Risk Profile Development for Trees in Insurance Claims

Location: Sydney, NSW

Duration: 3 months

Project Background

Enviro Frontier has been a pioneer in the management of tree works across Australia since 2013. The use of a Software platform to support and manage tree works and a wide network of tree partners across the country together with over 20 years of tree works management experience provides Enviro Frontier an edge over its competitors in this space. The data captured over the years play a pivotal role in identifying the nature, type and extent of tree works conducted in different parts of the country. This project aims at using this data and generate knowledge vectors to determine the risk that trees bring to the table as part of an insurance claim. This involves observing existing data to determine tree cost parameters. Then overlay this information with external postcodes, weather events, insurance claim data to then determine the risk rating of each tree in the claim. Currently, the business has a good understanding of tree costs, the next steps involve integrating this cost information with other external data points and come up with the risk matrix assessment of trees in the property. The scope also includes development of Power BI dashboards and integrating them with the software platform that Enviro currently uses.

Research to be Conducted

Enviro Frontier has started the process of converting its tree data into essential information. The research to be conducted is as below:

  •  Tree Data information is available and a primitive model to predict cost of tree works is in existence. The next step is to enhance and enrich the model.
  • Look at possible external vectors for integrating weather and suburb information.
  • Explore 3rd party integration to factor in tree impact cost based on additional damage in the event of tree damage (Enviro will provide details for the 3rd party to acquire this information).
  • Analyse the risk of tree works for a insurance industry w.r.to claims.
  • Perform probability and feasibility analysis to develop Tree Risk Matrix for Insurance claims.

Skills Required

If you’re a PhD student and meet some or all the below we want to hear from you. We strongly encourage women, indigenous and disadvantaged candidates to apply:

  • A thorough understanding of data analytics models.
  • A track record/working experience in Data Modelling and Statistical Analysis
  • Good SQL queries and Database programming knowledge.
  • Power BI and Tableau dashboards
  • Programming fundamentals (JavaScript integrations)

Expected Outcomes

  1. Business Intelligence Dashboards for key insights about job time frames, KPI analysis etc
  2. Integration of Dashboards with Software platform (Technical, JavaScript)
  3. Calculate overall risk rating for the tree work to be performed based on the results of the below analysis points.
    • Analysis of Job cost parameters and create a model to predict tree cost based on a set of input information.
    • Use data (confidential, will be shared) from 3rd party to calculate additional impact cost of tree works such as cost of roof repair, cost of fence repair etc.
  4. Gather historic and forecast data related to weather, suburb, tree risks.
  5. Analyse % risk from trees in the context of insurance claims
  6. Integrate results from 3.a) and 3.b) with 4) and 5) to determine risk factor of trees in an insurance claim.

Additional Details

The intern will receive $3,000 per month of the internship, usually in the form of stipend payments.

It is expected that the intern will primarily undertake this research project during regular business hours, spending at least 80% of their time on-site with the industry partner.  The intern will be expected to maintain contact with their academic mentor throughout the internship either through face-to-face or phone meetings as appropriate.

The intern and their academic mentor will have the opportunity to negotiate the project’s scope, milestones and timeline during the project planning stage.

Applications Close

12 February 2020


APR – 1172