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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

Combining Financial and Behavioral Information to Predict Defaults for Small and Medium-Sized Enterprises – A Dynamic Weighting Approach

One large challenge lenders currently face is how to combine different types of information into metrics that can support good business decisions. Currently, the banking industry uses two primary types of information — financial information and behavioral information — independently, to assess risk. Financial information includes Income Statement, Balance Sheet, Cash Flow, and Financial Ratios. Behavioral information includes spending and payment patterns, among others. Both types of information provide unique insights, but, to date, they have not been combined to generate one comprehensive risk metric for commercial use.

September 2017 Pdf Alessio Balduini, Dr. Douglas DwyerDr. Janet Zhao, Sara Gianfreda, Reeta Hemminki, Lucia Yang

Moody's Analytics RiskCalc Transfer Pricing Solution

Tax authorities monitor cross-border, inter-company loan and financing transactions to curb tax avoidance and require arm's length pricing for such transactions. At the core of arm's length pricing is the process of understanding the creditworthiness of a borrower and identifying a typical interest rate charged to borrowers with comparable credit ratings. The Moody's Analytics RiskCalc Transfer Pricing Excel Template provides a consistent, analytical solution to the arm's length transfer pricing process. This document explains the methodology behind this tool.

August 2017 Pdf Dr. Janet Zhao, Jeunghyun Kim

Machine Learning: Challenges, Lessons, and Opportunities in Credit Risk Modeling

In this article, we analyze the performance of several machine learning methods in assessing credit risk of small and medium-sized borrowers.

July 2017 WebPage Dinesh BachamDr. Janet Zhao