Researchers from University of Technology Sydney Provide Details of New Studies and Findings in the Area of Artificial Intelligence (Cost-sensitive Parallel Learning Framework for Insurance Intelligence Operation)

2019 NOV 28 (NewsRx) -- By a News Reporter-Staff News Editor at Network Business Daily -- Investigators publish new report on Artificial Intelligence. According to news reporting originating from Sydney, Australia, by NewsRx correspondents, research stated, “Recent advancements in artificial intelligence are providing the insurance industry with new opportunities to create tailored solutions and services based on newfound knowledge of consumers, and the execution of enhanced operations and business functions. However, insurance data are heterogeneous, and imbalanced class distribution with low frequency and high dimensions, which presents four major challenges to machine learning in real-world business.”

Financial supporters for this research include Australian Government through the Australian Research Council, Australia Government Department of Health, Australia Research Alliance for Children and Youth and Global Business College Australia.

Our news editors obtained a quote from the research from the University of Technology Sydney, “Traditional machine learning algorithms can typically apply to standard data sets, which are normally homogeneous and balanced. In this paper, we focus on an efficient cost-sensitive parallel learning framework (CPLF) to enhance insurance operations with a deep learning approach that does not require preprocessing. Our approach comprises a novel, unified, end-to-end cost-sensitive parallel neural network that learns real-world heterogeneous data. A specifically designed cost-sensitive matrix then automatically generates a robust model for learning minority classifications, and the parameters of both the cost-sensitive matrix and the hybrid neural network are alternately but jointly optimized during training. We also study the CPLF-based architecture for a real-world insurance intelligence operation system, and demonstrate fraud detection and policy renewal experiments on this system.”

According to the news editors, the research concluded: “The results of comparative experiments on real-world insurance data sets reflecting actual business cases demonstrate the effectiveness of our design.”

For more information on this research see: Cost-sensitive Parallel Learning Framework for Insurance Intelligence Operation. IEEE Transactions on Industrial Electronics, 2019;66(12):9713-9723. IEEE Transactions on Industrial Electronics can be contacted at: Ieee-Inst Electrical Electronics Engineers Inc, 445 Hoes Lane, Piscataway, NJ 08855-4141, USA. (Institute of Electrical and Electronics Engineers - http://www.ieee.org/; IEEE Transactions on Industrial Electronics - http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=41)

The news editors report that additional information may be obtained by contacting S.R. Pan, University of Technology, Feit, Center for Artificial Intelligence, Sydney, Nsw 2007, Australia. Additional authors for this research include X.X. Jiang, G.D. Long, J. Jiang, C.Q. Zhang and F. Xiong.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1109/TIE.2018.2873526. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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