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...

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,...

A conceptual model based on opportunity cost and expected utility principles establishes linkages between the likelihood of prevented planting claims in crop insurance and existing share leasing arrangements/internal farm business structures. Results of heteroskedastic probit estimation...