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Optimisation Lead

2 weeks ago

North Lakes, Australia Auto & General Insurance Full time

The Rec & Sett Optimisation Lead will play a crucial role in driving operational excellence and delivering exceptional service and strong cost outcomes through continuous improvement and optimisation of motor claims processes across the Third Party Recoveries & Settlements team.

This role will be responsible for analysing and identifying areas of improvement in claims handling from initial reporting to the department through to settlement and recovery.

By implementing effective strategies and optimising workflows, the Rec & Sett Optimisation Lead will aim to reduce costs, minimise leakage including fraud detection, enhance third party customer experience and expedite the settlements and recovery process.

The role will collaborate with various stakeholders across the Third Party Recoveries & Settlements team, including consultants, leaders and managers along with other stakeholders from across the group as required.

Auto & General is a data driven company and insights will be fundamental to identification of opportunities. A strong focus on innovative thinking and technologies and automation of processes along with a structured approach to implementation and embedment.

Job Requirement
- 3-5 years Motor Claims experience and/or 3-5 year Process Optimisation experience preferably within a personal insurance context
- Well developed and demonstrated understanding of the legislative, regulatory and compliance obligations to operate in Motor Claims
- Excellent analytical, problem solving and decision making skills
- Exceptional communication (both verbal and written) and interpersonal skills, with the ability to liaise effectively with various stakeholders
- Demonstrated ability to effectively engage, influence and develop others
- High level conceptual, analytical and problem solving skills and the ability to develop innovative solutions.
- High level project management experience i.e. an ability to deliver results to agreed timeframes.