Master the Credit Application Scorecard: Building and Deploying Predictive Models for Confident Lending

During this session, we'll discuss: 

Learn to Build & Understand Scorecards

• Build, assess, and monitor machine learning models in an intuitive drag-and-drop interface or the SAS language, R, and Python code 
• Enable your team to easily generate credit applicant predictions by developing scorecards, collection models, and Basel reports
• Manage third-party data (delinquency scores, failure scores, payment ratings, demographics, historical account activity, etc.) to know the probability of loan default and minimize your organization's risk.

Deploy Predictive Models with Confidence

• Deploy models (via Cloud, server, or local) as APIs for real-time and on-demand applications
• Track performance to ensure model currency
• Integrate directly with common third party applications like Fiserv, Jack Henry, Black Knight, Sagent, Loan Sphere, Equifax, Experian, and more
• Import and export models built in Python, R, or the SAS language