Collusion In The US Crop Insurance Program:
Applied Data Mining
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
Keywords
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