Today, data is everywhere.

    Data allows us to characterise the world around us in wholly new ways, and has changed our lives immeasurably.

    The same is true for the use of data in financial services.

    The introduction of cloud storage and increased computing power allows for new insights and analytical know-how for those working with data.

    As an independent firm specialising the stewardship of long-term investments, this is an area of great professional interest and focus for us at Hymans Robertson.

    Such capabilities are now being introduced into advisory tools in the UK.

    As this world of data has expanded and been refined over time, it has become more reliable and robust, providing greater predictability and confidence to support decision-making.

    It's against this backdrop we should consider the FCA’s Dear CEO letter to advice firms issued at the start of the year, and the regulator's Assessing Suitability Review 2, which the FCA hopes to resume next year.

    The regulator has signalled its concerns around unsuitable advice, and this has brought further scrutiny over how advisers effectively and impartially analyse their clients’ retirement goals and product recommendations.

    So it's more important than ever to make sure you're confident in the methods and frameworks that support your professional judgements. It is here that the world of data needs to come alive.

    Here we look at the type of data available, and some related tools, that can help advisers and clients make better informed decisions. 

    A 'balanced scorecard' approach

    Cloud computing has transformed the design of investment solutions.

    Analysis that was previously available only to large financial services institutions can now be brought to the retail market in cost-effective ways.

    To take an example from our own business, economic projections, such as our Economic Scenario Service (ESS) have traditionally used for complex investment strategy reviews for large pension schemes or insurers.

    Yet these can now be used at a completely different scale to better understand the risk and return of potential portfolios.

    Enhanced computing power and access to new datasets also allows us to characterise even more asset classes.

    This gives a more granular view of the specific characteristics of each asset class and the interactions (for example, the correlations and diversification benefits) between them.

    Additional computing power also allows us to position an individual’s goals in the context of the economic environment, by combining cashflow modelling with an understanding of expected portfolio behaviour.

    In practical terms, we can now assess a much larger pool of potential portfolios, with tens of thousands of candidate portfolios being the norm.

    This lets us to take a 'balanced scorecard' approach to selecting an optimal portfolio, considering traditional metrics such as expected return and volatility alongside more outcome-focused measures like the probability of achieving the client’s goals, or the impact of severe downside risks on their outcomes.

    In turn, this allows us to create more resilient solutions designed to meet actual client goals, rather than abstract return targets.

    This particularly works well when supporting client suitability in retirement advice.

    In the hands of the investment manager, who is designing products for specific client types, these sources of data and analyses combine to produce solutions that provide a better client experience with more focus on client outcomes.  

    Risk management 

    There is increasing scrutiny from regulators wanting advisers to demonstrate the suitability of their advice at an individual client level. 

    A hot topic at the moment is sustainability of retirement income, which has historically been difficult to assess due to the complex combination of financial and longevity risks.

    Once again, advances in technology now allow institutional analysis to be applied in the retail sector.

    On the financial risks, if the same set of underlying economic projections used to create the investment solution is then used to assess an individual personalised plan, this provides a more consistent approach.

    On longevity risk, we have been building a detailed dataset of pensioner longevity for nearly two decades, providing these analytics to insurers and reinsurers.

    The data gives a rich insight into the longevity of the UK retirement population at a very detailed level – down to individual postcodes.

    This level of data analysis can now be applied to the retail market, giving advisers an objective measure of their clients’ individual longevity prospects.

    Combining all this data with the client fact-find and cashflow modelling provides a detailed understanding of the downside risks the client faces, as well as the potential trade-offs between risk and return. 

    Greater access to data, and the insights that can be gained, can be used to manage risk and select the most appropriate solution for a client, managing both sustainability and drawdown.

    Not only does this improve outcomes for clients, but it gives an objective way to demonstrate the suitability of advice.

    Predicting investing behaviour

    The use of these kinds of tools and systems can help map preferences and trends which can improve the client experience over time.

    Helping clients towards a goal or outcome they are happy to work towards can encourage positive behavioural change.

    This kind of 'nudge theory' can help persuade clients why they should make a a particular change, such as increasing their pension contributions to realise their retirement income target.

    One way we've put this in practice is through our Guided Outcomes portal, which can be used to automatically set a retirement goal for each client based on their salary. 

    Clients can then see in a clear and simple format where they are in terms of their pensions savings, showing their target and if they are on or off track. 

    It flags the chances of achieving their goal using a red, amber or green traffic light system, and gives guidance on suitable actions.

    The system then provides ongoing monitoring of each client against their retirement goals, sending a pro-active message with helpful guidance if they fall off track again and suggesting any changes that are needed.

    And we have seen it yield results.

    Some 37 per cent of users started saving more after using the Guided Outcomes portal, with an average increase in contribution rate of 4.5 per cent. 

    What's more, over double the number of users were on track for their retirement goals after three years of using the portal. 

    A data-driven future

    Good quality data and user-friendly tools are already supporting advisers in decision making and will no doubt become increasingly important with further regulatory scrutiny ahead.

    Creating client-specific investment portfolios, managing the risk of these portfolios, and predicting future investing behaviour all mean clients and their advisers can make better informed decisions, knowing that a solid foundation of data supports them.

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