Islamic Azad University Researchers Detail Research in Agricultural Insurance (A model for detecting abnormal claims in crop insurance using deep learning): Agriculture – Agricultural Insurance

Agriculture Daily

2024 JAN 02 (NewsRx) -- By a News Reporter-Staff News Editor at Agriculture Daily -- Data detailed on agricultural insurance have been presented. According to news originating from Tehran, Iran, by NewsRx correspondents, research stated, “Fraud cases have increased in recent years, especially in important and sensitive financial and insurance fields. Therefore, to deal with such frauds, there is a need for different measures than traditional inspection methods.”

The news journalists obtained a quote from the research from Islamic Azad University: “Agricultural insurance is also not exempted from this threat due to its nature and wide extent and every year a lot of money is spent on paying fake damages. This research was presented with the aim of providing a model to discover unrealistic damage claims in agricultural insurance by using data mining and machine learning techniques. It was used to build a deep learning model. The data used was obtained from the Agricultural Insurance Fund and related to wet and rainfed wheat insurance policies of Khuzestan province, for which compensation was paid in the 2018-2019 crop year. After preparing and preprocessing the data, using deep learning to discover unusual cases, the action and results were evaluated by the experts of the Agricultural Insurance Fund. After analyzing the results, it was found that 1% of the damages paid were related to unrealistic requests and more care should be taken in paying the damages.”

According to the news reporters, the research concluded: “The accuracy of the model in detecting unusual cases for wet and dry wheat was 53.53 and 63.37 percent, respectively. In the review of the results, it was found that 5 categories of unusual behavior have led to the payment of unrealistic damages, and the behavior of not providing damage documentation was more frequent than the others.”

For more information on this research see: A model for detecting abnormal claims in crop insurance using deep learning. mtal at mdyryt ksb w kar hwshmnd, 2023,12(45). The publisher for mtal at mdyryt ksb w kar hwshmnd is Allameh Tabataba’i University Press.

A free version of this journal article is available at https://doi.org/10.22054/ims.2023.69377.2213.

Our news editors report that additional information may be obtained by contacting Yaqub Ahmadlou, Department of Information Technology Management, Faculty of Management and Economics, Islamic Azad University, Science and Research Branch, Tehran, Iran. Additional authors for this research include Alireza pourebrahimi, jafar tanha, Ali Rajabzadeh Ghatari.