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Machine Learning in Building a Prepayment Model

1 Hour
Janet Jozwik
Fan Zhang
Lei Zhao

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Prepayment models are complex, machine learning can help - In this webinar we discuss a number of applications:


- Preprocessing the Data

- Determining which variables are important to include in prepayment models

Modeling Approach

 - Evaluating machine learning approaches

Tuning the Model

- Opening the black box

- Optimizing model performance

About The Hosts

Janet Jozwik
Managing Director - RiskSpan


Fan Zhang
Director of Model Development

Fan Zhang has 12 years of quantitative finance experience specializing in behavioral modeling, fixed income analysis and, machine learning. At RiskSpan, Fan leads the quantitative modeling team where he is currently driving improvements to prepay modeling and application of cutting edge machine learning methods. Fan was a senior quantitative manager at Capital One where he worked on prepayment, deposit, MSR, auto, interest rate term structure, and economic capital modeling. He was also a senior financial engineer at Fannie Mae managing a team to validate model implementation and risk analytics. Fan holds an MBA from the University of Maryland and a BA in Economics from the University of Michigan.

Lei Zhao
Quantitative Modeling Analyst

Lei Zhao is a key member of the quantitative modeling team at RiskSpan. Lei has done extensive research on clustering methodologies and his postdoctoral research paper has been cited over a hundred times in scholarly publications. Lei holds a Master of Science degree in Financial Engineering from University of California, Los Angeles, and a PhD in Mechanical Engineering from Zhejiang University, China.