Case Study: Leading Credit Provider achieves accurate, risk-free scoring model testing with Synthesized

Test Rigorously and Risk-Free with High-Quality Synthesized Data

Leading Credit Provider wanted to build its own proprietary scoring engine to improve customer segmentation but faced the need to test and validate models on millions of representative profiles without risking real customer data. Manual profile creation was too slow and sparse to cover edge cases, so the customer partnered with Synthesized and its ML-driven synthesization engine / Synthesized DataOps platform to get high-quality test data safely and at scale.

Synthesized provided on-demand, high-fidelity synthetic datasets—generated in minutes rather than the days required by manual methods and faster than competitors’ ~10-hour average—delivering millions of representative records, 1000+ simulated scenarios, and comprehensive coverage of outliers and edge cases with zero risk to sensitive information. The Synthesized platform also consolidated test-data workflows, sped up provisioning, enhanced distributed-team collaboration, and enabled rigorous, risk-free validation of the new scoring engine before launch.