General Information & Client Service
  • Americas: +1.212.553.1653
  • Asia: +852.3551.3077
  • China: +86.10.6319.6580
  • EMEA: +44.20.7772.5454
  • Japan: +81.3.5408.4100
Media Relations
  • New York: +1.212.553.0376
  • London: +44.20.7772.5456
  • Hong Kong: +852.3758.1350
  • Tokyo: +813.5408.4110
  • Sydney: +61.2.9270.8141
  • Mexico City: +001.888.779.5833
  • Buenos Aires: +0800.666.3506
  • São Paulo: +0800.891.2518

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
Whitepaper

Sovereign & Size-Adjusted EDF-Implied Rating Template (for Private Firms)

RiskCalc™ EDF™ (Expected Default Frequency) values and agency ratings are widely used credit risk measures. RiskCalc EDF values typically measure default risk for private companies, while agency ratings are only available for rated companies. A RiskCalc EDF value measures a company's standalone credit risk based on financial statement information, while an agency rating considers qualitative factors such as Business Profile, Financial Policy, external support, and country-related risks. Moody's Analytics new Sovereign & Size-Adjusted EDF-Implied Rating Template combines RiskCalc EDF values with additional factors to provide a rating comparable to agency ratings for private companies. The new template applies to RiskCalc EDF values across numerous geographies and regulatory environments. With the new template, users can generate a rating more comparable to an agency rating than RiskCalc EDF values or EDF-implied ratings. Analyzing data from 3,900+ companies in 60+ countries, we find that sovereign rating and total asset size, in addition to EDF value, have a statistically significant impact on an agency rating — our quantitative template incorporating these three variables reliably estimates agency ratings in a robust fashion.

December 2018 Pdf Maria Buitrago, Uliana Makarov, Dr. Janet Zhao, Dr. Douglas Dwyer
Whitepaper

Identifying At-Risk Names in Your Private Firm Portfolio — RiskCalc Early Warning Toolkit

This report outlines a practical approach for using RiskCalc EDF credit measures to effectively monitor large portfolios of private firms and to proactively identify at-risk names. The RiskCalc Early Warning Toolkit Excel add-in is an easy to use, yet comprehensive tool that allows users to focus costly and scarce resources on a highly targeted selection of the most at-risk names in their portfolios. This research for private firms compliments previous research on Early Warning Toolkit for public firms. The Early Warning Toolkit identifies at-risk names within a private firm portfolio well before default, using a number of different EDF-related risk metrics.

November 2018 Pdf Ziyi Sun, Dr. Janet Zhao, Gustavo Jimenez
Whitepaper

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
Presentation

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
Webinar-on-Demand

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
Whitepaper

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 Dwyer, Dr. Janet Zhao, Sara Gianfreda, Reeta Hemminki, Lucia Yang
Whitepaper

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
Article

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 Bacham, Dr. Janet Zhao
Webinar-on-Demand

CECL Quantification: Commercial & Industrial (C&I) Portfolios

In the third webinar in our CECL quantification webinars series, our experts discussed which commercial and industrial (C&I) models and methodologies can be leveraged to fulfill CECL requirements, and key considerations in transitioning these models.

March 2017 WebPage Emil Lopez, Dr. Janet Zhao
Presentation

CECL Quantification:Commercial & Industrial (C&I) Portfolios Webinar Slides

In the third webinar in our CECL quantification webinars series, our experts discussed which commercial and industrial (C&I) models and methodologies can be leveraged to fulfill CECL requirements, and key considerations in transitioning these models.

March 2017 Pdf Emil Lopez, Dr. Janet Zhao
RESULTS 1 - 10 OF 13