Shanshan Li

Title: Innovations of Shanshan Li in Predictive Modeling

Introduction

Shanshan Li is an accomplished inventor based in Springfield, MA, known for his significant contributions to the field of predictive modeling. With a total of four patents to his name, Li has developed innovative systems that leverage artificial intelligence to enhance predictive analytics in various applications.

Latest Patents

Li's latest patents include "Systems and methods for predictive modeling" and "Systems and methods for excluded risk factor predictive modeling." The first patent discusses methods and systems for an interdependent series of AI models. In this embodiment, a processor receives projection assumption inputs from a user device and executes a population builder machine learning model to predict a dynamic adjustment table. It applies the model to population data to generate a value population file, simulating a subset of the population based on the predicted table. This file contains value cells representing instances of a product. The device then runs a mortality machine learning model to determine mortality data for the product using the simulated population. Finally, it executes a flow projection model to generate a projection report for the product, incorporating mortality data and projection assumptions.

The second patent outlines a suite of fluidless predictive machine learning models, which includes a fluidless mortality module, smoking propensity model, and prescription fills model. These fluidless machine learning models are trained against a corpus of historical underwriting applications of a sponsoring enterprise, including clinical data of historical applicants. A data appended procedure supplements historical applications data with public records and credit risks. Various features of this data are engineered for improved predictive characteristics. Fluidless models are trained by applying a random forest ensemble, including survival, regression, and classification models. The trained models produce high-resolution, individual mortality scores. A fluidless underwriting protocol runs these predictive models to assess mortality risk and other risk attributes of a fluidless application that excludes clinical data to determine whether to present an accelerated underwriting offer. If any of the fluidless predictive models determines a high-risk target, the applicant is required to submit clinical data.

Career Highlights

Shanshan Li is currently employed at Massachusetts Mutual Life Insurance Company, where he applies his expertise in predictive modeling to improve underwriting processes and risk assessment.

Collaborations

Li collaborates with talented colleagues, including Marc Maier and Hayley Carlotto, who contribute to the innovative projects at Massachusetts Mutual Life Insurance Company.

Conclusion

Shanshan Li's