Graduate student placements aim to meet the increasing demand for industry-ready ICT graduates. Employers, get behind these programmes and help bring talent into insurtech. If you are a student considering a data-led career in insurance, hear what these insurtech leaders had to say during TechWeek 2023.
The Auckland ICT Graduate School is a joint initiative by the University of Auckland and the University of Waikato, offers two programmes:
- Postgraduate Certificate in Information Technology (PG Cert), a programme designed for students new to IT
- Master of Information Technology (MInfoTech)
The final semester of the MInfoTech is a compulsory full-time minimum 10 week internship.
Since the introduction of the Masters programme, a number of students have completed their internships in the insurance industry. In 2023, Southern Cross hosted two students who helped with a new product roll out. Ripzem Lepcha, Head of Service Delivery has this to say of the experience:
“Hosting interns from University of Auckland’s MInfoTech programme has been an absolute delight for us. Our interns Yikuan Tu and Joseph Johnson’s enthusiasm, eagerness to learn, and exceptional delivery have exceeded our expectations. Thank you!”
Three placements each year: Mid-year (July to September); summer (November to February) and Semester One (March to June.)
Wednesday 20 March 2024 for interns in Semester 2, 2024 (end of July to October)
Wednesday 14 August 2024 for interns in Summer Semester, 2024 (November to February)
Contact: Deb Crossan, Industry Engagement Manager
The University of Auckland’s Master of Business Analytics programme includes a ten-week applied project.
Students investigate an organisation’s issue that relates to business analytics, developing strategies and recommendations that are compiled into a final report and presentation.
Case study: Risk Profiling and Claims Trend Analysis for an Insurance Company
This project focused on addressing the key concerns of insurance companies regarding the risk profiles of their clients. The project tasked the student team with utilizing data provided by the insurance firm to achieve several objectives. Firstly, they were required to develop risk profiles for both existing and prospective customers. Secondly, the project entailed developing claims trend profiles using existing claims data. These profiles enabled insurance underwriters to establish risk appetites based on claims trends, considering the nature of the claims, premises location, and property characteristics.
To accomplish these objectives, the student team developed several machine learning models, individually and in combination, for classification and further analysis purposes.
Contact: Anastasia Timoshkina, Employer Liaison Manager