The global financial recession in 2009 brought attention to consumers and financial industries concerning the important role credit history plays regarding lending and debt repayment. Going through this financial era, especially, collection agencies have made continued effort seeking strategies to further maximize their financial benefit and minimize risks. For this project, collection agency data were analyzed for the purpose of collecting on past-due accounts receivable balances and seeking strategies to sort through the thousands of records of consumers to increase the re-collectability of the debt. The data were modeled using the methods of Principal Components Analysis, Fisher’s Discriminant Analysis, Classification through Logistic Regression, and Binary Decision Tree.
|Commitee:||Safer, Alan, Suaray, Kagba|
|School:||California State University, Long Beach|
|Department:||Mathematics and Statistics|
|School Location:||United States -- California|
|Source:||MAI 55/06M(E), Masters Abstracts International|
|Keywords:||Binary Decision Tree, Credit agency, Debt, Discriminant Analysis, Logistic Regression, Principal Components Analysis|
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