Abstract Number:
3700
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Jonathan Woody (1), Robert Lund (2), Tung-Lung Wu (1), Zhicong Zhao (3)
Institutions:
(1) Mississippi State University, N/A, (2) University of California, Santa Cruz, N/A, (3) Mississippi State University, Mississippi State, MS
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
This talk will consider a novel approach to detecting anomalous transactions linked with fraud in food stamp purchases.
The methods detect clusters in the order statistics of the transaction amounts that merit further scrutiny. The techniques then use scan statistics to determine when an excessive number of transactions occur (cluster) about some price point, which is shown to be historically linked to fraud. A scoring paradigm is constructed that ranks the degree in which detected clusters and individual transactions are anomalous among approximately 250 million total transactions.
Keywords:
Fraud|Data Science|Scan Statistic|Order Statistic|Markov chain|Forensic Science
Sponsors:
IMS
Tracks:
Miscellaneous
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