Analysis of telematics data using machine learning techniques

Summary

Usuage-based auto insurance (UBI) represents a significant evolution in automobile insurance pricing over traditional pricing as it can provide more personalised premiums based on individual driving mileage and behaviour, thereby incentivize drivers to safety and less.  As more and more cars will install telematics, it becomes a real challenge to analyse this Big Data.  This project derives predictive models that will differentiate driving behaviours as measured from the voluminous telematics data through machine learning algorithms.

Supervisor(s)

Associate Professor Jennifer Chan

Research Location

School of Mathematics and Statistics

Program Type

Masters/PHD

Synopsis

Usage based insurance is growing fast in the decade by the great technological advancement in telematics to capture driving behaviour information which facilitate more accurate premium calculation.  Current predictive models use the same rating formula for all drivers.  This project will develop predictive models, using mixtures of generalised linear models to allow different rating formulae for different risky drivers.  The model will integrate machine learning techniques such as Random Forest for variable selection and cross-validation technique for model assessment and updating.  This project requires some basic programming techniques for model implementation.

Additional Information

HDR Inherent Requirements

In addition to the academic requirements set out in the Science Postgraduate Handbook, you may be required to satisfy a number of inherent requirements to complete this degree. Example of inherent requirement may include:

- Confidential disclosure and registration of a disability that may hinder your performance in your degree;
- Confidential disclosure of a pre-existing or current medical condition that may hinder your performance in your degree (e.g. heart disease, pace-maker, significant immune suppression, diabetes, vertigo, etc.);
- Ability to perform independently and/or with minimal supervision;
- Ability to undertake certain physical tasks (e.g. heavy lifting);
- Ability to undertake observatory, sensory and communication tasks;
- Ability to spend time at remote sites (e.g. One Tree Island, Narrabri and Camden);
- Ability to work in confined spaces or at heights;
- Ability to operate heavy machinery (e.g. farming equipment);
- Hold or acquire an Australian driver’s licence;
- Hold a current scuba diving license;
- Hold a current Working with Children Check;
- Meet initial and ongoing immunisation requirements (e.g. Q-Fever, Vaccinia virus, Hepatitis, etc.)

You must consult with your nominated supervisor regarding any identified inherent requirements before completing your application.

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Keywords

telematics data, Machine learning, mixture models, generalised linear models, cross-validation

Opportunity ID

The opportunity ID for this research opportunity is: 2444

Other opportunities with Associate Professor Jennifer Chan