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 modeling methods, which poses a serious operational challenge.
On this webinar, we discuss CECL and provide 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.
- 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
Seph MardHead of Model Validation, DataRobot
Head of Model Validation, DataRobot