Lessons on digital transformation
QBE are deploying some of the most ambitious transformation projects in the insurance industry, increasingly using AI to improve efficiency and accuracy across the insurance process.
In this post, we recap our recent fireside chat with QBE COO, David McMillan, to discuss his learnings about digital transformation in insurance. This talk was moderated by Paul Clark, Partner and Managing Director of Boston Consulting Group. Watch the full-length video below.
What are the key learnings from the QBE digital transformation journey so far?
The business recognised the imminent impact of digital change and adapted early:
There was a recognition by the Executive Committee that data science, data analytics and artificial intelligence would be one of the new frontiers which the insurance industry compete over 3, 5-10 years. There is a real desire to invest and win in the space.
Focus on small targeted wins to gain momentum over large transformational projects:
Over the last 18 months, we have done about 150 data analytics interventions. This has created a real belief at all levels in the organisation that there is real value for the company and a real excitement around data analytics. There’s also a real degree of competition within business units on who is the most progressive.
How should organisations prepare for change?
Establish a vested executive committee:
QBE has an ops and tech committee and pretty much all board and non-executives sit on it to meet every four or so months. It’s a mix of traditional people and those who have seen transformation across financial services and other industries. There is a real focus on data analytics and where it’s going. The debate is not around whether this is important, whether we should invest in it and whether it will transform our industry. The debate is around ‘the how’ and ‘the pace’ of change.
Not everybody will be on board:
There is a mix of people who are excited that we’re getting to the cutting edge, but not everyone arrives at the same point at the same time.
Successful projects bring organisational momentum:
We have a strong vision on where we’re heading and the ability to be more agile doing small things builds confidence. This is helping from a change management perspective.
How will the role of the underwriter change?
The augmentation of data and process is constantly evolving:
I don’t think we’re at the point where we’re really addressing what it means for the underwriter. What does a modern underwriter look like in 2021? What are the skills you need to have and how do you need to harness this technology to do your job properly?
Access to new data will transform commercial underwriting:
One of the things that is really exciting about commercial insurance from a data science perspective is that it’s still relatively virgin territory compared to personal lines. Commercial property is probably 25-30% of our book… and it’s been underwritten in a very traditional way; the way it’s been underwritten for years. Yet you look at it, and there is an abundance of data available on individual commercial properties and the people that own them. That’s really what Richard and the team have tapped into. The wave in our industry is really gathering force.
Have you got examples of how to win hearts and minds across business units?
Get people across the organisation involved:
Getting people involved is always really helpful. A lot of data analytics projects come unstuck because it’s a whole bunch of really smart people in a darkened room with boxes of pizza cutting algorithms. You then get to the point that the algorithms are beautiful but how do they actually get implemented.
Deploy multidisciplinary teams:
Enabling the ultimate users, the technology, operational people and data science to work together is critical in getting projects off the drawing board and into production.
Stakeholder sponsorship makes a huge difference:
We’ve had more success when there has been a supportive pool, very active sponsorship and subject matter expertise input from the business.
What advice would you give to organisations addressing change over the next five years?
Create an environment for technology and data science to flourish:
Machines versus humans? A lot of this is about human capital and talent. We’ve not found it that difficult to recruit people as there’s a degree of understanding within the data science community that insurance is virgin territory and there’s a lot to do. There’s also the understanding that there’s a lot of data to work with. It’s really important – particularly for more traditional insurers to create an environment where data scientists, data people, digital people and technologists want to work.