Presentation

As Australia’s leading life insurance specialist, TAL’s team of underwriters handle thousands of customer applications through our direct and retail (adviser/broker) channels. The sheer volume of applications and complexity of each has meant the quality assurance (QA) team is only able to review 1-2% of cases, which is in-line with industry norms

Our Incubator team, tasked with leveraging Artificial Intelligence and other emerging technologies to step-change our business, had the hypothesis that machine learning could help – by applying a machine learning algorithm, we could identify inconsistent cases to target with our QA.

Our tool, ‘WunderWriter’, is unique in the industry. It enables us to audit 100% of cases and prioritise inconsistent cases for the QA team to focus on. This has step-changed our QA as the team are no longer ‘wasting’ around 80% of their time reviewing cases that have no issues – effectively delivering a team productivity improvement of 500% (from 20% to 100%).

Every case in our direct channel is now being automatically reviewed by ‘WunderWriter’, versus the 1-2% previously. This means Our QA team can focus on priority cases, based on inconsistencies identified, contributing to improved customer experience and more effective risk assessment, each of which directly impacts on business performance.

The nature of our in-house Incubator team – being a focused cross-functional team including data scientists, data engineers, developers, customer proposition leads and operations analysts – meant that the team was able to build the tool quickly and robustly. It is now fully deployed across the direct QA team.

The reason behind

Uniqueness

Implementation

Project organization

Results

Investments