Statistics and Analytical Sciences
The credit industry creates models to determine the risk of lending money to consumers as well as to commercial customers. These models are heavily regulated in the U.S. as well as in other countries. Model inputs must be explainable to customers as well as to regulators. Two such modeling approaches that are currently commonly used are logistic regression models and time series models. This paper steps through the preprocessing and model building of these two models on a large commercial data set and compares the predictive ability of these two methods. The two models achieved similar accuracy results: the logistic model had an accuracy of 89.6% while the time series model had an accuracy of 89.3%.
Staples, Lauren, "A Comparison of the Predictive Ability of Logistic Regression and Time Series Analysis on Business Credit Data" (2018). Grey Literature from PhD Candidates. 9.