Greater personalisation in retirement planning and advice could help boost projected annual income for Australian retirees by up to 50%

Rachel White

A new study from Vanguard shows that depending on the complexity of their financial circumstances, Australian retirees could increase their projected annual incomes between 3-51% by incorporating personal and household data into their retirement income strategies, compared to relying on a minimum withdrawal strategy.^[1][2]

The study findings have implications for retirement advice. While individuals have access to the full scope of their personal information, they may lack the financial acumen to optimise their retirement income according to their financial circumstances. Professional advisers, however, could significantly increase expected retirement outcomes using a greater extent of additional personal information, given their retirement planning expertise. Superannuation funds can also play a role, particularly for members who cannot or do not want to engage a financial adviser, but they are currently limited in their ability to access and use personal information at scale.

The study, which aimed to quantify the financial impacts of greater use of personal information on retirement outcomes, modelled three retirement income strategies that incorporated different levels of personal and household data:

1. Minimum withdrawal strategy: Regardless of their personal financial circumstance, a retiree withdraws only the legislated minimum drawdowns from their retirement income account.
2. Superfund best efforts strategy: Guidance from a hypothetical superannuation fund that has limited visibility of a member’s financial picture and does not accurately factor in information such as partner status, age pension eligibility, or assets held outside superannuation.
3. Full information strategy: An optimised strategy that incorporates comprehensive individual and household information.

The study found that the more personal and household data that goes into a retirement income strategy, the better the outcomes, highlighting the financial benefits of considering personal information in retirement income planning. The study shows that that as a person’s financial situation increases in complexity, so too does the potential value a full information strategy bring.

For example, in the study:

• As the complexity in financial situations increased, the full information strategy increased projected annual retirement incomes by 3-51% compared to the minimum withdrawal strategy.
• As the complexity in financial situations increased, the superfunds best efforts strategy increased projected annual retirement incomes by 3-34%, compared to the minimum withdrawal strategy.

Figure 1 below shows four fictional personas to illustrate the impact of incorporating personal information into retirement income strategies.^[3]

Figure 1: Superfund best efforts can improve retirement outcomes relative to basic strategies but cannot maximise potential financial value for retirees with complex financial circumstances compared to full information strategies. See the Notes section for the study methodology and assumptions.

Source: Vanguard calculations in AUD as at 1 July 2024 based on VCMM. All percentages have been rounded up to the nearest whole number for simplicity and clarity.
Chart notes: The results are presented in terms of annual “certainty equivalent income” so that the overall value of each retirement income strategy is able to be compared. “Certainty equivalent income” is a theoretical, guaranteed annual income that the household would receive for life, which provides the same level of satisfaction and security as implementing a strategy that could have different levels of income in each year. This is assessed based on the preferences of the personas.
Important: The projections or other information generated by the Vanguard Capital Markets Model (VCMM) regarding the likelihood of various investment outcomes are hypothetical in nature, do not reflect actual investment results, and are not guarantees of future results. Distribution of return outcomes from the VCMM are derived from 2,000 simulations for each modelled asset class in AUD. Simulations are as of 1 July 2024. Results from the model may vary with each use and over time.

Vanguard Australia’s Head of Financial Adviser Services Rachel White says the study underscores the importance of personal information in optimising retirement income strategies.

“The study shows that the more personal and household information incorporated into a retirement income strategy, the greater the potential financial benefits. This underscores the critical role personalised financial advice plays in improving retirement outcomes, particularly as financial complexity increases,” said Ms White.

“Comprehensive personalised advice, which combines financial acumen with a person’s full financial picture, is undoubtedly the gold standard. Unfortunately the cost of comprehensive personalised advice and a shortage of advisers make it hard for people to access this level of advice.”

Ms White says given Australia’s growing retirement needs and the potential value advisers can bring to retirement planning, there is a need for settings that encourage a greater supply of advisers in the market.

“Not everyone, however, needs that level of advice, and our research shows that even the consideration of limited personal information and broad assumptions – as in the case of the hypothetical superfunds best efforts strategy – can already lead to improved outcomes. And that’s a great reminder to make sure you’re engaging with your super fund and making the most of the information and services they provide,” said Ms White.

“But the study shows that for people with more complex financial situations, there is a substantial gap in potential retirement outcomes compared to when the full information strategy is used. We think more can be done to close this gap.”

Ms White said Vanguard advocates for policy-settings and industry practices that allows accessibility to the broad spectrum of guidance and advice to improve financial and retirement outcomes for Australians – from comprehensive personalised advice, to simple personalised guidance, to general information and education.

“The benefits of a more personalised approach are clear from a financial standpoint, but the emotional and behavioural benefits that come from having a personalised plan, and the confidence that brings, are also important.

“According to Vanguard’s upcoming How Australia Retires 2025 report, retirees who had a good idea or clear understanding of what actions they needed to take were three times more likely to feel highly confident in their ability to fund their desired lifestyle in retirement, and 65% more likely to have a positive outlook on retirement,” said Ms White.

Noting the draft legislation proposed to deliver the next tranche of the Federal Government’s financial advice reforms, Ms White says the task to bridge the Australia’s advice gap remained urgent.

“With millions of Australians expected to draw down on their super in the coming decade, there’s no time to waste in progressing reforms that uphold critical consumer protections and enable Australians to safely and easily access personalised advice to suit their individual needs.

“Vanguard is pleased to contribute this study and our upcoming How Australia Retires research to this important national conversation. And we continue to consult on legislative reform and industry initiatives that support Australians to enjoy the financial security and satisfaction they have worked hard to achieve.”

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Methodology: This study uses fictional Australian retiree personas to simulate household spending patterns under various financial scenarios. It includes Age Pension entitlements, personal finances, partner status, and uncertainties around longevity, market returns and inflation. Taxes and housing wealth are excluded, assumes retirees hold tax-free superannuation pensions. Personas aim to balance spending more with financial certainty, balance avoiding running out of money with not leaving an inheritance, and ensure surviving partners maintain a two-thirds of the couple’s living standard. These examples are illustrative only and are based on the factors and assumptions stated. They should not be taken to contain or provide an estimate or forecast of retirement outcomes.

Important: The projections and other information generated by the VCMM regarding the likelihood of various investment outcomes are hypothetical in nature, do not reflect actual investment results, and are not guarantees of future results. Distribution of return outcomes from VCMM are derived from 10,000 simulations for each modeled asset class. Simulations as of July 1, 2024. Results from the model may vary with each use and over time. For more information see the Chart Notes. The VCMM projections are based on a statistical analysis of historical data. Future returns may behave differently from the historical patterns captured in the VCMM. More importantly, the VCMM may be underestimating extreme negative scenarios unobserved in the historical period on which the model estimation is based.The Vanguard Capital Markets Model® is a proprietary financial simulation tool developed and maintained by Vanguard’s primary investment research and advice teams. The model forecasts distributions of future returns for a wide array of broad asset classes. Those asset classes include U.S. and international equity markets, several maturities of the U.S. Treasury and corporate fixed income markets, international fixed income markets, U.S. money markets, commodities, and certain alternative investment strategies. The theoretical and empirical foundation for the Vanguard Capital Markets Model is that the returns of various asset classes reflect the compensation investors require for bearing different types of systematic risk (beta). At the core of the model are estimates of the dynamic statistical relationship between risk factors and asset returns, obtained from statistical analysis based on available monthly financial and economic data from as early as 1960. Using a system of estimated equations, the model then applies a Monte Carlo simulation method to project the estimated interrelationships among risk factors and asset classes as well as uncertainty and randomness over time. The model generates a large set of simulated outcomes for each asset class over several time horizons. Forecasts are obtained by computing measures of central tendency in these simulations. Results produced by the tool will vary with each use and over time.