Automated 70% of a 2,500-Module SAS Migration for a U.S. Based Consumer Credit Provider with Data Lifter
Client Overview
The client is a U.S. based financial services provider ranked among the top 20 credit card issuers and top 10 merchant acquirers. With millions of customers and nearly $8 billion in banking assets, the business relies heavily on data-driven operations for credit decisions, reporting, and regulatory compliance.
As their analytics environment grew, thousands of SAS modules became difficult to maintain and scale. Indium partnered with the client to migrate these workloads to Snowflake and Python using Data Lifter, Indium’s proprietary agentic AI platform.
When Does a Legacy Analytics Environment Become a Bottleneck?
Over time, the client’s analytics environment grew to include nearly 2,500 SAS modules supporting data processing, reporting, and regulatory analytics. These programs supported everything from operational reporting to regulatory analytics.
Maintaining the environment became increasingly difficult as the organization scaled.
01
Rising Platform Costs
High licensing and operational costs associated with maintaining the SAS environment.
02
Limited Scalability
The legacy platform struggled to scale efficiently as data volumes and analytics demands increased.
03
Talent Availability Risks
Dependence on a shrinking pool of SAS specialists made long-term maintenance challenging.
04
Barriers to Modern Analytics
Integrating cloud data platforms, advanced analytics, and AI workloads was difficult within the existing environment.
05
Complex Migration Effort
Rewriting thousands of SAS modules while preserving business logic and data integrity posed significant risk.
Diagnosed the Gaps Before Designing the Solution
Instead of immediately beginning code conversion, the platform was used to understand how the existing SAS environment worked.
Data Lifter analyzed dependencies, business logic, and operational workflows to identify system fragility, migration risks, and opportunities for safe automation.
Diagnosed the Gaps Before Designing the Solution
Instead of immediately beginning code conversion, the platform was used to understand how the existing SAS environment worked.
Data Lifter analyzed dependencies, business logic, and operational workflows to identify system fragility, migration risks, and opportunities for safe automation.
01
Mapped the SAS Landscape
The platform cataloged nearly 2,500 SAS modules and documented their dependencies to understand how data and logic flowed across the environment.
02
Identifying Hidden Business Logic
Many modules contained embedded business rules built over years of development. These rules were analyzed and documented to prevent loss of logic during migration.
03
Detected Migration Risks Early
Data Lifter identified areas where manual conversion could introduce errors or break downstream processes.
04
Prepared the Environment for AI-Assisted Conversion
The required tooling, testing frameworks, and validation mechanisms were established to support the migration. The platform handled SAS module conversion and validation to the target architecture.
A Modernized Data Analytics Foundation
With the migration complete, the client now runs critical analytics workloads on a modern Python and Snowflake architecture. The new platform supports high-volume data processing and removes dependency on legacy SAS systems.
Teams can now build and deploy analytics workflows faster while maintaining the business logic and reliability the organization depends on. Data Lifter created a foundation for faster analytics development with greater adaptability.
Claims dispatch activities moved through an automated workflow and reduced dependency on manual coordination and repetitive operational follow-ups.
Achieved End-to-End Legacy SAS Modernization
The migration went beyond replacing SAS. Data Lifter helped establish a scalable analytics foundation while preserving the business logic and reliability that the organization’s operations depend on, which is an essential requirement for any data migration.
Accelerated Migration
AI-assisted conversion automated nearly 70% of the code translation process.
Quality and Functional Parity
Parallel validation ensured functional parity between SAS and Python outputs. Automated comparisons verified accuracy before production cutover.
Mapping the data landscape
We began by meticulously understanding the existing data flows ("AS IS") from the policy issuance and agency systems into the designated data platform (data warehouse or data lake). This comprehensive mapping exercise ensured a seamless data integration process.
Scalable Delivery Mode
A factory model used four parallel PODs across 34 tranches. This structure maintained consistent throughput and preserved quality controls throughout the migration.