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Risk Prediction for Property & Casua...

Varadharajan, Sriram.

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Risk Prediction for Property & Casualty Insurance Policies.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Risk Prediction for Property & Casualty Insurance Policies./
Author:	Varadharajan, Sriram.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	17 p.
Notes:	Source: Masters Abstracts International, Volume: 80-02.
Contained By:	Masters Abstracts International80-02.
Subject:	Computer Engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10970661
ISBN:	9780438298699

Risk Prediction for Property & Casualty Insurance Policies.
Varadharajan, Sriram.

Risk Prediction for Property & Casualty Insurance Policies. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 17 p.

Source: Masters Abstracts International, Volume: 80-02.

Thesis (M.S.)--University of Massachusetts Lowell, 2018.

This item must not be added to any third party search indexes.

The core idea behind insurance is spreading the risk among a pool of insurers. The concept of evaluating a risk determines the outcome of the success of an insurance company. With the advent of big data systems, the amount of risk data that gets collected is huge. With proper modelling of these data the risk prediction process can be streamlined and can get close to reality. This will obviously help companies make educated decisions on how to go about insuring a risk. The aim of this project is to build a few models that will help quantify the risk associated with insuring a particular business. The scope of this project was narrowed to data from Property and Casualty business of Insurance . Property and Casualty business (P&C) refers to insuring personal properties like home and cars and also insuring business entities like boilers and machinery, ship building company, and so on. Multiple models are going to be evaluated for this process, and based on the accuracy of the models, decision to choose a model will be made. The models will be built following the basic principles of predictive modelling which encompasses data preprocessing, scaling, training, validation, and testing. The dataset that we propose to use is publicly available at https://catalog.data.gov/dataset/iowa- property-casualty- insurance-premiurns-and-losses. This model currently covers a wide array of lines of business within property and casualty. In the future this can be broken down by individual lines of business and then separate models can evolve to cater to specific needs.

ISBN: 9780438298699Subjects--Topical Terms:

1567821
Computer Engineering.
Subjects--Index Terms:

P&C Risk Modelling

Risk Prediction for Property & Casualty Insurance Policies.
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100 1 $a Varadharajan, Sriram. $3 3542740
245 1 0 $a Risk Prediction for Property & Casualty Insurance Policies.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 17 p.
500 $a Source: Masters Abstracts International, Volume: 80-02.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Ge, Tingjian.
502 $a Thesis (M.S.)--University of Massachusetts Lowell, 2018.
506 $a This item must not be added to any third party search indexes.
506 $a This item must not be sold to any third party vendors.
520 $a The core idea behind insurance is spreading the risk among a pool of insurers. The concept of evaluating a risk determines the outcome of the success of an insurance company. With the advent of big data systems, the amount of risk data that gets collected is huge. With proper modelling of these data the risk prediction process can be streamlined and can get close to reality. This will obviously help companies make educated decisions on how to go about insuring a risk. The aim of this project is to build a few models that will help quantify the risk associated with insuring a particular business. The scope of this project was narrowed to data from Property and Casualty business of Insurance . Property and Casualty business (P&C) refers to insuring personal properties like home and cars and also insuring business entities like boilers and machinery, ship building company, and so on. Multiple models are going to be evaluated for this process, and based on the accuracy of the models, decision to choose a model will be made. The models will be built following the basic principles of predictive modelling which encompasses data preprocessing, scaling, training, validation, and testing. The dataset that we propose to use is publicly available at https://catalog.data.gov/dataset/iowa- property-casualty- insurance-premiurns-and-losses. This model currently covers a wide array of lines of business within property and casualty. In the future this can be broken down by individual lines of business and then separate models can evolve to cater to specific needs.
590 $a School code: 0111.
650 4 $a Computer Engineering. $3 1567821
650 4 $a Computer science. $3 523869
653 $a P&C Risk Modelling
653 $a Predictive Modelling P&C
653 $a Risk Modelling Insurance
690 $a 0464
690 $a 0984
710 2 $a University of Massachusetts Lowell. $3 1017839
773 0 $t Masters Abstracts International $g 80-02.
790 $a 0111
791 $a M.S.
792 $a 2018
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10970661