Successful CECL Compliance with Automated Machine Learning
Thought Leadership Webinar: PRMIA is pleased to share this opportunity with you as a resource for your risk career. Thank you to DataRobot for making this webinar available to the PRMIA community.
The new Current Expected Credit Loss (CECL) accounting standard is expected to vastly increase costs associated with loan provisioning and loan loss reserve processes. To ensure compliance, banks of all sizes will need to develop accurate and reasonable loan loss forecasts using modelling methods, which poses a serious operational challenge.
Please join us as we discuss CECL and a holistic roadmap for how banks can successfully use automated machine learning to quickly and cost effectively build highly accurate and transparent loss forecasting models for CECL compliance.
You'll Discover:
- An overview of CECL and how banks can build and maintain a compliant CECL program
- How to build highly accurate expected credit loss models, including dual-risk rating models (PD/LGD)
- How to automatically generate industry standard compliance documentation with the click of a button
- How to maximize transparency while ensuring adaptability and scalability with Automated Machine Learning
Presented by: Seph Mard, Head of Model Validation, DataRobot
Moderated by: Michael Ivie, Executive Vice President, Head of Financial Services Consulting at Arrayo and PRMIA NY Steering Committee Leader
Date and Time: Thursday, July 26, 2018 at 11:00 a.m. EDT
This learning opportunity is made possible for the PRMIA network through the generous contributions of DataRobot.