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Predictive Modeling for Insurance Pr...

Lyu, Ting.

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Predictive Modeling for Insurance Pricing: A Comparative Analysis of Actuarial Techniques and Machine Learning Algorithms.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Predictive Modeling for Insurance Pricing: A Comparative Analysis of Actuarial Techniques and Machine Learning Algorithms./
Author:	Lyu, Ting.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
Description:	42 p.
Notes:	Source: Masters Abstracts International, Volume: 85-12.
Contained By:	Masters Abstracts International85-12.
Subject:	Statistics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31331045
ISBN:	9798382832944

Predictive Modeling for Insurance Pricing: A Comparative Analysis of Actuarial Techniques and Machine Learning Algorithms.
Lyu, Ting.

Predictive Modeling for Insurance Pricing: A Comparative Analysis of Actuarial Techniques and Machine Learning Algorithms. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 42 p.

Source: Masters Abstracts International, Volume: 85-12.

Thesis (M.S.)--University of California, Los Angeles, 2024.

This thesis examines insurance pricing with the goal of improving predictive accuracy through a comparative analysis of traditional actuarial techniques and modern machine learning algorithms. By utilizing real-world datasets from insurance companies, the research applies five distinct methodologies to analyze the key variables within the insurance dataset. The primary objective is to identify the most effective approaches in forecasting claim amounts. The findings of this study seek to advance predictive accuracy and provide substantial business value, thereby promoting innovation and excellence in risk management within the insurance industry.

ISBN: 9798382832944Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Comparative analysis

Predictive Modeling for Insurance Pricing: A Comparative Analysis of Actuarial Techniques and Machine Learning Algorithms.
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245 1 0 $a Predictive Modeling for Insurance Pricing: A Comparative Analysis of Actuarial Techniques and Machine Learning Algorithms.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 42 p.
500 $a Source: Masters Abstracts International, Volume: 85-12.
500 $a Advisor: Wu, Yingnian.
502 $a Thesis (M.S.)--University of California, Los Angeles, 2024.
520 $a This thesis examines insurance pricing with the goal of improving predictive accuracy through a comparative analysis of traditional actuarial techniques and modern machine learning algorithms. By utilizing real-world datasets from insurance companies, the research applies five distinct methodologies to analyze the key variables within the insurance dataset. The primary objective is to identify the most effective approaches in forecasting claim amounts. The findings of this study seek to advance predictive accuracy and provide substantial business value, thereby promoting innovation and excellence in risk management within the insurance industry.
590 $a School code: 0031.
650 4 $a Statistics. $3 517247
650 4 $a Information technology. $3 532993
653 $a Comparative analysis
653 $a Insurance pricing
653 $a Predictive modeling
653 $a Datasets
653 $a Machine learning algorithms
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690 $a 0489
690 $a 0800
710 2 $a University of California, Los Angeles. $b Statistics 0891. $3 2095317
773 0 $t Masters Abstracts International $g 85-12.
790 $a 0031
791 $a M.S.
792 $a 2024
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31331045