Decision making based on multiple models
Is it possible to design a decision engine based on multiple models? Learn how the EyeOnRisk platform handles multiple models with conditional rule sets.
Is it possible to design a decision engine based on multiple models? Learn how the EyeOnRisk platform handles multiple models with conditional rule sets.
Introducing a new decision engine feature on the EyeOnRisk to ensure you get the right answers for your organization.
AI FOR BANKS: THE NEXT GENERATION OF CREDIT MODELING OVERCOMING THE DRAWBACKS OF TRADITIONAL CREDIT SCORING
How the EyeOnRisk platform addresses the unique challenges faced by both traditional banks and fintech companies.
Discover the essential features that make a credit risk modeling platform truly agile and effective.
Explore how augmented reality can revolutionize the way we view and interact with credit risk data.
Learn how external data sources can provide a complete view of credit risk assessment.
Discover strategies for effective population segmentation to improve credit risk model accuracy.
Why customized credit risk modeling platforms are essential for different business needs.
How to create an efficient assembly line approach for developing predictive models.
Addressing bias and discrimination in credit risk assessment to ensure fair lending practices.
Best practices for responsible credit management and sustainable lending practices.
Understanding and implementing Weight of Evidence methodology in credit risk modeling.
How the pandemic has impacted credit risk modeling and what changes are needed.
Key insights and lessons learned about credit risk modeling from industry experience.
Watch demonstration videos showcasing the features and capabilities of the EyeOnRisk platform.
Key lessons learned from deploying machine learning models in production environments.
Comprehensive overview of the EyeOnRisk platform for end-to-end credit risk modeling.
The case for transitioning to AI-based credit scoring models and the benefits of early adoption.
How traditional banks are falling behind without AI-powered credit risk modeling solutions.
The limitations of traditional credit risk models and how machine learning provides better solutions.