Using data mining to detect crop insurance fraud: is there a role for social scientists?

Roderick M. Rejesus (Assistant Professor, Department of Argicultural and Applied Economics, Texas Tech Univerity, Lubbock TX, USA)
Bertis B. Little (Executive Director, Center for Aribusiness Excellence, Tarleton State University, USA)
Ashley C. Lovell (Director of Agricultural Programs, Center for Aribusiness Excellence, Tarleton State University, USA)

Journal of Financial Crime

ISSN: 1359-0790

Publication date: 31 December 2004

Abstract

Defines data mining as the extraction of potentially useful information from large databases. Shows how data mining can be applied to detecting anomalous behaviour in American agriculture and thus support the Risk Protection Agency in its compliance mission to detect fraud in crop insurance, using corn as the crop studied and percentage of acres harvested as the key indicator for “proof of concept”. Indicates potential areas of improvement, such as the development of a single data warehouse, and the role of social scientists with knowledge of data analysis and agricultural management. Concludes that data mining could be more effective than the current technique of random selection for investigation of individual entities.

Keywords

Citation

Rejesus, R.M., Little, B.B. and Lovell, A.C. (2004), "Using data mining to detect crop insurance fraud: is there a role for social scientists?", Journal of Financial Crime, Vol. 12 No. 1, pp. 24-32. https://doi.org/10.1108/13590790510625052

Download as .RIS

Publisher

:

Emerald Group Publishing Limited

Copyright © 2004, Emerald Group Publishing Limited

To read the full version of this content please select one of the options below

You may be able to access this content by logging in via Shibboleth, Open Athens or with your Emerald account.
To rent this content from Deepdyve, please click the button.
If you think you should have access to this content, click the button to contact our support team.