Predicting Customer Churn for Subprime Auto Loan Borrowers
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Location
Virtual
Start Date
9-11-2020
End Date
9-11-2020
Description
This presentation specifically discusses the notion of churn. Customer churn (commonly referred to as churn) is the idea that customers can refrain from doing business with a provider by discontinuing purchases of the good or service provided by the firm (Gordini and Veglio, 2017; Tamaddoni et al., 2016; Knox and Van Oest, 2014; Sharma and Panigrahi, 2011).
The presentation will describe churn in the context of the banking world. More specifically, the author examines the determinants of used car customer auto loan churn. Using a combination of both traditional (logistic regression, linear discriminant analysis) as well as non-traditional machine learning (decision trees and random forests) supervised classification methods the study finds a clear difference between the full model and the restricted model. Furthermore, the random forest classification technique reports the strongest performance and details the most important character variables to be individual net worth. Both the explanatory and predictive power of each of the models is analyzed using multiple performance measures.
Predicting Customer Churn for Subprime Auto Loan Borrowers
Virtual
This presentation specifically discusses the notion of churn. Customer churn (commonly referred to as churn) is the idea that customers can refrain from doing business with a provider by discontinuing purchases of the good or service provided by the firm (Gordini and Veglio, 2017; Tamaddoni et al., 2016; Knox and Van Oest, 2014; Sharma and Panigrahi, 2011).
The presentation will describe churn in the context of the banking world. More specifically, the author examines the determinants of used car customer auto loan churn. Using a combination of both traditional (logistic regression, linear discriminant analysis) as well as non-traditional machine learning (decision trees and random forests) supervised classification methods the study finds a clear difference between the full model and the restricted model. Furthermore, the random forest classification technique reports the strongest performance and details the most important character variables to be individual net worth. Both the explanatory and predictive power of each of the models is analyzed using multiple performance measures.
