Today, data is everywhere. Data allows us to characterise the world around us in wholly new ways and has changed our lives immeasurably. No less is the case in financial services.

    The introduction of cloud storage and increased computing power enables an array of new insights and analytical know-how for those working with data. This is an area of great professional interest and focus for Hymans Robertson as an independent firm specialising in the stewardship of long-term investments. Such capabilities are now being introduced into advisory tools in the UK – placing these at the adviser’s disposal to help generate better outcomes for both the adviser and their clients. The expanse and refinement of this world of data over time has improved its reliability and robustness, providing greater predictability and confidence to support decision-making. 

    It is in this setting we should consider the FCA’s recent retirement income advice review and their ‘portfolio strategy letter for financial advisers’. This has brought further scrutiny over how advisers effectively and impartially analyse their clients’ retirement goals and product choices. It is therefore more important than ever to ensure that advisers can be confident in the methods and frameworks that support their professional judgements. It is here that the world of data needs to come alive. 

    Let us explore the type of data available, and some related tools, that can help both adviser and clients make better informed decisions.

    Creating investment products/ SAAs

    Cloud computing has been transformative in 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. For example, economic projections, such as those from the Hymans Robertson Economic Scenario Service (ESS), traditionally used for complex investment strategy reviews for large pension schemes or insurers, 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 (e.g. correlations and diversification benefits) between them.  

    Additional computing power also enables 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 allows us to take a balanced scorecard approach to selecting an optimal portfolio, considering traditional metrics such as expected return and volatility alongside new outcome focussed metrics like the probability of achieving the client’s goals, or the impact of severe downside risks on their outcomes. This allows us to create more resilient solutions that are designed to meet actual client goals (rather than abstract return targets). This carries great appeal especially for supporting client suitability in decumulation advice cases.

    In the hands of the investment manager, who are designing products for specific customer 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 the advice being given to clients at an individual level too. This has led to increasing demand for solutions from advisers who are currently underserved by the tooling they have access to, particularly in the retirement income planning space.

    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. 

    To assess the financial risks, we can harness the power of the economic projections from the ESS, with all the benefits previously discussed. If the same set of underlying economic projections that was used to create the investment solution is then used to assess an individual personalised plan, this provides a more consistent approach.

    Turning to longevity risk, Club Vita, Hymans Longevity analytics tool, has been building a detailed dataset of pensioner longevity for nearly two decades and is now the leading provider of longevity analytics to insurers and reinsurers. This 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 the ESS (financial) and Club Vita (longevity) data with client fact-find data means sophisticated cashflow modelling of client outcomes and associated risks can be carried out. Combining all this data can help manage risks in a holistic way; providing a detailed understanding of the downside risks the client faces, as well as the potential trade-offs between risk and return they may have to make. For the professional adviser seeking a stronger methodology, this combined approach creates a tailored outlook for the client, enabling better decision-making that is focussed more on outcomes.

    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 also gives an objective way to demonstrate the suitability of the advice given.

    Predicting investing behaviour

    The use of tools and systems, such as those described, nowadays amasses extensive records of user interaction. These preferences and trends can help generate a valuable feedback loop which can be used to adapt the proposition and process as required. They can also highlight where there is friction in the process and evolve the model/system to improve the client experience.

    Design defaults and pre-solved scenarios can be used to guide a client towards sensible solutions e.g. in decumulation solve and present drawdown income at different levels of sustainability. This helps get to the right answer faster and complement the advice given.

    Giving clients a goal/outcome they are happy with can encourage positive behavioural change. This is known as nudge theory and uses the principles of behavioural psychology by providing the tools needed and persuading them why they should make that change, such as increasing their pension contributions to realise their retirement income target. 

    The Guided Outcomes (GOTM) digital engagement portal draws together these forms of behavioural analysis and can automatically set a retirement income goal for each client based on their salary. It then enables them to see online, 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 as red (low chance), amber (moderate chance) and green (high chance) and then, through an online dashboard, gives guidance on suitable actions. It also shows them how they can get back on track and enables all changes to be made through the portal. 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 any changes that are needed. 37% of users started saving more after using GOTM with an average increase in contribution rate of 4.5%. Over double the number of users were on-track after 3 years of GOTM being used.

    A data-driven future

    Good quality data and user-friendly tools are proving highly effective to financial advisers in aiding 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 that clients and their advisers can make better informed decisions, knowing that a very solid foundation of data supports them.

    David McGruther
    Financial risk modelling consultant, Hymans Robertson

    Risk warning: This communication has been approved and issued by Hymans Robertson LLP (HR) on behalf of Hymans Robertson Investment Services LLP (HRIS) and is based upon their understanding of events as at date of publication. It is designed to be a general summary of topical investments issues, it does not constitute investment advice and is not specific to the circumstances of any particular financial advisory firm. Please note where reference is made to legal matters, HRIS is not qualified to provide legal opinions and you may wish to seek independent legal advice. HRIS accepts no liability for errors or omissions.

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