Heng Z. Chen

Education Background

Heng received his MS degree from University of California at Davis in 1988 and PhD degree from the Ohio State University in 1992, majored in econometrics and environmental economics.

Financial Services Industry Experience:

For more than 20 years, Heng worked for several US and multi-national bank holding companies such as American Express (AXP), Discover Financial Services (DFS), GE Capital, and Hong Kong Shang Hai Banking Corporation (HSBC). Heng has a broad understanding of the end to end financial risk management process from marketing acquisitions, credit authorizations, account management, and loss recovery. Heng’s experience in the risk modeling and predictive analytics includes PD, LGD, EAD, Loss Forecasting for CCAR and EC for the retail and commercial financial businesses.

Academic Experience:

Heng began his professional career in 1993 at Michigan State University. His research was focused on the econometric models for environmental and natural resources valuation. Over the previous few years, Heng also teaches a graduate class called Predictive Analytics for Credit Risk Management at Northwestern University. He is interested in the improvement of econometric modelling techniques and their applications in financial services industry in the areas such as PD, EAD, LGD, Economic Capital, and Loss Forecasting. Heng published in several peer reviewed journals including Journal of Econometrics, American Journal of Agricultural Econometrics and Journal of Credit Risk, and contributed to academic books.

Articles by Heng Z. Chen

Original research

Semi-nonparametric estimation of operational risk capital with extreme loss events

The authors put forward a means to estimate value-at-risk capital during extreme loss events which combines SNP estimation with EVT-POT theory.

29 Feb 2024

Original research

An interpretable Comprehensive Capital Analysis and Review (CCAR) neural network model for portfolio loss forecasting and stress testing

This paper proposes an interpretable nonlinear neural network model that translates business regulatory requirements into model constraints.

16 Aug 2021

Original research

A new model for bank loan loss given default by leveraging time to recovery

In this paper, the author estimates a two-equation system: one for LGD that incorporates time to recovery as one of the model explanatory variables, and the other for time to recovery using survival models that address data censoring.

23 Aug 2018

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