Granular risk rating models allow creditors to understand the credit risk of individual loans in a portfolio, facilitating underwriting and monitoring activities. In this webinar we will outline the value of granular risk rating models for CECL.
Join us for an in-depth analysis of CRE loan performance and credit risks under Moody’s latest economic and real estate scenarios.
Credit loss forecasting models are only as effective as the data on which they were built, and few, if any, were designed to capture the effects a sudden pandemic would unleash on the U.S. economy. In times like this, how are financial institutions determining the right amount to set aside for future credit losses?
Join us for a comprehensive presentation on the state of the U.S. CRE market, and the impact on measures of credit quality.
Volatility has risen significantly in financial markets, driven by COVID-19. How might this affect US multifamily and commercial real estate (CRE) transaction markets? What are the mechanisms through which panic and a flight to safety will hurt some markets but benefit some players?
We look at climate risk and consider how a heating planet might impact a bank's performance
Expanding Roles of Artificial Intelligence and Machine Learning in Lending and Credit Risk Management
With ever-expanding and improving AI and Machine Learning available, we explore how a lending officer can make good decisions faster and cheaper through AI. Will AI/ML refine existing processes? Or lead to completely new approaches? Or Both? What is the promise? And what is the risk?
Employing a data-driven approach to risk rating commercial loans has gone from a nascent idea to an established practice, allowing financial institutions to make informed decisions, improve profitability, and better identify trends in risk. Join us for an in-depth discussion on leading practices and lessons learned from a decade of enhancing the process of risk rating commercial loans.
Learn how data and technology are being used to improve CRE lending and investment decisions…and how to motivate your underwriting staff!
When banks manage risk, conservatism is a virtue. We, as citizens, want banks to hold slightly more capital than strictly necessary and to make, at the margin, more provisions for potential loan losses. Moreover, we want them to be generally cautious in their underwriting. But what is the best way to arrive at these conservative calculations?
The traditional build-and-validate modeling approach is expensive and taxing. A more positive and productive validation experience entails competing models developed by independent teams.