Context

The purpose of this idea note (v1.0) is to outline our thinking about the future of financial advice.

We believe the industry is at a crossroads, and the definition of what will define ‘us’ and ‘them’, the ‘past’ and the ‘future’ and the ‘winners’ and ‘losers’ is now clear:  do you have (or can you get) a dynamic (data) model.

It was written by Angus MacNee with input from Elise Davies and Kevin Moss. If you would like to discuss this note in more detail or provide feedback, please contact us.

Introduction

The financial advisory market is evolving rapidly. The incumbent financial institutions are not evolving rapidly, and the very attributes that have made them successful in the past will increasingly be a disadvantage in the future.  That is, when broad accessibility, affordability, personalisation and a customer-centric value proposition will be essential.  However, when looking to the future of the financial advisory market, the key incumbent disadvantage and legacy issue, that is deeply entrenched within the regulations, is the ‘snapshot’ approach to data capture, client suitability and risk assessment.

Having a ‘snapshot’ approach is a consequence of a static (and analogue) information capture and analysis process, legacy systems and a ‘set-and-forget’ (FUM-driven) business model that does not necessarily put the needs of clients first.  The implications of a static approach mean that threats and opportunities cannot be managed in real-time, and efficiencies cannot be created through digital automation.

In contrast, a dynamic approach to data capture and analysis can enable threats and opportunities to be managed in real-time, it can provide more personalised solutions to more people and it can support better client outcomes at scale.

The market and client expectations are evolving.  A static approach is the past.  The future is dynamic.  In this note we outline our thinking about the future of financial advice.

Background

The financial advisory industry in the UK supports a market value of over £1 trillion[i] and almost 14,000 FCA-regulated firms[ii]. A big part of managing that value historically has been via ‘household’ names who have been assisting individuals manage their wealth and achieve their objectives in some cases for over 100 years.

The industry is growing.  The demand for financial advice increased 40% in 2018 with an additional 1.3 million people seeking advice[iii].  Despite this growth, the market penetration rate is still low with only one in ten adults in the UK receiving financial advice.  And despite this low market penetration, the UK wealth management market is worth over £1 trillion and will see the largest inter-generational wealth transition in the next 30 years with £2.8 trillion[iv] to be transferred to the younger generation.

Convergence of factors changing the market

Within the market, a convergence of technological, consumer and regulatory forces are creating opportunity and driving unprecedented industry transformation.

• New disruptive technologies: Technological innovation is impacting incumbent business models and their value proposition by creating efficiencies, improving the user experience and increasing the accessibility of financial services.  Examples include robo-advice, commission free trading, digital banks, digital currencies and many more.

• New consumer expectations:  Consumers today (particularly the younger ‘digitally-native’ generation) seek more control, personalisation, transparency and digitally-enabled methods to access information and services[v].  With only one in ten adults receiving financial advice and £2.8 trillion cascading over the next 30 years, services must adapt or be created to address this new market reality.

• New regulatory paradigms: New paradigms such as ‘Open Banking’ give individuals more control of their data by unlocking the silos of large banks and removing their systemic advantage in having exclusive access to client data.

Incumbents to be disadvantaged in the future

The financial advisory market today is dominated by institutions which do not (or cannot) champion a client-centric approach nor will their business models support the flexibility and adaptability to win in the future.

The times have changed: yet industry entrenched around a snapshot (i.e. static) approach

The financial advisory industry exists to support clients achieve better outcomes in the context of market factors, their financial and lifestyle goals, and their personal circumstances.  The industry is founded upon professional expertise and robust systems and regulation.  However, the financial advisory industry today is, along with its supporting regulations, entrenched around a snapshot approach to data capture (“as is” without metadata) that results in a static model for client suitability, portfolio evaluation and risk assessment, and mostly unstructured information.  That is:

• A client ‘fact-find’ generally achieves a ‘snapshot’ of the client’s circumstances and objectives;

• A file review (for compliance purposes) generally achieves a static risk, suitability and compliance assessment on an ex-post or after-the-fact basis.  This means that the file review can only ever determine the compliance of what information is available at the time, and not the compliance of the actual advice given, compliance in the future nor the qualitative value of the adviser outcomes; and

• An annual review (as mandated by FCA COBS 9A.3.9 & article 54(13) of the MiFID Org Regulation) generally provides a ‘snapshot’ assessment of continued suitability.

This information is then relied upon for the next 12 months which means that it may or may not accurately reflect the clients’ circumstances or objectives in the future.  That is, the information may be dated or inaccurate at any moment in time which impairs the effective usefulness of the information.

In considering this set of circumstances for how things are, and the fact that these practices support hundreds of billions of pounds in value, it seems appropriate to ask what is the purpose of (for example) a fact-find or annual review?  An answer to this question could be to understand the client’s objectives, circumstances and ‘attitude-to-risk’, to put in-place a suitable portfolio to support such circumstances and to monitor the suitability on an ongoing basis with respect to personal and market factors (all within a compliant framework).  Or more simply, it could be stated that the purpose of these measures is to support good process and therefore good client outcomes, assuming that these two criteria do actually correlate.

So, if this is the case, it could be appropriate to ask ‘why’ does the industry use, and its regulations mandate, a snapshot and static process to achieve this, what are the limitations and drawbacks of such a ‘set-and-forget’ approach, and how does this approach support the stated objectives?  For example:

• Why do people seek financial advice?  What are retail clients seeking to achieve?

• Why do we undertake a fact-find?  Does the client know and disclose all of the relevant information?  Does this introduce risk around communication and disclosure?

• Why is an annual review mandatory and not every six or eighteen months?  Is the timeframe arbitrary?

• Why do we undertake file reviews for compliance purposes?  Do file reviews lead to more compliant advice with better client outcomes?

• And so on…

The Why: Why static, snapshot and set-and-forget?

Because that is how it has always been done!  And how it could only be done in the past.  And, an example of how a legacy approach is deeply entrenched within the industry predicated around a business model of “retain and grow FUM” that is optimised for these conditions yet increasingly out of touch and subject to decreasing margins, increasing costs and having no compelling or differentiated value proposition.

So why not change?  Well, transformation is not easy. There is a regulatory disincentive to do things differently. And even if there is a desire to modernise, significant organisational change is difficult and expensive for any large institution particularly when:

• Legacy systems and processes are deeply entrenched in the operating model;

• The fundamental technological architecture is dated and cannot support 3^rd party integrations and APIs; and

• Legacy data has been ingested “as is” without metadata or context meaning it is mostly analogue, unknown, unstructured and therefore unusable.

So, when it comes to why we must perform tasks like annual reviews and the regulatory compliance, whether it was the regulation first which influenced the organisational design or vice versa, the operating model of the incumbents is optimised and structured accordingly. So if this business model represents the evolutionary dead-end of the previous era, then like dinosaurs in a changing environment, it is our contention that it will not be suited to a future when having a unified technology, data and financial services proposition will be essential. 

---

The Future is Dynamic

There is an alternative to static… the opposite i.e. dynamic

---

The opposite of static is dynamic, and the opposite of snapshot is continuous (i.e. real-time).  Therefore, if a dynamic approach to data capture and analysis is feasible, this would translate through to services that can be deliverable or undertaken in real-time, and information that is up-to-date in continuum.  In the context of financial advice, where the entire fact-find, research, recommendation, portfolio, suitability and compliance process is based on the information available, the implications for a dynamic approach are therefore far reaching and include the ability to:

• Deliver more personalised solutions at scale by having more accurate information
• Manage threats (i.e. risk) and opportunities (i.e. investment opportunities) in real-time
• Provide a more accurate assessment of the probability of success for desired outcomes
• Reduce the risks associated with providing financial advice and improve compliance procedures
• Delineate different risk profiles and increase the accuracy for pricing and insuring risk
• Create time and cost efficiencies with a better user-experience through digital automation
• Deliver better client outcomes to more people at greater profit with less risk

With these characteristics, it is evident that a dynamic approach to data capture and analysis can revolutionise the client experience, transform the industry value proposition and exceed the requirements of the current regulatory framework governing retail financial services.

With respect to precedent, there are many examples across a variety of industries (i.e. from manufacturing, ecommerce,  entertainment, banking and consumer goods) in which companies are utilising real-time data analytics to drive innovation, efficiencies, reduce risk, improve supply chain management and ultimately improve the customer experience.  For example, the Bank of Singapore was able to reduce the calculation time for value of risk from 18 hours to a matter of minutes[vi] when using a big data risk management system as part of a trajectory to real-time risk analysis.  There are also many recent examples when having real-time data is essential.  For example, the sudden nature of the Coronavirus pandemic and its impact on the global economy has put a premium on real-time data to help produce more accurate estimates for economic modelling and the fiscal and monetary response.  The simple fact is that no prior data-set has had any historical information for this type of ‘black swan’ event and therefore if you can’t access real-time data there’s nothing in the economic models to analyse and indicate what could happen.

Fact-find & suitability example

Currently, the process for a financial adviser to undertake a fact-find involves obtaining the information from the client either via an information exchange (i.e. face-to-face or via a communication tool) or self-directed data capture (i.e. through an application, portal or even engagement with a ‘chat bot’).  With each method, there are different levels of efficiency and user experience for both the client and adviser, and the innovation has historically been on the digitalisation of the user interface to streamline the data capture (and avoid re-keying).

The same approach is true for determining the ongoing client suitability when there is an ongoing provision of service by the financial adviser.  That is, on at least an annual basis (in line with MiFID requirements) the adviser will be required to ascertain from the client whether their circumstances or objectives have changed, and therefore whether their portfolio is suitable.  Again, the information must be obtained from the client either from an information exchange or by using a self-directed data capture (which can create efficiencies via automation).

Now, in contrast, imagine the concept for a ‘dynamic’ fact-find.  That is, effectively, when a client’s circumstances and objectives can be updated automatically and in real-time from the datasets and information already available (e.g. via Open Banking and other data feeds).  In this scenario, the ‘fact-find’ could be considered a live dashboard of the client’s circumstances and objectives that is being updated automatically and in real-time.  What this enables is for the client “review” or suitability assessment to occur on a continuous basis, or as-and-when most appropriate (i.e. not on an arbitrary or annual basis).  The implications for this dynamic approach are far-reaching: having up-to-date information enables an adviser to deliver better, more personalised solutions with less risk whilst also increasing the potential to create new commercial opportunities.  These implications are in the client’s best interests, the adviser’s best interests and they also satisfy and exceed the very essence of what the relevant regulations are seeking to facilitate for retail clients.

Now, a key point to acknowledge with a dynamic model and having access to information in real-time is that it is not about encouraging ‘tweaking’ of the portfolio (particularly when investing to achieve long-term objectives in a balanced portfolio) or intervention for interventions sake.  The key point is that with a dynamic model you have the real-time insight and option to act either via manual intervention or digital automation.  An apt analogy for how to think about this is it is like an Apple Smart Watch beaming back the health information to your doctor or emergency services provider in real-time.  If you are healthy and well, there is no need to act.  If your circumstances change for the worse or you have an accident, the medical authorities will be alerted and can take action.  In the context of financial advice, in our determination the capability to act based on real-time insight (determined behind-the-scenes) embeds the value proposition of the financial adviser to the client.  That is, the financial adviser is monitoring their ‘financial health’ in real-time behind-the-scenes with an ability able to act in a timely fashion when necessary, and in their best interests.

Compliance

As outlined in SYSC 6.1.1 of the FCA Handbook, financial advisory firms must ensure compliance with their obligations under the relevant regulations and counter the risk that they might be used to further financial crime.  Currently, the primary supervisory mechanism for most organisations within their compliance framework is an ex-post file review.  The file review may be complemented by spot checks, events, site visits and other supervisory measures such periodic financial assessments.  A characteristic of file reviews, that is common amongst most compliance measures, is that they generally achieve a static or snapshot assessment of risk, suitability and compliance.  That is, they can conclude; this advice or this client file does or doesn’t demonstrate suitability at this moment in time based on the information provided. 

With this approach, the compliance strategy is therefore underpinned by a ‘what is everyone else doing’ comfort-blanket and a ‘checkbox’ approach to supervision that endeavours to influence best-practice and ensure compliant advice via general oversight, feedback and punitive levers.  From a business perspective, the compliance regime is a cost-centre weighing on margins and generally a roadblock to scaling and growth.  Furthermore, it doesn’t scale.  If a business is 10 times larger it generally has 10 times more people with, until recently, only one CF10 Compliance Officer per FCA licence so the bottleneck or introduction of risk is inevitable

Dynamic Compliance Framework

In contract, a dynamic compliance framework can genuinely change the game.  That is, when a dynamic data model and data analysis engine can evaluate client case information in real-time using rule-sets, key risk indicators and AI to evaluate:

• Does the information required exist?
• Is the information satisfactory, unsatisfactory, low risk, etc.?

And then automate the relevant notifications and actions to avoid risk, manage risk and influence best-practice.  To provide context for this opportunity, in Australia our proprietary systems saw an 80% cost efficiency when scaling adviser numbers using a dynamic risk management approach.

The benefits of a dynamic data model are also apparent when it comes to risk assessment and pricing.  For example, a dynamic data model can better support the delineation of risk profiles for different financial advisory businesses and clients based on characteristics of their advice such as their client segmentation, circumstances, investment structure and product recommendations.  This enables more tailored, sophisticated and accurate pricing models in the context of Professional Indemnity Insurance (PII), a more extensive risk analysis framework that is ‘fit-for-purpose’ and an ability to handle legacy or complex investment types.  This differs to current PII polices that are based on a snapshot provision of information to an underwriter every twelve months that may not reflect the real risk of the current business model, or the prior liability risk of past advice.

Synergy with outcomes-based advice

In the context of outcomes-based advice, which itself is a more personalised and customer-centric approach to delivering financial advice, the benefits of utilising a dynamic approach (and data model) are evident and support better client outcomes and responsibilities for treating customers fairly.

Besides the obvious benefits (mentioned previously), a key difference with a dynamic approach to outcomes-based advice is that the desired outcomes themselves can be dynamic.  That is, the desired outcome (i.e. goal) of the client can adjust dynamically and in real-time in line with changing circumstances or information. For example, if a goal is predicated on a certain income level or lifestyle event, then the goal could change in real-time based on new information along with any required modification to the portfolio recommendation to ensure ongoing suitability for the client.    

Of course, the benefits (and pitfalls) of such a reality might be clear, however, even if the goal remains fixed (i.e. it does not change dynamically), a dynamic approach can bring considerably more resources to bear (i.e. accuracy) when seeking to predict the likelihood of achieving the goal over time (and ensuring suitability for the client). That is, the adviser will be better positioned to assess, forecast and recommend advice in order to achieve the desired client outcome and the client will have access to advice that fundamentally is aligned to their circumstances and a portfolio recommendation that is intrinsically suitable.

What can make this possible?

What makes it possible to even contemplate having a dynamic data model that can revolutionise the client experience, the value proposition for financial advisory businesses and the regulatory framework for retail financial services?

Simple.  A unified technology, digitalisation and data strategy and the systems and software to execute the strategy.  So perhaps more accurately, simple to articulate yet difficult to achieve.  Deriving actionable insights from data is the key and it is all about getting the right data, in the right format, to the right place if you are to benefit. 

For large incumbent organisations, their vast datasets are largely unknown, unstructured and unusable, and legacy systems are deeply entrenched.  What is required to transition to a dynamic data model are systems and software that have the capacity for large-scale dataset aggregation, data cleansing, data analysis and then data-driven automation and information services.

Despite the magnitude of the opportunity and also the challenge to realise all of the benefits of a dynamic data model, it is important to realise that an operating model can be evolved in phases to embrace a dynamic approach.  A starting point is having an open technology architecture and an API-first approach to software design that can support data sharing and 3^rd party integration. Even with legacy systems, advantages can be realised by getting the right data to the right place to benefit the adviser (and client) in their normal day-to-day business. 

Is this a new model for financial advice?

Not exactly.  It is clear that the operating model for financial advisory businesses is evolving into a hybrid model in which technology augments human capabilities to reach and service clients.  For example, financial advisers have been asked about what will transform the industry in recent surveys and almost 25% estimated data analytics, 30% robo-advice and digital platforms and 40% mobile applications[vii].  Indeed, there are many new FinTech firms seeking to operate in this space and also to serve self-directed retail clients via a direct-to-consumer model.

However, a dynamic data model and the corresponding benefits, such the automation of processes and an ability to translate data into actionable insights, does not necessarily require a change of approach or in the operating model for financial advisers.  With the right systems and software, you can get the right data, in the right format into the right place for the financial adviser and client to benefit WITHOUT them necessarily changing their approach.  This is an extremely powerful characteristic when undertaking a wide-scale implementation as it removes any barriers for adoption and requirements for stakeholders to embrace something ‘new’ before experiencing the benefits.  The promise and adviser proposition can be therefore: what if everything could stay the same yet change at the same time. 

This is all great… but what about the downside?

It needs to be outlined that, of course, that there are potential new risks and challenges for a dynamic data model that are less prevalent with the legacy approach.  Data privacy, data transfer and data security become significantly more important to even simple business tasks, and there are new regulatory requirements, such as The Payment Services Regulations 2017 in the provision of account information services.  In addition, the integrity of data capture is vital to avoid a ‘garbage in/garbage out’ scenario that can propagate for years into the future.  Thus, any dynamic data model needs to incorporate powerful checks and balances, so that data security and data integrity are maintained.

So, what does all of this mean?

The market is evolving. Static information is the past. The future is dynamic.

The large incumbent financial advisory institutions today are structurally disadvantaged to win in the future when having a unified technology, data and financial services proposition will be essential.  The incumbent disadvantage is an opportunity for organisations like Rimbal that embrace a dynamic data model and a vision for how this can transform retail financial services.

If you would like to discuss this in more detail, please contact us.

---

Sources:

[i] Khanna, A. and Hinrichs, E. (2019). U.K. Wealth Management: Spotlight on Value Creation. Executive Summary. L.E.K Consulting.

[ii] PIMFA, (2018). The Financial Adviser Market: In Numbers.

[iii] Critical Research (August 2018). The changing shape of consumer market for advice: Interim consumer research to inform the Financial Advice Market Review (FAMR).

[iv] St James Place, (2018). St James Place Annual Report.

[v] Courbe, J. (June 2016). Financial Services Technology 2020 and Beyond: Embracing disruption, PWC.

[vi] Kopanakis, j. (2019). https://www.mentionlytics.com/blog/5-real-world-examples-of-how-brands-are-using-big-data-analytics/.

[vii] Financial-planing.com Tech Survey 2019.