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Financial forensic statistics: novel methods and a case study.

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):

Robert Lund  
University of California, Santa Cruz
Tung-Lung Wu  
Mississippi State University
Zhicong Zhao  
Mississippi State University

First Author:

Jonathan Woody  
Mississippi State University

Presenting Author:

Jonathan Woody  
Mississippi State University

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

Can this be considered for alternate subtype?

Yes

Are you interested in volunteering to serve as a session chair?

Yes

I have read and understand that JSM participants must abide by the Participant Guidelines.

Yes

I understand that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is non-refundable.

I understand