Managing risk is a delicate business. And in a world with instant access to just about everything, real-time decision making is no longer a luxury, it’s a necessity -- especially for financial institutions. Particularly when evaluating the creditworthiness of consumers and businesses, making decisions using antiquated systems and manual processes just won’t cut it. 

In this on-demand webinar, our experts will explore how to make quick, consistent and reliable decisions about credit risk and credit access.

Learn how to:

  • Decrease risk and exposure to credit losses.
  • Determine customers' borrowing strengths and weaknesses in real time.
  • Keep pace with market and customer demands.
  • Automate key processes to avoid overtaxing IT resources.

Featured Speakers

Leonard RosemanLeonard Roseman, PhD 
Chief Scoring Officer and Vice President of Statistical Analysis
Capital One

Leonard Roseman received his bachelor's degree in physics from Swarthmore College and his master's degree and doctorate in statistics from Harvard University. Roseman has been an applied statistician for over three decades in various industries: high tech (BBN), biotech (Chiron), medical devices (W.L. Gore), strategy consulting (MMG, marchFirst and Arthur Andersen), database marketing (Seurat and Catalina Marketing) and financial services (Capital One). He has been a Vice President of Statistics in Credit Risk Management at Capital One since 2004 and was appointed Chief Scoring Officer in June 2011.

Mark DaytonMark Dayton
Senior Risk Solutions Architect
SAS

Mark Dayton has extensive experience in capital markets (Bear Stearns), retail banking (First Union, Barnett Bank, Bank of America), consulting (SRA International) and the software industry (Epiphany and SAS). Possessing knowledge and hands-on skills in both SAS predictive modeling and the end-to-end technical architecture of SAS, Dayton most recently focused on the finer aspects of using SAS risk solutions to better manage the analytic model life cycle. This includes the streamlined deployment of SAS models that are executable from credit risk-centric operational systems in real time.

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