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Publication Type : Journal Article
Publisher : International Journal of Engineering and Technology
Source : International Journal of Engineering and Technology, Engg Journals Publications, Volume 8, Number 2, p.1187-1199 (2016)
Keywords : Process mining (PM), ProM, α-algorithm, Teleclaim Model (TCM), Petrinets
Campus : Mysuru
School : School of Arts and Sciences
Department : Computer Science
Year : 2016
Abstract : In this paper, we propose a process mining model for teleclaim insurance process. The major problem faced by every insurance organization is to manage enormous amount of data, which were generated for every business activities. Managing teleclaim data is a complex task, which requires process model to know the control flow of the event logs. Researchers today use ProM tool as an extensible framework that supports a wide variety of process mining techniques which makes use of a-algorithm. a-algorithm generates a process model. But, the generated process model will not be specific for every cases instead it is the generalized model. So, the logs which will not suit the compliance or the process model will not be considered its neglected which is the main drawback of a-algorithm. To overcome this problem, we propose a Teleclaim Model algorithm. The proposed algorithm generates process models and traces for teleclaim dataset, in which processes flow within their respective models, so that it will not eliminate the logs which do not fit into given compliance. The fitness for each model is obtained by replaying the event logs on the process models to analyze its behavior. The proposed process model is useful for insurance organizations to improve their business process for their clients. Fitness for the proposed models can be used as a base of the insurance company to decide whether the claim is valid or not.
Cite this Research Publication : K. Ganesha, Gagana, J., and Namratha, A. C., “A novel process mining model for teleclaim insurance process”, International Journal of Engineering and Technology, vol. 8, pp. 1187-1199, 2016.