Analysis of customs data and design of risk assessment model with emphasis on the effect of hot and humid climate in customs of Khuzestan province

Its important to try to apply risk management techniques to reduce risk in customs. The purpose of this research is to develop a model of risk assessment management model using data mining algorithms. This research is based on descriptive and analytical methods based on data mining algorithms and also the present research is of method and nature, qualitative-quantitative type. The statistical population of this research includes experts and policy makers, managers and senior customs consultants of Khuzestan . In qualitative method section, 14 expert managers and senior consultants were considered, 14 expert colleagues of the organization, 10 university professors for the initial extraction of the model in the quantitative part, all managers and senior customs advisors of Khuzestan as the statistical population of the research. Were taken. In both qualitative and quantitative phases, sampling was done judiciously and purposefully. In this research, the decision tree algorithm was used to build a risk management model and analysis of customs data, which is a decision tree algorithm and analysis of customs data from Rapid miner software version 9.1. The results showed that among the risk factors identified in the transit procedure, the currency value is the highest and the type of transit is the least important. Among the risk factors identified in the insurance import procedure is the highest importance and the net weight is the least important, and among the risk factors identified in the net weight export procedure is the highest importance and the currency value is the least important.