Predictive Analytics - the future of Wealth Management?
Predictive Analytics represents a tremendous opportunity which could provide a significant competitive advantage for platform participants, says FNZ’s Jack Doig.
The untapped data opportunity.
Every second of every day, tens of thousands of data points are being collected throughout the investment life-cycle. Data points like life events, asset flow, adviser/investor interactions, allocation trends and user behaviour are all logged by cloud-based platforms.
Combining this data richness with recent advances in statistical computing and AI, plus the increasingly commoditised business of managing and manipulating large data sets, means this data can be leveraged to improve decision making for Platform participants. Today, this presents a tremendous competitive advantage, particularly for more established wealth management platforms; those with more data can use their superior insight to develop better products and features that their competitors cannot.
Advanced analytics give platforms the ability to expose intelligence and make better decisions. Business intelligence (statistical and visualisation tools which explore data) can expose trends and insight about the real-time state of a platform business. For example, advanced analytics can help understand investor trends and investment flows; monitor the impact of decisions; take a deep dive into the end investor to understand things like fee sensitivity; help hold third parties to account; optimise the design of products and services; and expose competitive and sales intelligence. All powerful stuff. So where does predictive analytics come in?
Predictive analytics use historical data to make predictions about a future state by applying machine learning and artificial intelligence. That means platforms can anticipate demand, predict and manage investor or asset attrition, predict life events and expose the best opportunities for business growth. Prescriptive behavioural analytics can recommend action and the expected outcome of that action. For example, investor attrition could be limited by developing a new product. Prescriptive analytics would then work out the expected change in retention from this decision.
Let’s look at an example
Phil oversees a large book of investors. But in a world of increased investor education and competitiveness, he’s concerned about investor attrition to low cost, direct-to-client multi-asset solutions. He doesn’t have great insight into the predictors of a leaver and he’d like to be certain he’s focusing on the right things to keep his highest-value investors.
Predictive models can isolate the variables that predict attrition. Predictive risk analysis can then pinpoint investors at the highest risk of leaving, as well as investors with the highest predicted life-time value (including those most likely to refer others). Phil can then optimize the effort he spends with clients, and minimize high-value attrition in his business.
As platform data assets grow, coupled with investment in advanced analytics, models will become more accurate. This means a higher quality, more personalised experience for investors, advisers will deliver a better service, and established investment platform providers will be able to help funds develop products based on insight which their competitors simply don’t have. Predictive analytics has the potential to bring a wealth of opportunity for the financial services industry.