SAS-to-Python Migration

This case study highlights how HOSTA Analytics helped a mid-sized insurance provider transition from a legacy SAS-based analytics environment to a scalable, open-source Python pipeline. The project improved long-term sustainability, lowered licensing costs, and enabled adoption of modern machine learning workflows.

SAS-to-Python Migration

Title: Modernizing Analytics for a Mid-Tier U.S. Insurance Firm
Service: Codebase Modernization

Sector: Insurance · Duration: 5 weeks

Background

The client, a U.S.-based property and casualty insurance company, had a portfolio of actuarial, underwriting, and marketing models written entirely in SAS. As the company matured, leadership identified strategic challenges tied to SAS dependency:

Rising licensing and renewal costs
Difficulty hiring staff with deep SAS fluency
Inability to integrate SAS logic into cloud-first or API-driven systems

The company wanted to migrate critical models to Python without disrupting business workflows or compromising accuracy — and needed a proven framework to do it.

The Challenge

The client's team faced multiple hurdles:

Hundreds of lines of legacy SAS code with embedded macros and undocumented business logic
No internal Python expertise or validated code conversion strategy
Pressure to demonstrate ROI from migration while maintaining regulatory documentation standards
Need for side-by-side validation between SAS and Python outputs before going live

Our Approach

HOSTA Analytics delivered a structured 5-phase migration framework:

Code audit: reviewed PROC steps, macros, and data dependencies
Conversion mapping: built one-to-one equivalents using pandas, numpy, statsmodels, and scikit-learn
Side-by-side validation: ran SAS and Python in parallel on test data to confirm output integrity
Documentation updates: rewrote SOPs and internal model guides in Jupyter Notebook format
Team training: provided “Python for SAS Users” materials and live walkthroughs of the new workflow

All conversion logic was verified against production test cases and traceable for audit purposes.

Results

Migrated four core models from SAS to Python within 5 weeks
Maintained output parity ≥ 99.8% on all test cases
Cut annual licensing costs by over 60%
Enabled CI/CD pipeline integration via Python API endpoints
Internal staff upskilled with reusable Python templates and transition playbooks