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

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Predictive Modeling for Insurance Pricing :/
Reminder of title:	A Comparative Analysis of Actuarial Techniques and Machine Learning Algorithms.
Author:	Lyu, Ting.
Description:	1 online resource (42 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-12.
Contained By:	Masters Abstracts International85-12.
Subject:	Statistics. -
Online resource:	click for full text (PQDT)
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. - 1 online resource (42 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382832944Subjects--Topical Terms:

556824
Statistics.
Subjects--Index Terms:

Comparative analysisIndex Terms--Genre/Form:

554714
Electronic books.

Predictive Modeling for Insurance Pricing : = A Comparative Analysis of Actuarial Techniques and Machine Learning Algorithms.
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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.
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538 $a Mode of access: World Wide Web
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653 $a Datasets
653 $a Machine learning algorithms
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