Chris Mann, Ph.D.

Chris Mann has over 25 years of experience in credit and credit risk management, spanning academic research, senior roles at major financial institutions, and applied consulting. At Columbia University, he currently teaches Credit Risk Analytics and has taught Credit Risk Management, Quantitative Risk Management (QRM), and Machine Learning for Risk Management.  His courses have focused on understanding risk management through the lens of data analysis and modeling.

His industry career includes senior leadership roles at MUFG Bank, where he managed a team of more than 30 quantitative analysts responsible for global wholesale rating models, CCAR stress testing, and CECL reserve estimation, and at U.S. Bank, where he advised on the transition to more formal, analytics-driven rating systems. Earlier in his career, he spent nearly a decade at Moody's Investors Service in quantitative research roles, where he provided internal consulting services for the ratings teams, developed the firm's Bond Implied Rating and CDS Implied Rating models, and established Moody's rating performance standards and quarterly reporting framework.

He has published peer-reviewed research in leading journals including the Journal of Finance, Journal of Banking and Finance, and the Journal of Fixed Income. He served as chairman of the 2024 GFMI Credit Conference and has provided expert witness testimony for the IRS, where he conducted fundamental credit analysis on a large international manufacturing conglomerate.

His current research focuses on credit modeling methodology, including effective sample size for panel and clustered data, treatment of unbalanced datasets, and neural network architecture for default prediction.

Outside of his finance work, Chris maintains an active self-study curriculum in advanced mathematics and physics, including general relativity and quantum field theory. He also studies Spanish at an advanced level and has family connections to Spain.