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This paper proposes a decision tree-based model for automatic assignments of IT service desk outsourcing in banking business. The model endeavors to match the most appropriate resolver group with the type of the incident ticket on behalf of the IT service desk function. Recently, service desk technologies have not addressed the problem of performance in resolving incidents dropped due to overwhelming reassignments. The paper made contribution of data preparation procedures proposed for text mining discovery algorithms. In the experiments, we acquired the incident dataset from Tivoli CTI system as text documents and then conducted data pre-processing, data transforming, decision-tree-from-text mining, and decision-tree-to-rules generation. The method of model was validated using the test dataset by the 10-fold cross-validation technique. The experimental results indicated that ID3 method could correctly assign jobs to the right group based on the incidents documents in text or typing keywords. Furthermore, the rules resulting from the rule generation from the decision tree could be properly kept in a knowledge database in order to support and assist with future assignments.