A five-year partnership between UTS’s Advanced Analytics Institute and Colonial First State (CFS) could mean tailored customer service, optimised financial advice and improved understanding of customers’ needs.
Australians have more than $2.5 trillion (yes, trillion!) in government-held superannuation assets. It makes our system the fourth largest pool of retirement savings in the world. While the ebbs and flows of the market have been scrutinised for decades, the rise of machine learning and big data is now uncovering new opportunities and insights into super which means better information and more savings for Australian workers.
A big pot of money. When it comes to retirement that’s what most of us want. But, until now, it hasn’t been what most retirees get.
A five-year partnership between UTS’s Advanced Analytics Institute (AAi) and Colonial First State (CFS), however, could mean tailored customer service, optimised financial advice and improved understanding of customers’ needs. All of which results in more adequate and long-lasting self-funded retirement.
And it’s all thanks to machine learning and artificial intelligence.
Associate Professor and Leader of AAi’s Data Science and Machine Intelligence Lab Guandong Xu explains: “This collaboration crosses a spectrum of activities including contract research projects, Australian Research Council (ARC) Linkage Project applications, internships and the Industry Doctorate Program (IDP), and in-house corporate training which prepares researchers for careers outside of academia.
“It’s allowing us to contribute to society, for our students to get a taste of real-world problem solving, and we can bring value to CFS through knowledge transformation and technology innovation, and upskilling their staff. Plus, we increase our research outputs. It’s a win-win situation.”
National Manager, Analytics and Business Intelligence at CFS James Brownlow agrees. “A collaboration with UTS – an organisation that shares our vision for innovation – is the ideal partner to help us access and learn from some of the best in the field of analytics, all while providing an opportunity for students to gain real-world industry experience at CFS.”
Analytics experts at AAi have so far been engaged to analyse data and deliver tools (or predictive models) that decipher how Australian workers invest their super guarantee, which customers are likely to change super funds, and even personality indicators to improve customer service.
For example, a customer who displays particular personality traits might be more likely to make sub-optimal investment decisions than other types of customers. This understanding allows CFS to help deliver the right assistance to the right person at the right time.
By combing through huge amounts of data from customer interactions, or by analysing how customers react in a volatile market, these research projects can improve CFS’s understanding of its customers, services and products.
“For CFS,” says James, “we want to use deep data analytics to help our customers respond to new opportunities. It’s a chance to provide real outcomes for Australia such as improving savings in the superannuation space.
“UTS has a market-leading research capability with a focus on big data, data sciences and data analytics. Utilising their scale and research expertise, we are now delivering the information and tools to inform and shape products, marketing and distribution efforts as well as provide a better understanding of how people engage with their finances and what actions we can take to improve it.”
In addition to these projects, the partnership sees UTS students placed in internships with CFS, as well as IDP candidates from CFS spending time with AAi on a weekly basis.
In fact, James himself is currently undertaking his PhD in analytics through the IDP at UTS, with Guandong as his supervisor.
“I wanted to do research that mattered within Australia,” says James, “As the demographic of Australia changes and drives an increase in the number of retired workers, using analytics to deliver better experiences and savings is important.”
With an anticipated completion date of 2020, James is delving deeper into how new technologies and approaches might improve retirement outcomes by looking at how investors’ engagement with their super can be quantified with data mining, instead of relying on anecdotal evidence.
Future-focused projects combining the best of industry and academic expertise have seen the work delivered by AAi and CFS win the 2016 BigInsights Data Innovation Award for Best Customer Insights, as well as becoming national finalists and NSW winners in the 2017 Australian Marketing Institute’s Awards for Marketing Excellence for both Customer Research Insights and Customer Retention.
It’s a chance to provide real outcomes for Australia such as improving savings in the superannuation space.
National Manager, Analytics and Business Intelligence at CFS
On top of this, researchers in AAi, including IDP students, have had three papers accepted for the 2018 Pacific-Asia Conference on Knowledge Discovery and Data Mining and the International Conference on Database Systems for Advanced Applications (two top international conferences in data mining and knowledge discovery) and a best application paper for the 2017 International Conference on Behavioural, Economic, and Socio-Cultural Computing. It shows the partnership really goes both ways.
“Our collaboration has gained industry recognition and academic recognition,” says Guandong, “so both sides are happy!”
Now in discussions for their fourth project of the partnership, AAi and CFS have also submitted an application for an ARC Linkage Project grant to focus specifically on applying deep analytics to reshape Australian superannuation practice.
Meanwhile, technology is improving and allowing for enormous progress in the fields of big data, deep learning and artificial intelligence in wealth management.
“We are very experienced in predictive analytics,” says Guandong, “which uses big data, machine learning and artificial intelligence to find underlying correlations between different customer attributes and outcomes, but the next step will be to have models like this working more deeply, on multiple levels, to drill down to find exactly the causality of certain decisions or actions.”
For example, this might mean predicting which customers are likely to react in an adverse way to a volatile market and help in providing education and support.
“Before,” says James, “researchers were more reliant on anecdotal or self-reported data to develop their understanding. But new technology like machine learning is allowing Australian workers to be more connected with their superannuation. That’s important as superannuation is likely to be the one of the largest investments that Australians will hold in their lifetimes.”