This study examines the factors that determine the likelihood of submitting a potentially fraudulent prevented planting claim. A theoretical model is developed and the theoretical predictions are empirically verified by utilizing a binary choice model and crop insurance data from the southern...

This article analyzes anomalous patterns of agent, adjuster, and producer claim outcomes and determines the most likely pattern of collusion that is suggestive of fraud, waste, and abuse in the federal crop insurance program. Log-linear analysis of Poisson-distributed counts of anomalous...

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,...

The analysis was conducted on the USDA's Risk Management Agency insurance data and NRCS Land Resource Regions from 1994 - 2001 to assist RMA in improving program integrity. The objective is to develop a data-mining algorithm that identifies anomalous producers and counties within LRRs based upon...

This paper determines whether the opportunity costs of share leasing and the share amounts of farmers/tenants affect the likelihood of submitting a prevented planting claim. Results from our probit analysis shows that lower opportunity costs of share leasing and higher farmer/tenant share amount...