WIT Press


Collusion In The US Crop Insurance Program: Applied Data Mining

Price

Free (open access)

Paper DOI

10.2495/DATA040281

Volume

33

Pages

12

Published

2004

Size

1,148 kb

Author(s)

B. B. Little, W. L. Johnston, Jr., A. C. Lovell, R. M. Rejesus & S. A. Steed

Abstract

This paper quantitatively analyzes indicators of Agent (policy seller), Adjuster (indemnity claim adjuster), Producer (policy purchaser/holder) indemnity behavior suggestive of collusion in the United States Department of Agriculture (USDA) Risk Management Agency (RMA) national crop insurance program. According to guidance from the federal law and using six indicator variables of indemnity behavior, those entities equal to or exceeding 150% of the county mean (computed using a simple jackknife procedure) on all entity-relevant indicators were flagged as \“anomalous.” Log linear analysis was used to test (1) hierarchical node-node arrangements and (2) a non-recursive model of node information sharing. Chi-square distributed deviance statistic identified the optimal log linear model. The results of the applied data mining technique used here suggest that the non-recursive triplet and agent-producer doublet collusion probabilistically accounts for the greatest proportion of waste, fraud, and abuse in the federal crop insurance program. Triplet and agent-producer doublets need detailed investigation for possible collusion. Hence, this data mining technique provided a high level of confidence when 24 million records were quantitatively analyzed for possible fraud, waste, or other abuse of the crop insurance program administered by the USDA RMA. This data mining technique can be applied where vast amounts of data are available to detect patterns of collusion or conspiracy as may be of interest to the criminal justice or intelligence agencies. 1 Introduction The U.S. Congress is concerned that the United States Department of Agriculture (USDA) Risk Management Agency (RMA) crop insurance programs are

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