Webinar
Published July 25, 2024
Advanced Analytics & Modeling Credit Decisioning
Our expert panel discusses the critical questions addressing machine learning in credit risk and key findings for best practices for building, fine-tuning and deploying robust and effective ML models.
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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
Tip Sheet
Closing the gaps: fraud prevention across the customer lifecycle
This tip sheet explores where fraud can enter the customer lifecycle and how predictive fraud risk scores help distinguish fraud from true credit risk and help you:
- Reduce first-payment default and bust-out fraud
- Detect synthetic identities earlier
- Improve prescreen and campaign ROI
- Strengthen account management decisions