Login | Register

Individual Claims Reserving: Using Machine Learning Methods

Title:

Individual Claims Reserving: Using Machine Learning Methods

Qiu, Dong ORCID: https://orcid.org/0000-0001-6255-2618 (2019) Individual Claims Reserving: Using Machine Learning Methods. Masters thesis, Concordia University.

[thumbnail of Qiu_MSc_S2020.pdf]
Preview
Text (application/pdf)
Qiu_MSc_S2020.pdf - Accepted Version
1MB

Abstract

To date, most methods for loss reserving are still used on aggregate data arranged in a triangular form such as the Chain-Ladder (CL) method and the over-dispersed Poisson (ODP) method. With the booming of machine learning methods and the significant increment of computing power, the loss of information resulting from the aggregation of the individual claims data into accident and development year buckets is no longer justifiable. Machine learning methods like Neural Networks (NN) and Random Forest (RF) are then applied and the results are compared with the traditional methods on both simulated data and real data (aggregate at company level).

Divisions:Concordia University > Faculty of Arts and Science > Mathematics and Statistics
Item Type:Thesis (Masters)
Authors:Qiu, Dong
Institution:Concordia University
Degree Name:M. Sc.
Program:Mathematics
Date:December 2019
Thesis Supervisor(s):Garrido, Jose
ID Code:986258
Deposited By: Dong Qiu
Deposited On:26 Jun 2020 13:33
Last Modified:26 Jun 2020 13:33
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Downloads per month over past year

Research related to the current document (at the CORE website)
- Research related to the current document (at the CORE website)
Back to top Back to top