Theses and Dissertations
ORCID
https://orcid.org/0009-0009-7003-025X
Issuing Body
Mississippi State University
Advisor
Woody, Jonathan
Committee Member
Zhang, Jialin
Committee Member
Patil, Prakash
Committee Member
Wu, Tung-Lung
Committee Member
Sepehrifar, Mohammad
Date of Degree
12-13-2024
Original embargo terms
Visible MSU only 1 year
Document Type
Dissertation - Campus Access Only
Major
Mathematical Sciences
Degree Name
Doctor of Philosophy (Ph.D.)
College
College of Arts and Sciences
Department
Department of Mathematics and Statistics
Abstract
This dissertation develops a novel method to detect clusters that appear in financial transaction data that are linked to fraud. The methods utilize properties of order statistics, renewal processes, and model segmentation. We apply the Minimum Description Length (MDL) principle to select the best model. Our technique makes use of a genetic algorithm to determine the number and location of anomalous cluster(s) embedded in a set of financial transactions. We apply our novel cluster detection method to the Paycheck Protection Program (PPP), a U.S. Government initiative launched in April 2020 amid the COVID-19 pandemic to aid small businesses affected by the crisis. A total of 11.46 million loans amounting to $796 billion were approved under the program.
Recommended Citation
Boateng, Kwabena Ansong, "Advances in cluster detection via order statistics, renewal processes and model selection methods with applications to financial forensic statistics" (2024). Theses and Dissertations. 6390.
https://scholarsjunction.msstate.edu/td/6390