Findings from Ryerson University Yields New Findings on Information Technology (Feature Extraction of Auto Insurance Size of Loss Data Using Functional Principal Component Analysis): Information Technology

2022 JUL 27 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- New research on Information Technology is the subject of a report. According to news reporting from Toronto, Canada, by NewsRx editors, the research stated, “To better understand the major pattern and natural variation of statistical reporting data, visualization and interpretability have been an ongoing challenging issue, particularly in complex statistical data analysis. In auto insurance rate regulation, the study of the size-of-loss distribution is critical because it serves as a benchmark for rate filings conducted by insurance companies.”

The news correspondents obtained a quote from the research from Ryerson University, “In this work, we propose dimension reduction and feature exaction approaches via functional principal component analysis to analyse auto insurance statistical reporting data. Dimension reduction aims to estimate the overall functional pattern of the size-of-loss, while feature extraction is to study the similarity and variability of extracted features in the feature subspace. We also investigate the functionality of relative claim frequency, size-of-loss, and the pattern and variability of extracted features, using loss data at the industry level. The proposed method helps improve data explainability and understanding of the overall auto insurance size-of-loss distributions pattern. Also, we found that the proposed method can capture the tail behaviour of the size-of-loss distribution. It is also applicable to similar data analysis in many applications, including economics and finance.”

According to the news reporters, the research concluded: “The impact of this research is to help develop strategies to control and manage the proposed rate changes by insurance companies for rate regulation purposes in the aspect related to large loss loading for reinsurance pricing.”

This research has been peer-reviewed.

For more information on this research see: Feature Extraction of Auto Insurance Size of Loss Data Using Functional Principal Component Analysis. Expert Systems with Applications, 2022;198:116780. Expert Systems with Applications can be contacted at: Pergamon-elsevier Science Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, England. (Elsevier - www.elsevier.com; Expert Systems with Applications - http://www.journals.elsevier.com/expert-systems-with-applications/)

Our news journalists report that additional information may be obtained by contacting Shengkun Xie, Ryerson University, Ted Rogers School of Management, Toronto, On M5B 2K3, Canada.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.eswa.2022.116780. 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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