From Data to Dollars: Next-Gen Credit Modelling

Modern credit risk modelling strategies are undergoing a radical shift as the industry moves away from stagnant credit scores toward dynamic, real-time risk assessment engines. This session from the Canadian Lenders Summit features a panel of risk experts who reveal that relying on traditional bureau data alone is a recipe for missed opportunities and increased default rates. A core insight from the video is that the integration of alternative data—such as utility payments, cash flow patterns, and even behavioral analytics—is now the primary differentiator for successful lenders. By implementing modern credit risk modelling strategies, firms can transition from a reactive posture to a predictive one, allowing them to identify high-potential borrowers that traditional systems would otherwise overlook.

The technical deep dive explores how lenders can leverage AI and machine learning to build more resilient risk engines. Adopting modern credit risk modelling strategies requires a sophisticated approach to data hygiene and model validation to avoid the common pitfalls of algorithmic bias. The speakers provide tactical advice on how to digitally transform legacy systems, ensuring that modern credit risk modelling strategies are both scalable and compliant with evolving Canadian regulations. Whether you are managing a prime or non-prime portfolio, the application of modern credit risk modelling strategies is essential for maintaining margin in a crowded marketplace. This session serves as a definitive guide for any lending professional ready to embrace the data-to-dollars evolution.

• Implementation techniques for integrating alternative data into existing underwriting workflows.
• Strategies for avoiding model overfitting and ensuring long-term predictive reliability.
• Best practices for balancing automated AI decisioning with human-led risk oversight.

Watch the full panel discussion to discover how to modernize your organization’s risk infrastructure.