MCFAM Seminar Series: “Improving life expectancy estimation with tree-based ensemble model for life settlements"

Please join us for our last seminar of the semester either in person or virtually! This seminar will feature Tianze Li from Longevity Holdings who will discuss improving life expectancy estimation with tree-based ensemble model for life settlements.

Note: Refreshments will be served to those who attend in person in Vincent Hall.

Tianze Li
Data Scientist
Longevity Holdings

About the lecture
Estimating life expectancy is crucial in the life settlement industry, but traditional methods such as Proportional Hazard models can be inefficient when taking into consideration possible co-morbidities. In this seminar, Tianze Li presents a novel approach to life expectancy estimation by transforming the problem into a classification task using a tree-based ensemble model. This method is more efficient at considering co-morbidities and provides significantly better performance than traditional methods. Li and his team's approach has the potential to improve the accuracy of life expectancy estimates and facilitate better decision-making in the life settlement industry.

About the speaker
Tianze Li earned his bachelor's degree in mathematics from the University of Minnesota and completed his master's degree in financial mathematics in 2019. He joined Longevity Holdings as a data analyst in 2018 and is currently serving as a data scientist. His primary focus in this role is on leveraging his interest in machine learning modeling to advance the company's data-driven initiatives, with an emphasis on Natural Language Processing and Life Expectancy models.