BlackRock

Introductory and general comments

BlackRock welcomes the opportunity to address, and comment on, the issues raised by this Discussion Paper and to contribute to the thinking of the ESAs on the development of automation in financial advice. BlackRock is one of the world’s leading asset management firms. We manage assets on behalf of institutional and individual clients worldwide, across equity, fixed income, liquidity, real estate, alternatives, and multi-asset strategies. Our client base includes pension plans, endowments, foundations, charities, official institutions, insurers and other financial institutions, as well as individuals around the world. We respond to the Discussion Paper with the experience we have of distributing our products to end consumers through third party intermediaries such as bank, insurance companies and third party direct to consumer platforms. We also comment from our experience of working with automated advisors primarily in the US. BlackRock generally does not engage in direct to consumer sales.

Assessing consumer needs

Automated advice constitutes one of a number of selection tools made available by financial intermediaries, such as financial advisors or discretionary portfolio managers, to assist their clients. By taking advantage of models and investment tools, financial intermediaries are better able to work with their clients to develop portfolios that improve their clients’ ability to achieve their investment goals in a more effective manner. These tools can also be repurposed for direct to consumer offerings. It is therefore essential to place the outcomes consumers are seeking at the heart of the review carried out by the ESAs. Consumers will only benefit from technological innovations if savings solutions are made relevant to them. Financial intermediaries and product providers are increasingly drawing lessons from behavioral finance to design simple savings solutions that explain the benefits of investment to consumers who may be unfamiliar with saving other than through bank deposits. In the future, we believe many savers will want to access savings solutions through digital platforms or advisory services with the backup option of speaking to an advisor via telephone and other forms support that can help validate their choices, rather than meeting with advisors for face-to-face advice. We recommend that future legislation and regulatory guidance looking at the development of automated advice fully assesses the complexity of consumer behaviour to avoid unintended outcomes. We share some of the findings of our own consumer research in our response to the Discussion Paper.

BlackRock’s Investor Pulse

In our response, we refer to consumer research that we conducted with end consumers regarding their attitudes on saving and investing: BlackRock’s Global Investor Pulse 2015 (“Investor Pulse”). This survey was carried out using an online methodology in 20 countries. The total global sample size was over 31,000 people, making it one of the largest surveys of its kind in the world. Eight countries (including Belgium, France, Germany, Italy, Netherlands, Spain, Sweden and the UK) were surveyed in Europe with a sample of 13,000 adults. The fieldwork was conducted during August and September 2015. A summary of the findings is available at https://www.blackrock.com/uk/individual/literature/brochure/global-investor-pulse-uk.pdf.
We also refer to Investor Pulse data on the attitudes of US consumers. While there are a number of structural and regulatory differences between the US and many EU markets, insights into US consumer behaviour raise a number of useful analogies when considering consumer behaviour in European markets.

Competing business models

In looking at the development of automated advice in the securities sector in the US, we observe a number of different business models. We encourage the ESAs to look at these alternative models in depth when assessing the relative benefits and risks of automated advice, as business model, client profiling and service proposition are likely to vary from one model to another. Many of the comments from the ESAs on the benefits and risks from automated advice models focus on new entrants to the banking, insurance and securities markets. It is equally important to focus on the significant investment being made by existing advisory networks to integrate new technologies such as automated advice to service their existing clients more effectively.

We observe three main types of market participant developing automated advice:
• Innovative start-ups, characterised by the development of new automated advice models built on new proprietary models but which face the hurdle of acquiring a new client base from scratch and have less familiarity with existing financial services legislation.
• Existing market participants with direct to consumer platforms offering to increase their service offering, with the advantage of an established brand.
• Existing bank advisory networks looking to retain advisory clients and/or service existing clients, particularly in the mass retail and mass affluent sectors, in a more cost effective and consumer-focussed way than under existing banking models. BlackRock acquired FutureAdvisor in 2015 to assist bank advisory networks who are developing these solutions – for further details see our answer to question 4.

Competing service models

We also draw the ESAs’ attention to the fact that there is great variety in the various models of automated advice on the market. These models are designed to meet the diverse needs for advice from different client types, ranging from the simple to the more complex. For example, in some models, consumers may be asked just a few questions for a relatively straightforward portfolio solution in comparison to more complex models, which may involve bringing together a full profile of the client and providing advice on his/her aggregated assets.
We highlight a number of relevant characteristics of automated advisory services that can lead to improved outcomes when compared to existing advisory models:

2.1 Automated advice models can give firms a way of engaging with a new client base as well as engaging better with their existing client base. Our Investor Pulse data shows a lack of engagement with financial advisors by many, though by no means all, segments of consumers. Many people simply don’t know how or where to start with investing, and online models may be less intimidating than approaching a financial advisor directly, especially given concerns that having a conversation with a financial advisor in person could entail a lot of technical jargon. Our Investor Pulse data also shows that many consumers are concerned that they don’t have sufficient investible assets to be worthwhile for a traditional advisor. The ability to offer a transparent service to cost-conscious consumers is seen as an advantage.

2.2 Some automated advice models allow data regarding different accounts held by consumers to be aggregated in one place. This allows the automated advice tool to develop a more holistic view of an individual’s assets and liabilities, enabling it to determine how best to construct a portfolio to meet the individual’s stated needs. Addressing data protection concerns regarding the sharing of what often constitutes sensitive personal data on an individual between different account providers is a core factor in encouraging the future development of automated advice models.

2.3 Automated advice models allow for greater consistency of implementation, as the use of model portfolios and algorithms can contribute to minimising the risk of human error from the advice process. This may reduce liability concerns regarding the mis-selling of financial products by advisors, although we note that many traditional advisory networks have already invested significantly in technology and processes to provide a more consistent approach to their clients.

Automated advice models are still best termed as hybrid models as they still incorporate the financial expertise of human portfolio management expertise into the asset allocation model. It is essential to understand the governance and oversight that advisors put in place to manage this process in the interests of their clients.

2.4 Some have suggested that the development of the consistent advice process inherent in automated advice models create a risk of “herding in investment decisions.” This view conflates a standardised investment process with standardised outputs. In fact, automated advice models offer greater opportunity for more personalised outputs by taking into account each individual’s personal circumstances and have the potential to reduce consumer biases to sell in a falling market. We discuss this further in our response to risk 12.
Our principal experience is in the US market and we see the most data in the US market. One of the most comprehensive studies of the US is in the Tracxn report on Robo Advisors from February 2016. This report breaks down robo-advisors into four different types of service model:

• Full Stack platforms which provide an end-to end, algorithm-based automated online platform,
• Advisor assisted platforms which provide algorithm –based money management along with traditional human advisory services,
• Recommendation platforms which recommend an investment portfolio based on the user’s financial situation and references but without access to an execution platform,
• Enablers which enable transitional advisors to offer algorithm-based investment advice and manage their clients better.

With higher consumer awareness of automated advice models such as robo-advice in US, our Investor Pulse survey surveyed over 4,000 US investors and asked them why they were turning to “robo-advice models.” We asked some specific questions and the findings showed that ease of access and greater alignment with consumers needs were seen as the primary drivers of the shift towards robo-advice for many respondents, especially among younger generations.

Chart – US consumer interest in robo-advice is generally high and peaks among younger people




Chart - Reasons for interest in robo-advice

Our principal experience is in the US market and we see the most data in the US market. One of the most comprehensive studies of the US is in the Tracxn report on Robo Advisors from February 2016. This report breaks down robo-advisors into four different types of service model:

• Full Stack platforms which provide an end-to end, algorithm-based automated online platform,
• Advisor assisted platforms which provide algorithm –based money management along with traditional human advisory services,
• Recommendation platforms which recommend an investment portfolio based on the user’s financial situation and references but without access to an execution platform,
• Enablers which enable transitional advisors to offer algorithm-based investment advice and manage their clients better.

With higher consumer awareness of automated advice models such as robo-advice in US, our Investor Pulse survey surveyed over 4,000 US investors and asked them why they were turning to “robo-advice models.” We asked some specific questions and the findings showed that ease of access and greater alignment with consumers needs were seen as the primary drivers of the shift towards robo-advice for many respondents, especially among younger generations.

Chart – US consumer interest in robo-advice is generally high and peaks among younger people (please see supplemental data)

[Investor Pulse 2015: BASE: US (n=4,213) Mass Affluent (n=1,000) Mass Retail (n=3,213)]

Chart - Reasons for interest in robo-advice (please see supplemental data)

Investor Pulse 2015 – US question - Why would you be interested in this type of service?
BASE: US (n=1,727) Mass Affluent (n=471)








Chart - Reasons for interest in robo-advice
BlackRock does not currently offer automated financial advice tools in the EU.

BlackRock acquired the business of FutureAdvisor in the United States in 2015 with the goal of helping our partners, such as banks and insurance companies, to build better portfolios for their clients. The aim is to empower financial institutions to better serve their clients by providing an offering that combines FutureAdvisor’s technology-enabled advice capabilities and BlackRock’s investment and risk-management solutions FutureAdvisor’s technology-enabled advice capabilities include: personalised advice that can look holistically across clients’ pension accounts; tax-efficient portfolio management; mobile and web applications; online account enrolment; and multi-custodian support While our initial focus will be in the United States, over time, we expect to expand the platform to assist intermediary clients in the EU, such as banks and insurers, to build automated advice models to service their existing client base. We do not currently have the intention of developing our own direct to consumer proposition in the EU.
We believe that there are a number of potential barriers that need to be considered as part of the review of automated advice, including:

• Industry and regulatory barriers preventing consumers from sharing their data with providers of automated advice.
• Inconsistent regulatory standards for the provision of advice, which do not reflect consumer needs.

We touch on each of these in turn:

5.1 Industry and regulatory barriers preventing consumers sharing their data with providers of automated advice

Automated advice solutions can fully realise their potential if they are able to access a full set of information regarding consumers’ assets and liabilities. These details are rarely held in one place and it can be time consuming for consumers to release this data and update the advisor on a real time basis as circumstances change. The development of account aggregation tools provide a significant benefit; however, this development is hindered by a number of conflicting requirements such as data protection constraints, particularly in respect of the sharing of sensitive personal data and restrictive terms and conditions from account providers that prevent individuals from releasing account details to trusted third parties. We support the development of robust industry standards which permit account aggregation to trusted third parties who commit to meet high standards of confidentiality and cybersecurity. We set out in our response to Question 23 a number of suggestions regarding the development of a Digital ID, which could help alleviate these problems by putting the individual in charge of his/her data in a secure manner that permits safe data sharing. The UK’s Tax Incentivised Savings Association (TISA) carried out a recent survey of consumer attitudes in the UK to the introduction of a Digital ID which showed that so far only a third of those surveyed were comfortable with the idea. To respond to these concerns we recommend that a Digital ID is designed as a voluntary rather than a mandatory process. As these initiatives come to fruition and consumers can see how they work and the protections which are built in we would then expect these confidence levels to rise. This emphasises the importance of addressing consumer confidence when implementing new consumer-facing technologies.

A further area where consumers would reap the rewards of industry cooperation is that of account transfers where in many cases paper-based, manual process inhibit the easy transfer and reregistration of account holdings. This discourages consumers from taking action to manage their finances more effectively. The development of industry standards and service levels would benefit consumers and encourage the development of more effective account aggregation.

5.2 Inconsistent regulatory standards for the provision of advice which do not reflect consumer needs

The ESAs note in the introduction to the Discussion Paper that “ ‘Advice’ is used in the common meaning of the word.” In practice, the current regulatory framework distinguishes between multiple types of advice and guidance and imposes a number of different standards on the providers of advice. This has resulted in a disconnect in common parlance and the regulatory requirements which advisors must meet in specific sectors e.g., investment advice under MiFID or simplified advice in the UK. In order to achieve the goal of ensuring that the majority of the population is equipped to save effectively, the process of delivering the help they require needs to be simplified, consumer-centred and consistently applied.

We believe there needs to be agreement on common terminology to describe financial advice rather than a myriad of confusing definitions of advice (pension advice, mortgage advice, investment advice, simplified advice, guidance) with different standards and liabilities. Rather there should be a focus on an individual’s key financial needs and how to achieve them. We must find a way to break down barriers to advice and encourage people to become investors. In an era of greater individual financial responsibility, finding a new way to talk about financial services and advice is crucial. In particular, we draw the ESAs attention to the Financial Advice Market Review (“FAMR”) currently being conducted in the UK by Her Majesty’s Treasury and the Financial Conduct Authority (”FCA”), which is considering these issues. From our experience, many of the issues raised in FAMR apply to a greater or lesser degree in most other EU member states. For more information, please see BlackRock’s response to FAMR in which we explore many of the issues in further detail, including the need for consistent guidance standards from regulators to help people manage their often conflicting financial short-term and long-term priorities more effectively. Our response is available at https://www.blackrock.com/corporate/en-fi/literature/publication/hmt-fca-financial-advice-market-review-call-for-input-221215.pdf.

Automated advice propositions do not claim to provide all the solutions consumers that need, but they can play an increasingly important role in providing more consumer-focussed outcomes. Rather than just adapting the regulation of advice to take into account the characteristics of automated advice models, we recommend that the ESAs and other European and national policy makers consider both definitions of advice and the regulation of advice to ensure that the advice available to consumers can meet their diverse needs.
We comment on the various benefits in turn:

6.1. Consumer access

We believe that the most fundamental policy benefit of automated advice is the opportunity for a wider range of consumers to have access to advice through automated tools.

As responsibility for long-term income provision increasingly passes from the State and employers to individuals, access to simple, consistent help is more important than ever before, especially as individuals are faced with an increasing choice of products. Many people find finance baffling and need support to demystify the savings process. BlackRock’s Investor Pulse highlights the level of ‘consumer disengagement’ among key sectors of the population and has found that many people – where they save at all – choose overwhelmingly to save in cash and cash-like investments, rather than in other options like shares. Saving only in such a low-risk, low-return asset class is likely to hinder people from achieving their long-term financial goals.

The development of automated advice is one among a number of core initiatives to improve consumer engagement.
Our Investor Pulse survey shows that even where people have clear advice needs, most are unwilling to use traditional forms of advice. For example, only around 17% of those surveyed in both UK and in Germany and 14% of those in the Netherlands currently use the services of an advisor. The ability to offer more accessible and cost effective advice models though automated advice models could help to reverse this trend.

Chart – Use of professional financial advisors in selected European countries
[Investor Pulse 2015: A financial advisor could include an advisor at a bank, insurance company, a broker or an independent advisor]




This level of disengagement is particularly prevalent in jurisdictions that have responded to the issue of potential bias in advice through the prohibition of the payment of commissions from product manufacturers to intermediaries, such as the UK and the Netherlands. In these jurisdictions, the need to pay up-front for advice has discouraged many respondents to our Investor Pulse survey from seeking advice. We asked people in the UK why they had stopped taking financial advice. In total, one quarter (26%) of those surveyed who previously used advice had stopped taking advice because it had become too expensive. The number of formerly advised clients citing cost as the reason for no longer taking financial advice is higher in the UK than it is in other European markets. It is difficult to pin-point this precisely to the market impact of the RDR. However, consumer responses in the UK are in line with other EU markets such as the Netherlands that have recently undertaken regulatory reforms to their advice markets and where there is a higher percentage of mass retail investors that regard themselves as previous users of financial advice. In other European markets (Spain, Italy, France, Belgium, Sweden), mass retail savers are more likely to have a current advisor relationship. In other words, among the middle and lower wealth groups, a larger portion of investors have dropped out of the advice market in in recent years.
Instead of taking advice, people are increasingly relying on online services to support self-directed decision-making. Current regulatory practice assumes that individuals have well-defined investment goals and clearly articulated financial needs. In reality, self-directed individuals find it difficult to manage numerous overlapping and ill-defined objectives. [See AFM: Self-directed investors: important insights December 2015].Our Investor Pulse survey found that people tend to compartmentalise their objectives and need help to think holistically; otherwise saving – and particularly investing – falls down their list of priorities, in particular behind buying a home. This is a particular problem for millennials who find it difficult to visualise themselves as retired and who face a perpetual tug-of-war between debt repayment and saving. Savers need a new framework for advice and guidance to ensure that they have the support to help them invest constructively. Increased saving is unlikely to translate into greater investment without expanding access to help consumers manage their conflicting objectives. Individuals need engaging, consistent and standardised guidance to allow them to combine their sources of potential income well in advance of retirement and make informed investment decisions. Consumers are likely to derive the most benefit from digital advice models if they are offered in combination with a simplified guidance regime setting out a safe harbour within which governments, regulators, industry and the third sector can provide a consistent set of core messages to help consumers work through these conflicting priorities.

As noted in our answer to question 3, our survey of US consumers shows that ability to choose to obtain financial advice in a faster, simpler way is seen as a benefit to many, consumers, especially younger consumers. This may be particularly important for those consumers who are less willing or less comfortable talking directly to an advisor in the first instance.

Underlying the general trends on the use of financial advice noted above are a number of significant differences in the use of advice by different sectors of the population. This is reflected in the chart below which presents advice usage by wealth, gender and age and shows a number of significant discrepancies. By providing a simple interface, automated advice models can address some aspects of consumer needs, particularly for those who choose self-directed investing, who are more reluctant to consult an advisor or who are no longer eligible for an advisor given changes in regulation. This process needs to be as engaging as possible and drive effective rather than sub-optimal decision making.

Chart: Breakdown of use of financial advisors in the UK (please see supplemental data)
[Investor Pulse 2015]

6.2 Outcome oriented investment and cost effective advice

A key aspect of much automated advice is the ability to deliver advice on asset allocation in a more accessible and cost-effective way. This is separate from the cost of the underlying building blocks. Rather than focussing on investment into low-cost products as the primary outcome of automated advice models, we recommend a focus on the ability to offer accessible outcome-oriented investing.

We acknowledge that as part of the process of offering outcome-based investment solutions, many providers of automated advice include lower cost products such as Exchange-Traded Funds (ETFs) or index funds as the component building blocks in their asset allocation models. These products give retail investors greater ease of access to investment opportunities that might have otherwise been unavailable to them, or available only at a higher cost.

Access to outcome-oriented product solutions complements the development of automated advice. The ongoing proposals for a Pan-European Personal Pension (PEPP) with default investment solutions that can be easily accessed through online portals are a first step in offering the potential for outcome-oriented investing at a European level, though a number of challenges remain. See BlackRock response to EIOPA Consultation Paper on the creation of a standardised Pan-European Personal Pension product at https://www.blackrock.com/corporate/en-gb/literature/publication/standardised-pan-european-personal-pension-product-eiopa-10052015.pdf


By way of comparison, in the US, the US Pension Protect Act of 2006 (“PPA”) is a good example of a fundamental change that achieved the objectives of facilitating and encouraging the establishment and contribution to pension plans and Individual Retirement Accounts while focusing on outcome-oriented investing. Through the PPA, Congress and the Department of Labor enacted legislation and implemented regulations designed to make it simple to increase savings and improve investment content of those savings. The PPA provided for automatic enrolment, automatic escalation and “qualified default investment alternatives” (“QDIA”), which were intended to collectively improve retirement outcomes. In particular, by adopting asset allocation products as QDIAs, the PPA addressed the problem that the average investor, with little knowledge of finance and investments, had overwhelming allocations to company stock and conservative fixed income investments and did not change allocations over time. The DoL recognised that participants need help in allocating their savings across asset classes to achieve a better outcome and, by establishing asset allocation products as a safe harbour, it provided that help. The PPA has been successful because it made it simpler and easier to save for retirement and supported investment options – QDIAs – that were considered appropriate to achieve retirement goals.

[Sources:
- Pension Protection Act, Pub. L. No. 109-280, 120 Stat. 780 (2006).
-Notice of Proposed Rulemaking - Default Investment Alternatives under Participant Directed Individual Account Plans, 71 Fed. Reg. 56806, 56807 (Sept. 27, 2006).
71 Fed. Reg. at 56806-07; See also William J. Wiatrowski, “401(k) Plans Move Away from Employer Stock as Investment Vehicle,” Bureau of Labor Statistics, Monthly Labor Review (November 2008), available at http://www.bls.gov/opub/mlr/2008/11/art1full.pdf.
- Pension Protection Act, Pub. L. No. 109-280, § 624(a), 120 Stat. at 980. See e.g., U.S. Bureau of Labor Statistics, Automatic enrollment, employer match rates, and employee compensation in 401(k) plans, Monthly Labor Review, at 3 (May 2015), available at http://www.bls.gov/opub/mlr/2015/article/pdf/automatic-enrollment-employer-match-rates-and-employee-compensation-in-401k-plans.pdf. ]



We note that the forthcoming implementation of a number of EU consumer protection legislation (e.g., MiFID 2, PRIIPs and IDD) will provide consumers with a far more complete and standardised set of cost disclosures than currently required. These enhanced cost disclosures standards will have the effect of requiring all firms to justify the value for money they provide for the services they provide. Providers of automated advice will need to meet these requirements and compete on the basis of the overall value of the services they provide to consumers, just like any other provider of advice.

6.3 Quality of service

Given the variety of automated advice models, neither will there be one common standard as to quality of service. The assessment of whether an advice models is the extent to which it has clearly defined the outcomes it wishes to achieve, disclosed these to the consumer and then delivered these outcomes in a consistent way.

The need to offer a consistent advice process is important for all advisors and it is important to note that consistent advice processes can be delivered without automation of the advice model, though technology can increasingly facilitate a more efficient process. The quality of consumer outcomes is only as good as the data collected on the consumer’s profile and their needs which underlines the need in all cases for a robust and engaging suitability assessment. In automated advice a balance needs to be achieved between asking too many questions so that the consumer fails to complete the process and asking too few so as to compromise the quality of the advice.

One of the benefits of financial advice, whether automated or not, is the ability to assist consumers to achieve long term investment goals by correcting consumer biases, such as holding excessive amounts of cash or the tendency to buy high and sell low, by explaining the benefits of regular savings in smoothing out the effects of short term market volatility. Good service models, whether face-to-face or automated, will engage with consumers in times of market volatility and recommend appropriate courses of action based on an individual’s specific circumstances. Technology can offer advisors the ability to communicate more effectively with their clients, which is particularly valuable for the younger client demographics who are comfortable with digital media as a communication tool.

We also agree with the ESAs that the ability to retain and have easy access to client recommendations is a useful design feature. This helps both in terms of client servicing and in providing legal certainty in the event of any dispute, as it minimises issues with record keeping and access to documents. Electronic document storage is of course available in other servicing models, but the design of automated advice services can facilitate its provision to consumers.





















6.2 Outcome oriented investment and cost effective advice

A key aspect of much automated advice is the ability to deliver advice on asset allocation in a more accessible and cost-effective way. This is separate from the cost of the underlying building blocks. Rather than focussing on investment into low-cost products as the primary outcome of automated advice models, we recommend a focus on the ability to offer accessible outcome-oriented investing.

We acknowledge that as part of the process of offering outcome-based investment solutions, many providers of automated advice include lower cost products such as Exchange-Traded Funds (ETFs) or index funds as the component building blocks in their asset allocation models. These products give retail investors greater ease of access to investment opportunities that might have otherwise been unavailable to them, or available only at a higher cost.

Access to outcome-oriented product solutions complements the development of automated advice. The ongoing proposals for a Pan-European Personal Pension (PEPP) with default investment solutions that can be easily accessed through online portals are a first step in offering the potential for outcome-oriented investing at a European level, though a number of challenges remain. See BlackRock response to EIOPA Consultation Paper on the creation of a standardised Pan-European Personal Pension product at https://www.blackrock.com/corporate/en-gb/literature/publication/standardised-pan-european-personal-pension-product-eiopa-10052015.pdf


By way of comparison, in the US, the US Pension Protect Act of 2006 (“PPA”) is a good example of a fundamental change that achieved the objectives of facilitating and encouraging the establishment and contribution to pension plans and Individual Retirement Accounts while focusing on outcome-oriented investing. Through the PPA, Congress and the Department of Labor enacted legislation and implemented regulations designed to make it simple to increase savings and improve investment content of those savings. The PPA provided for automatic enrolment, automatic escalation and “qualified default investment alternatives” (“QDIA”), which were intended to collectively improve retirement outcomes. In particular, by adopting asset allocation products as QDIAs, the PPA addressed the problem that the average investor, with little knowledge of finance and investments, had overwhelming allocations to company stock and conservative fixed income investments and did not change allocations over time. The DoL recognised that participants need help in allocating their savings across asset classes to achieve a better outcome and, by establishing asset allocation products as a safe harbour, it provided that help. The PPA has been successful because it made it simpler and easier to save for retirement and supported investment options – QDIAs – that were considered appropriate to achieve retirement goals.

We note that the forthcoming implementation of a number of EU consumer protection legislation (e.g., MiFID 2, PRIIPs and IDD) will provide consumers with a far more complete and standardised set of cost disclosures than currently required. These enhanced cost disclosures standards will have the effect of requiring all firms to justify the value for money they provide for the services they provide. Providers of automated advice will need to meet these requirements and compete on the basis of the overall value of the services they provide to consumers, just like any other provider of advice.

6.3 Quality of service

Given the variety of automated advice models, neither will there be one common standard as to quality of service. The assessment of whether an advice models is the extent to which it has clearly defined the outcomes it wishes to achieve, disclosed these to the consumer and then delivered these outcomes in a consistent way.

The need to offer a consistent advice process is important for all advisors and it is important to note that consistent advice processes can be delivered without automation of the advice model, though technology can increasingly facilitate a more efficient process. The quality of consumer outcomes is only as good as the data collected on the consumer’s profile and their needs which underlines the need in all cases for a robust and engaging suitability assessment. In automated advice a balance needs to be achieved between asking too many questions so that the consumer fails to complete the process and asking too few so as to compromise the quality of the advice.

One of the benefits of financial advice, whether automated or not, is the ability to assist consumers to achieve long term investment goals by correcting consumer biases, such as holding excessive amounts of cash or the tendency to buy high and sell low, by explaining the benefits of regular savings in smoothing out the effects of short term market volatility. Good service models, whether face-to-face or automated, will engage with consumers in times of market volatility and recommend appropriate courses of action based on an individual’s specific circumstances. Technology can offer advisors the ability to communicate more effectively with their clients, which is particularly valuable for the younger client demographics who are comfortable with digital media as a communication tool.

We also agree with the ESAs that the ability to retain and have easy access to client recommendations is a useful design feature. This helps both in terms of client servicing and in providing legal certainty in the event of any dispute, as it minimises issues with record keeping and access to documents. Electronic document storage is of course available in other servicing models, but the design of automated advice services can facilitate its provision to consumers.
We comment on several additional benefits in turn:

7.1 Holistic view of financial worth of investment objectives

The aim of a number of automated services to bring all of a client’s holdings together on a single platform. This can allow consumers to see their assets and liabilities set out clearly and help them to prioritise conflicting liabilities. We set out in further detail in our response to question 5.1.

7.2 Opportunity to address the advice gap

We note from Investor Pulse that as household wealth and income increases, so does the use of professional financial advice.

For example in the UK:
o 35% of those with investable assets of greater than £100,000 claim to use an advisor, compared to only 7% of those with investable assets of less than £10,000.
o 31% of those with a household income in excess of £50,000 use an advisor compared to 19% of those earning an income of £30,000-£40,000 or £40,000-£50,000.

NET: Investable assets Base Yes, use now No, although I have in the past No, never used
Up to £10,000 1754 7% 25% 69%
£10,001 to £20,000 418 16% 29% 55%
£20,001 to £50,000 663 21% 36% 43%
£50,001 to £100,000 424 27% 37% 37%
£100,000 upwards 741 35% 34% 31%

Household income (before tax) Base Yes, use now No, although I have in the past No, never used
Up to £20,000 p/a 1244 10% 23% 67%
£20,001 - £30,000 p/a 827 13% 30% 57%
£30,001 - £40,000 p/a 650 19% 34% 47%
£40,001 - £50,000 p/a 477 19% 33% 48%
£50,001+ p/a 802 31% 36% 33%

Income and wealth also influence where people turn to for advice and information. Among the mass affluent advised – whom we define in the UK as those with a household wealth threshold of in excess £100,000 in liquid assets – over half (53%) use an independent advisor. Less than a quarter of these people (23%) use bank advice.
In contrast, 34% of people who use an advisor in the mass retail segment use a bank advisor and just 37% use an independent advisor. Personal bankers may provide advice on basic advised sales but they are unlikely to offer wider investment advice. This means that in reality, even where people in the mass retail market are seeking advice, it is more likely to cover a narrower range of advice options, which results in a narrower range of savings and investments.

What type of professional financial advisor do you currently use? Base of 694 respondents in the UK
Total Mass Affluent Mass Retail
High Street bank advisor 29% 23% 34%
Private bank/ wealth manager/ asset manager 19% 19% 18%
Insurance company 4% 3% 5%
Independent- works for themself/ a firm of IFAs 43% 53% 37%
Other 5% 2% 7%

Chart: advice use varies by type of advice depending on personal wealth levels
What type of professional financial advisor do you currently use? If you use more than one please think of your main advisor.
All advised UK Mass affluent Mass retail
High street bank advisor 29% 23% 34%
Independent advisor 43% 53% 37%

7.3 Opportunity to encourage consumers to invest in non-cash instruments

Our Investor Pulse survey shows a significant reliance on cash savings across the EU, though there are national variations, such as the relative popularity of insurance-linked investments in France and bond investing in Italy, that drive cash holdings below the European average. There are a number of possible reasons for this trend, some of which reflect cultural attitudes to savings as opposed to investing. For example, in Germany, respondents to our Investor Pulse survey preferred saving in cash instruments and property to investing in markets. While the majority of respondents acknowledged that they typically held nearly more than twice as much cash as they needed in their portfolios (on average cash represented 63% of portfolio holdings as compared to an identified need of 33%), respondents were reluctant to invest for a variety of concerns. These concerns include fears over the loss in value of the initial capital invested and concerns as to how market shocks could affect capital values. The UK shows even an even higher proportion of people holding cash at 67% of portfolio holdings, reflecting similar concerns over investing. Among mass market retail clients, even those who do take advice rely on high street bank advice, which could partially explain the sale of cash products rather than investments. This may be due to the fact that banks post-RDR have been reluctant to advise on the sale of investment products to individuals, although there have been a number of recent indications that UK banks are now preparing to offer automated advice services. [See Financial Times, 19th February 2016 at http://www.ft.com/cms/s/0/48e5f1a0-d631-11e5-829b-8564e7528e54.html] Greater access to automated advice models for mass market retail consumers could help to lead to more balanced asset allocations.

Chart: Asset allocation as a percentage of overall portfolio, by country. (see supplemental data chart. [Investor Pulse 2015]
In general, these differences reflect the different product types and disclosure requirements in each of these sectors. We recommend a cross sectoral approach in key areas such as disclosure standards and cost transparency so that the consumer receives the same level of information regardless of the product or service they buy.
Please see our response to question 7.
We comment on the various benefits in turn:

10.1 Quality of service

As noted in our response to question 6, the ability to process customer data in a consistent manner, while achievable in a non-automated setting, may be easier to accomplish with automation. This automation can assist firms in providing more standardised service, though this is largely dependent on the precise service model adopted by the advisor. As we have noted there are many different advice models and we would not recommend setting a standard level of quality of service without factoring in the different outcomes intended by advisors. To the extent that data collection and investment outputs are standardised, this could reduce the risk of advisor recommendations being inappropriate for the client. We note that existing advisory networks are increasingly turning to technology to drive more consistent outcomes for their clients so this phenomenon is not limited to new entrants to the market.

10.2 Cost to institutions

We emphasise that the cost of developing an automated advice service requires significant set up and maintenance costs, though this has not deterred a number of start-up ventures (both within established firms and as independents) from entering the market. Once a critical mass of clients has been reached, the cost of servicing each client is likely to be lower than under existing face-to-face advisor models. We note the comment from the ESAs that automated advice services do not require the employment of human advisors but believe this does not reflect the diversity of business models which are developing and point to our comments below on the ongoing ability to access human advisors. Automated advice services we have worked with provide the opportunity for the consumer to contact a person with queries or to discuss investment decisions. In addition, the design of the algorithm and investment decisions, such as fund selection, are overseen by individual staff members and investment committees. Automated advisors, as regulated entities, must have robust compliance, data security and related programs and employ staff for these functions, similar to that of non-automated advisors. On an ongoing basis digital advisors will need to continue to invest in both the ongoing development and maintenance of the model(s) and in cybersecurity with specific focus on the protection of client data stored with the advisor. Automated advice permits a more effective use of human advisors, but does not exclude human intervention or rely solely on algorithms without human oversight.

10.3 Size of the potential client base

We agree that automated tools offer the opportunity for financial institutions to service their existing client base in a more effective way, especially clients, and also may be a way to provide advisory services to client segments which previously were not cost effective to service. For example, existing advisors may be better equipped to service client with smaller amounts to invest but also to offer a digital service to those existing clients who are more comfortable dealing primarily on a digital basis.
Many existing bank advisory networks are investing significant amounts in technology and supporting individual advisors by developing central asset allocation models. While there may continue to be face-to-face client interaction, the mechanics of the advice process increasingly rely on the use of model portfolios so even in a partially automated model benefits will accrue to financial institutions in terms of efficiency of client service.
In general differences will reflect the different product types and disclosure requirements applicable to each of these sectors. Any new regulation or supervisory standards should aim to provide consistent regulatory outcomes regardless of the product or service the consumer is buying.
As the development of automated advice is still in an early stage it is too early to provide definitive examples.
Many of the risks identified by the ESAs arise out of consumer biases, which have been identified in traditional advice models. Increasingly, all advice models, whether automated or face-to-face, need to factor in lessons from behavioural finance. It is important to consider how key information is presented and test how consumers react to various presentations in order to deliver the most favourable consumer outcomes. We comment on the likelihood of the various risks the ESA have identified in turn:

R.1 Unsuitable decision making as a result of lack of information, and reduced opportunity to fill the gaps or seek clarification

We believe that this risk is present in all advice models where the quality of advice is dependent on the quality of information collected on the client. Consumer willingness to read and act upon disclosure documents depends on how key disclosure information, for example in product information such as UCITS KIIDs or PRIIPs KIDs, is presented. We note that different consumers react more or less favourably to different presentations of information. Well-designed automated advice models can offer consumers different presentations of the same information, allowing consumers view the information in a way which is more or less compelling them. The availability of different products benefits consumers, as they can choose a disclosure model. Well-designed interfaces can offer the consumer the ability to stop and query individual terms or descriptions as they move through the advice process in a way which meets their needs. As many models at in their early stages of development and are evolving, it is still too soon to evaluate the overall quality of products available and regulatory intervention could be best be served with examples of good practice to encourage higher standards.

As with any review of disclosure standards, it is important that the consumer is directed to the core information they need to make a decision. We draw the ESAs’ attention to the significant consumer testing which has taken place in designing disclosure documents under UCITS and PRIIPs and we recommend that these insights are applied across other financial products.

R.2 Unsuitable advice as a result of not being aware of how the information they input is used by the automated tool

The ESAs note the existing MiFID client assessment requirements and we agree that this provides a valuable standard for ensuring that this particular risk is properly mitigated. A prerequisite of any consumer questionnaire is adequate consumer testing across a variety of consumers to check understanding and so avoid potential misunderstanding and ambiguities. Our expectation is that as the majority of automated advice models target mass market retail clients, continuous consumer testing of the customer interface has to form an integral part of the design and ongoing testing of the system. From a business perspective, firms who do not have an engaging interface will see lower completion rates than the firms who have an intuitive consumer-tested approach and will be at a competitive disadvantage. Firms will need to be able to justify to their supervisors that the right level of consumer testing has been carried out to ensure adequate consumer understanding and to meet ongoing regulatory requirements.

R.3 Unsuitable advice as a result of biases in the tool that they are not aware of

We note the reference in the Discussion Paper to the MiFID requirements that require effective management of conflicts of interests to avoid the type of risks raised in this section. We believe that the enhanced cost disclosures required under MiFID and PRIIPs will also contribute to mitigating this risk. We recommend that supervisors and policy makers consider the application of similar standards to financial products not captured by these requirements.

R.4 Limited or unclear information about the extent to which the tool produces recommendations tailored to them

We fully support the emphasis on transparency and disclosure to the consumer of what they can expect given the variety of business models which are being developed in the market. As noted in our response to question 5, lack of clarity as to the standards expected from firms in the provision of different forms of advice or guidance could give rise to this type of risk. As consumers often perceive advice as help provided by a professional in making a decision, there is the potential for consumer misunderstanding when regulatory definitions of different types of advice vary and are subject to different standards. It is important that the consumer understands what they should and should not expect from a specific advice model.

R.5 Lack of understanding of who is providing advice because of the fragmented nature of the advice process

Consumers need to have certainty that the entity they contract for advice is responsible to them for failure to deliver the contracted service. This is not a situation unique to automated advice models and is reflected in the responsibility of regulated firms where they outsource the provision of various functions to third party providers – for example under the MiFID or UCITS directives or the requirements of many NCAs such as the UK SYSC rules. Provided there is clarity as to who has overall responsibility for the provision of advice or connected services such as custody of assets, the fact that the underlying process is broken down into a number of separate components can provide benefits to consumers.

For example, in recent years we have seen a growth in the development of model portfolios, which are collections of possible investment portfolios, comprising a wide range of strategies (e.g., growth, low volatility, inflation protection, income), product types (e.g., exchange traded funds, unit trusts) and risk profiles. The models are rebalanced or updated by the model provider on a periodic basis, but the model provider does not generally purchase and sell the securities contained in the model on behalf of any investor. The models are developed based on what an asset manager believes would be an appropriate or attractive strategy for some sub-set of investors without targeting any particular investor. Thus, the model provider is not “recommending” any model to an investor. It is making the model portfolios available as a product and/or service to a financial intermediary, who, in turn, may evaluate and recommend the models for specific clients. We believe that the advisor remains responsible to the end consumer for the selection and use of a particular model. Any “output” from a tool should not be characterised as advice from a third party model provider direct to a particular individual investor but should be subject to review and oversight by the advisor even if this conducted electronically before it is provided to the consumer.

In addition, as technology improves, asset managers are increasingly offering financial intermediaries sophisticated investment tools to assist them in taking advantage of the asset manager’s models. The financial intermediary obtains information regarding a client’s investment objective, risk profile, time horizon, total savings, etc. and uses that information with the tool to generate a potential portfolio for a client, using the model that best fits the client’s information. The tools and technology are accompanied by detailed financial information on the investment funds or other securities included in the model, the strategy and risks. The financial intermediary generally shares with their client a streamlined version of the “output” generated by use of the tool, including information regarding the proposed model portfolio and actual portfolio, once an investment is made. The “output” will generally include investment funds (e.g., exchange traded funds or unit trusts) managed by the model/tool provider and is likely to identify the asset manager that provides the model and/or tool.

Model portfolios and investment tools are viewed as a portfolio management or investment service provided by an asset manager to a financial intermediary. As model providers, asset managers do not have a contract with a client and are unlikely to know the identity of the end client. A financial intermediary, and not the asset manager, is responsible for determining whether a client would benefit from an investment program based on models, determining the appropriate model provider (which may be BlackRock or another asset manager), selecting a particular model and determining whether to follow the model in its entirety or to make modifications based on the intermediary’s judgment or the client’s preference. The financial intermediary, and not the asset manager, will have responsibility for execution of securities purchase and sale instructions, including rebalancing.

R.6 Lack of awareness that personal data inputted in the tool is used in ways they do not envisage

In our mind, this risk exists with the application of existing data protection legislation. Firms do, however, need to acknowledge consumers’ concerns about misuse of their personal data. Our comments on the development of a Digital ID in our response to question 22 reflect the need to develop new approaches to use of consumer data in a way which facilitates the operation of innovative tools while still putting the consumer in charge of their own data. As noted in our response to question 5.1 it is key to address data protection concerns from consumers.

R.7 Unsuitable decisions because of limitation or assumptions within the tool

The outcome from the use of model portfolios are only as good as the quality of data inputted into the model. All models contain potential for bias and it is important to understand the model provider’s investment philosophy and ensure that appropriate disclosure of key elements of the model to ensure that consumer do not take unsuitable decisions.

R.8 Unsuitable decisions because there are errors in the tool

We emphasise the need for investment professionals to be closely involved in the design and oversight of the financial advice tool to ensure that the algorithm delivers the expected outcome. The models used can only be as good as the humans who programme, maintain and update them. This highlights the need for appropriate governance and ongoing testing of the model by investment and risk professionals. Testing and control of the product is not a duty which can be delegated to compliance or internal audit teams whose role is to challenge and advise those responsible for the design and operation of the model on an ongoing basis.

R.9 Consumer detriment because the financial advice tool they use if hacked and the underlying algorithm is manipulated

As with any financial services offering, we acknowledge the importance of ensuring the maintenance of robust cybersecurity and fraud protection tools. We agree with the need for a strong governance and oversight function to ensure to ensure the ongoing resiliency of the model.

R.10 Unsuitable decisions because the tool facilitates them to move through too quickly through the process

As noted in our response to risk 7 the quality of data inputted is core to the success of any model. It is also important to understand that different models offer a wide variety of different outcomes. An engaging client interface designed around proper consumer testing does not necessarily mean that the tool is unsuitable.

R.11 Lack of motivation to act on advice given by automated tools where such tools do not facilitate an end-to-end process

It is important that automated tools be able to provide specific investment alternatives, not just categories of potential investments, in order to allow individuals to connect information about asset allocation to actual products. Without this information, investing will be made significantly more time consuming and difficult for savers. In a number of EU member states individuals are increasingly having to take responsibility for planning their own retirement, it is important that individuals have sufficient knowledge and information to manage their investments on their own. Without specific investment options, an investor at most learns that he/she should diversify his/her portfolio among various asset classes in certain percentages but is not provided sufficient information to actually follow through with the investment decision. In order to provide retirement investors with enough information to improve their investment allocations, automated tools should reference specific investment alternatives. This allows the individual to build a complete retirement solution.

One of the common elements underlying potential risks 10 and 11 is failure to obtain the adequate data from consumers for the tool to be effective and engaging. As with our comment on risk 1, our research has shown the need for detailed consumer testing of the consumer interface to ensure appropriate engagement and understanding from consumers as to what the automated advice tool will deliver to them. As with our comment to risk 13 below, we note that many consumers may want the opportunity to access a human advisor to verify or validate their understanding of the investment process. Firms will need to consider a variety of access points e.g. by telephone, chat or e-mail to ensure continued consumer engagement.

R.12 “Herding risk” if automated advice tools based in similar algorithms result in many consumers taking the same actions in the relation to the same types of products /services

We believe the risk of “herding” is minimal given the wide variety of models of automated advice using diverse models and intended to deliver different outcomes to consumers and at this stage of development we do not see the likelihood of one automated advisor achieving a dominant position across all principal European markets, given the wide difference in consumers expectations and attitudes to saving and investment.

Even where many consumers rely on a single provider of automated advice, the advantage of the algorithms used by many automated advisors is that they respond to the individual circumstances of consumers. By setting a few simple parameters such as the consumer’s investment horizon, risk tolerance, and goals and by taking into account an individual’s existing asset allocation, age and other factors (such as tax considerations), the algorithm will respond in a diversity of ways to the same market event based on the individual’s needs. For example, many consumers have long and medium terms goals and are better served by retaining long term holdings rather than selling in terms of market disturbance. The algorithm in automated advice models will seek to rebalance from time to time to ensure that the portfolio remains within the consumers desired risk tolerance. Even in this scenario, we do not see evidence of potential herding given the diverse needs of different investors. For example, one of the key components of many automated advice models is tax planning. The benefits of the algorithms in automated advice models is the ability to adjust trading patterns to reflect an individual’s tax liabilities so that portfolio disposals are not recommended if they would incur an unnecessary tax liability. Individuals are subject to very different tax treatments and have different access to tax-incentivised savings plans from one EU jurisdiction to another (e.g., UK Individual Savings Accounts (ISAs) or German Riester Plans). This is another example of how trading models will inevitably vary from country to country.

Accordingly, the more automated advice models are able to aggregate data on the consumer’s total holdings and tax positions, the more likely it is that the models will deliver a personalised response to market developments. In fact, automated advice tools can have the effect of reducing consumers’ bias towards buying high and selling low by reducing the incidence of selling in a downturn. By reducing the impact of consumer bias and taking consumers’ investment horizons into account in a standardised way, automated advice models are more likely to have a counter-cyclical effect rather than a procyclical effect on markets by mitigating rather than amplifying herding risk.

R.13 Lack of opportunity to access any human financial advice

Our review of various automated advice models shows that most consumers require the ability to talk to or contact an individual about their finances, even with an automated solution. Consequently, automated advice models generally offer access to professionals to help guide consumers though the financial decision-making process. Automated advice models typically offer consumers the ability to contact an individual via telephone, chat, email or face-to-face contact technologies such as Skype. We believe that there are few consumers who would be wholly comfortable with a purely automated service. To succeed, business models in this space need to offer consumers the ability to verify and validate the choices they have made with a human professional. While potentially less cost-effective for the provider, these may be a necessary feature for some consumers.
No, apart from general risks which apply to all consumers investing in markets which apply regardless of the channel they use.
Potential risks may vary due to differences in dealing with the treatment of consumers in sectoral different legislation in respect of issues such as cost transparency, disclosure standards and management of conflicts of interest.
Please see our comments to our response in question 14 on how many of these risks have already been identified and are managed under legislation such as MiFID.
A number of the risk identified are common to the provision of existing advisory services. Existing participants in the market will be well aware of these risks and it is important that supervisors work with new entrants to the markets so that they fully appreciate the need to design the right governance and control structures to minimise these risks from set up. The FCA’s Project Innovate is an example of how national supervisors can encourage debate with new entrants to the market on how best to structure their business to meet these concerns. We consider each of the potential risks in turn:

R.14 Exposure to litigation and subsequent reputational risk due to faulty automation

We agree that there need to be strong internal governance and controls over the operation and output of the algorithms. We do not agree with the implication that compliance and internal audit teams should be the primary line of defence against faulty automation. Rather, we note the importance of putting in place robust investment oversight and risk management over the design and operation of both the algorithm and the selection and ongoing review of products included in the investment offering. As mentioned in our response to Risk 3, we also note the importance of robust conflict of interest policies to avoid the risk of conflicts or potential inducements undermining the integrity of the model. While compliance and internal audit teams play an important role, it is critical to have independent and robust oversight and risk management functions both in the development of the automation model and a robust testing and verification process on an ongoing basis.

As mentioned in our response to risk 9, we note the importance of ensuring the maintenance of robust cybersecurity and fraud protection tools in the financial services sector and note that these concerns are not unique to automated advice models.

R.15 Overuse of human advisors as an alternative means to obtain advice so as to supplement the automated advice on the product/service

As we have noted earlier, we believe that relatively few consumers will be confident using an entirely automated advice model and so automated advice models need to provide access to human advisors to supplement the automated model either to clarify unclear terms or to allow consumers to validate their decisions. While potentially less cost-effective for the provider, these may be a necessary feature for some consumers.

Firms can respond in a number of ways to high levels of use of human advisors. For example, they can treat this contact as a means of valuable feedback on ambiguities in the automated interface (see our comments on the need for extensive consumer testing in response to Risk 2) or alternatively, if the customers have more extensive needs than can be met by the basic, model they could seek to offer a premium pricing model for customers seeking a more personalised service.

R.16 Legal disputes as to unclear allocation of liability

These risks are not unique to the automated advice sector and are already a key focus of supervisory oversight by national competent authorities. Please see our comments in respect of risk 5 on existing regulatory requirements to oversee and take responsibility for the activities of delegates in the securities sector (e.g., MiFID, AIFMD and UCITS). Existing market players are well aware of existing regulatory requirements and supervisors can work with new entrants to ensure they are fully aware of their responsibilities in this area.
As noted in our response to question 18, the risks will vary depending on the business model and type of services which are intended to be provided using automated advice tools.
Differences may arise as a result of inconsistencies in the relevant sectoral legislation.
Please see our comments in response to Question 18.
In discussions with intermediaries around the EU, we have observed a definite trend towards increased client segmentation around net wealth/investable assets, with clients below a certain threshold being encouraged to move away from advisory services to execution only platforms and/or discretionary management services. We believe this trend will continue to dominate European distribution models, especially with the costs of meeting the enhanced suitability and liability standards under MiFID 2. Consumers using execution-only platforms are likely to be poorly equipped to make optimal investment allocation decisions, trade more frequently than necessary, diversify insufficiently and display a tendency to sell profitable shares too quickly and retain loss-making ones in the portfolio too long . With the increasing growth of self-directed investment advice, investors need additional tools that can support them to make more efficient investment decisions.
We agree that there have been increasing consumer concerns about the use of data protection issues. Consumers will only realise the full benefits of automated advice services when providers of automated advice are able to aggregate information about consumers to form a holistic view of an individual’s assets and risk exposure. We support initiatives which allow the aggregation of consumer data in a way that reassures consumers that data protection concerns are fully taken into account. In particular, we draw the ESA’s attention to the initiative by the UK’s Tax Incentivised Savings Association (TISA) to develop a Digital ID for UK consumers as a practical example of the way in which the apparently conflicting drivers of data protection and data aggregation could be reconciled.

The concept of the Digital Identity is to provide consumers with a single point of entry to a range of different financial service providers such as insurers, banks, building societies and asset managers, making it much easier for people to manage their assets in one place, with all anti-money laundering and know-your-client procedures completed once, up front. An initiative like this would reduce complexity: people would no longer need their identity to be certified by their bank, producing two or three copies of utility bills every time they open a new savings vehicle. It would also mean that individuals would be less likely to lose track of their savings – as they could all be accessed in one place. In today’s society, consumers move homes and jobs much more frequently than in previous generations. This means an individual is likely to have multiple savings vehicles and even multiple pension pots. When an individual moves homes or jobs, there is every likelihood that they may forget to update one or two accounts. In the course of a working life, this is likely to lead to multiple lost accounts which are not taken into account in the assessment of an individual’s assets. Automated advice would facilitate the aggregation of account data and the development of more individualised advice, which looks systematically at all of individual’s data and automatically updates the individual’s profile as circumstances change.
No additional comments
Martin Parkes
B