Global Credit balances risk and return as it harnesses AI to build a competitive advantage in financial services.
Global Credit needed to grow online lending while keeping risk models accurate and explainable. The in-house data team spent most of its time on pipeline work instead of modeling.
Prustaz partnered with the risk team to standardize feature stores, train interpretable scoring models, and deploy monitoring dashboards with human review gates for edge cases.
Several high-impact models shipped in eight weeks, increasing loan acceptance rates while portfolio risk indicators stayed within policy limits.
As a regulated lender, Global Credit keeps data science in-house — but needed a platform and delivery partner to move faster without compromising governance.
We introduced reusable training pipelines and approval checkpoints so analysts could test hypotheses without rebuilding infrastructure each time.
Production models include drift detection and reason codes for declined applications, giving risk officers confidence in automated decisions.
High-value or borderline applications route to underwriters with model explanations attached, preserving relationship banking strengths.
“The strongest streamlining the ai lifecycle in lending work starts with the real workflow and makes the next step obvious.”