, scaling automatically to handle any data size without manual tuning.
Funding
Funding not disclosed
Founders
Product
Problem
Insurance companies often rely on fragmented tools and manual processes to build predictive models, leading to lengthy development cycles, difficulty maintaining version control, and challenges meeting regulatory audit requirements.
Solution
Prediction Lab offers an end‑to‑end predictive modeling platform built specifically for insurance professionals. The system connects directly to data warehouses or cloud storage, allowing users to ingest internal and curated external datasets without custom pipelines. AI‑driven data preparation suggests cleaning steps and runs automated quality checks, accelerating the preprocessing phase. Throughout the modeling workflow, every experiment, dataset transformation, and model version is automatically recorded, providing built‑in version control and comprehensive audit trails. Real‑time collaboration features let actuaries and stakeholders review results, adjust parameters, and share insights within the same environment, reducing hand‑off delays and supporting regulatory compliance.
Target Audience
Primary users are actuaries, data scientists, and analytics teams within property‑casualty, life, and health insurers who need to develop and maintain predictive models at scale.
Features
- Direct integration with on‑premise data warehouses and cloud storage, plus access to an expanding marketplace of external datasets
- AI‑assisted data cleaning and transformation with automated quality checks and missing‑value handling
- Full lifecycle automation covering data prep, feature engineering, model training, and reporting
- Enterprise‑grade version control and immutable audit logs for every modeling step
- Real‑time collaborative workspace for multiple users to view, comment, and iterate on models
- Scalable compute infrastructure that auto‑adjusts to data size without manual tuning