Analytics expert achieves national Award and ARC funding
In an exceptionally good week for Associate Professor Guandong Xu, he has been named one of Australia’s top 10 analytics leaders and awarded $415K ARC funding!
Guandong Xu identified in Australia's Top 10 analytics leaders! Image: UTS
Associate Professor Xu, core member of the Advanced Analytics Institute, is acknowledged in the Top 10 of Australia’s analytics leaders by the Institute of Analytics Professionals of Australia (IAPA),
This inaugural IAPA list (part of its Top 25 Analytics Leaders program) recognises the vital role of analytics professionals helping modern business grow as they champion analytics and insight-driven decision making, incorporating data analytics, data science and analytics-driven insights for better business decisions. Leaders are acknowledged for excellence in four key areas strategy and impact: influence and advocacy; innovation and improvement; and team growth and leadership.
Assoc Prof Xu is the only academic identified in a list which includes peers from PwC, QBE, Westpac, Ferrier Hodgson and the Department of Finance, Services and Innovation, said Annette Slunjski, General Manager of IAPA.
All businesses stand to benefit from analytics-led insights. This inaugural cohort spans all industries from financial services, utilities and telco to eCommerce, education, software, IT and government, with consulting organisations also strongly represented. Analytics leaders are the linchpin to the successful delivery of business value from analytics.
Associate Professor Xu says insight derived from data analytics is increasingly widely received within organisations, with some of our clients and collaborators leading the way.
We have developed a 4-D (Discovery, Design, Development, and Deployment) analytics development methodology for working with them. At a discovery workshop/meeting we deliver clear messages to decision-makers showing how data analytics can benefit and improve their business, how analytics could be applied, and how such insights could be deployed into their daily business. Deep business understanding and domain knowledge is essential to ensure the appropriate design of techniques and the achievement of goals.
He and his team have several industry projects in the FinTech sector leveraging predictive modelling and machine learning. ANZ-OnePath and Colonial First State (CFS) are two of their major industry partners.
For ANZ-OnePath, artificial intelligence is breaking down insurance into its fundamental building blocks – data – and helping design products and experiences which are faster, smarter and better for both customers and providers. This project will be a world-first using AI to optimise and automate the underwriting process for the life insurance industry.
Research collaboration with CFS has been running since 2016 and customer engagement and retention is the key focus of their analytics strategies. AAI developed a customer churn prediction model so CFS can monitor their investor engagement and be aware of their customer’s churn decision at a much early stage for customer service intervention. This industry collaboration with CFS has won two awards: 2016 Consensus Award of BigInsight Innovation in Best Customer Insights and 2017 NSW Marketing Excellence Award in ‘Customer Research’ and ‘Customer Retention’ by the Australian Marketing Institute.
And this collaboration has just been awarded a further $415K from the ARC’s Linkage Projects scheme which provides targeted support for vital research collaborations with industry and community organisations. A three-year project will combine CFS data with UTS expertise in data analytics, machine learning, behavioural economics, and quantitative finance research to better determine ‘longevity risk’ – outliving retirement savings due to increased lifespan.
Insights arising from this project can contribute to safeguarding the future of Australia’s superannuation schemes, and to better financial security for individuals at retirement, said Professor Xu.
Using sophisticated data analytics and machine-learning techniques, combined with economic modelling and quantitative finance we will try to understand the broad characteristics of Australian superannuation investors and their practice from a ‘big data’ perspective. We expect outcomes to identify the key determinants for successful superannuation behaviour to inform decision-making for better superannuation practices and policies.
