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Authors: Shengkun Xie and Clare Chua-Chow

Affiliation: Global Management Studies, Ted Rogers School of Management, Ryerson University, Toronto, Canada

Keyword(s): Explainable Data Analysis, Data Visualization, Principal Component Analysis, Size of Loss Frequency, Business Analytics.

Abstract: The study of high dimensional data for decision-making is rapidly growing since it often leads to more accurate information that is needed to make reliable decision. To better understand the natural variation and the pattern of statistical reporting data, visualization and interpretability of data have been an on-going challenging problem, mainly, in the area of complex statistical data analysis. In this work, we propose an approach of dimension reduction and feature extraction using principal component analysis, in a novel way, for analyzing the statistical reporting data of auto insurance. We investigate the functionality of loss relative frequency, to the size-of-loss as well as the pattern and variability of extracted features, for a better understanding of the nature of auto insurance loss data. The proposed method helps improve the data explainability and gives an in-depth analysis of the overall pattern of the size-of-loss relative frequency. The findings in our study will hel p the insurance regulators to make a better rate filling decision in the auto insurance that would benefit both the insurers and their clients. It is also applicable to similar data analysis problems in other business applications. (More)

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Paper citation in several formats:
Xie, S. and Chua-Chow, C. (2020). Improving Statistical Reporting Data Explainability via Principal Component Analysis. In Proceedings of the 9th International Conference on Data Science, Technology and Applications - DATA, ISBN 978-989-758-440-4; ISSN 2184-285X, pages 185-192. DOI: 10.5220/0009805901850192

@conference{data20,
author={Shengkun Xie. and Clare Chua{-}Chow.},
title={Improving Statistical Reporting Data Explainability via Principal Component Analysis},
booktitle={Proceedings of the 9th International Conference on Data Science, Technology and Applications - DATA,},
year={2020},
pages={185-192},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009805901850192},
isbn={978-989-758-440-4},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Data Science, Technology and Applications - DATA,
TI - Improving Statistical Reporting Data Explainability via Principal Component Analysis
SN - 978-989-758-440-4
IS - 2184-285X
AU - Xie, S.
AU - Chua-Chow, C.
PY - 2020
SP - 185
EP - 192
DO - 10.5220/0009805901850192

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