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Janet joined the research team of Moody's Analytics in 2008. She leads RiskCalc model development and small business modeling efforts. Janet works closely with clients to facilitate better understanding and applications of RiskCalc models. She also pushes forward on research initiatives such as exposure-at-default modeling, accounting quality measurement, and machine learning in credit risk modeling. She has published in academic and professional journals. Janet has a PhD in finance from City University of Hong Kong and a PhD in accounting from Carnegie Mellon University.

Related Insights

Features of a Lifetime PD Model: Evidence from Public, Private, and Rated Firms

With the new CECL and IFRS 9 requirements, we see an increased need for lifetime probability of default models. In this document, we formally investigate and summarize the term structure properties consistently seen across public, private, and rated firms. We observe that the default rate for “good” firms tends to increase over time, while the default rate for “bad” firms decreases over time, an indication of the mean-reversion effect seen with firms' default risk.

May 2018 Pdf Sajjad Beygiharchegani, Uliana Makarov, Dr. Janet Zhao, Dr. Douglas Dwyer

Applications of Alternative Data in Credit Decisioning

In this webinar, a panel of research and data scientist experts across Moody's Analytics discuss social data in probability of default modeling, closed and open data for location scoring, and text analytics for credit risk.

April 2018 Pdf Eric Bao, Irina Korablev, Rama Sankisa, Dr. Janet Zhao

Applications of Alternative Data in Credit Decisioning

With an immense amount of available data generated worldwide within the last two years, the next evolution of banking analytics will include information from a variety of open and closed sources.

April 2018 WebPage Eric Bao, Irina Korablev, Rama Sankisa, Dr. Janet Zhao