Presenting a hybrid model for identifying claims of suspicious damages in agricultural insurance

It is very difficult and maybe impossible to identify suspicious damage claims in agricultural insurance using traditional methods and using the opinions of experts among a multitude of claims. In the current research, a model for discovering suspicious damage claims in agricultural insurance using data mining techniques has been presented to help the agricultural insurance fund in identifying such claims.

The research method in the present research is applied in terms of intention and descriptive-post-event in terms of quiddity. One of the applications of data mining is anomaly detection. In the present study, a technique for detecting anomalies in the data using ensemble machine learning models is carride out. To enforcement this method, real data on compensation paid for wheat insurance (irrigated and rainfed) for one year in Khuzestan province was used. Because of differences in the process of determining damages of irrigated and rainfed wheat insurance policies, their anomalies were analyzed separately and a number of suspicious claims were acquired for each.

Based on the obtianed results, the presented model can be used to detecte suspicious claims in wet and dry wheat insurance policies. Since most of the unusual cases are caused by not providing sufficient documentation, it can be due to the presentation of forging insurance policies or the existence of collusion between the insured, the agent or the assessor. Therefore, more care should be taken in the payment process. The present study was conducted on the product and can be used for other crops as well.