STATISTICS COLLOQUIUM
Lu Lu
Visiting Assistant Professor of Data Sciences
Smith College
Applications of statistics and machine learning in credit scoring:
a case study
Abstract
Credit scoring is the process to assess the default risk based on the borrower's characteristics. Lending Club (LC) is a P2P platform that issues personal loans. Their datasets include several features about each loan: borrower's FICO score, employment length, LC assigned loan grade, etc. The main goal here is to predict the default risk of the loan using these factors. We will apply several machine learning algorithms such as random forests to LC's data and compare their performances. Since most loans are in good status, the modeling also involves dealing with the class imbalance problem.
DATE: Wednesday, October 17, 2018
TIME: 4:00 pm
PLACE: Philip E. Austin Bldg., Rm. 108
Coffee will be served at 3:30 pm in the Noether Lounge (AUST 326)
For more information, contact: Tracy Burke at tracy.burke@uconn.edu