Case Study
Published June 3, 2025
Advanced Analytics & Modeling Credit Decisioning Marketing
The Forrester Total Economic Impact™ (TEI) study, commissioned by Experian, explores the quantifiable and unquantified benefits of implementing the Experian Ascend Platform™. Based on interviews with global financial institutions, the report provides a comprehensive analysis of how the platform improves credit decisioning, reduces fraud, and delivers significant ROI.
Key insights include:
- 183% return on investment (ROI) and $13.3M in net present value (NPV) over three years
- 67% improvement in credit decisioning efficiency
- 20% reduction in default costs through better risk modeling
- 5% annual growth in application volume without added headcount
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Infographic
Turning missed moments into revenue
View our infographic to learn how to capture high-intent moments and turn them into lasting revenue. Discover:
- The cost of waiting and the impact on growth
- The benefits of real-time engagement strategies
- How better data powers your timing advantage
- Why partnering with us makes a difference
Case Study
How one lender achieved a 5 month payback
Education Credit Union modernized underwriting with Experian’s Advanced Decisioning—driving faster decisions, stronger approvals, and rapid ROI in a competitive market.
- 5-month payback period
- 4.4% lift in performance & funding
- $4M+ incremental originations
Video
5 model classification blind spots to watch out for in 2026
Model classification now drives governance in AI environments—but many frameworks lag reality. Static, inconsistent approaches create risk as inventories grow.
- Refresh classification continuously
- Map dependencies and shadow models
- Standardize decisions and documentation
White Paper
Why efficient hyperparameter tuning matters: Putting Bayesian Optimization to the test
Bayesian Optimization offers a more efficient way to tune machine learning models at scale. This white paper presents a controlled comparison with Grid Search using production credit-risk data to measure runtime and model performance.
- Up to 80× faster hyperparameter tuning
- No loss in CV or holdout AUC
- Evidence from LightGBM credit-risk models
- Guidance for governed ML environments