Dissertation/Thesis Abstract

Statistical analysis of credit card debt collectability based on data from a debt collection agency
by Yoo, Terrie, M.S., California State University, Long Beach, 2016, 88; 10131674
Abstract (Summary)

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.

Indexing (document details)
Advisor: Korosteleva, Olga
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
Source Type: DISSERTATION
Subjects: Mathematics, Statistics
Keywords: Binary Decision Tree, Credit agency, Debt, Discriminant Analysis, Logistic Regression, Principal Components Analysis
Publication Number: 10131674
ISBN: 9781339894454
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