4.1 Introduction

Modern economies and human society at large are facing a period of unprecedented change which can be explained by the interaction of three mega trends, affecting investors' behaviour globally, with geographical differences but virtually no borders. First of all, ownership of financial assets is polarizing in the hands of the top tier of wealthy individuals, while the US and European middle class is shrinking for the first time since the Second World War. Wealth is also migrating globally towards growth markets, particularly Asia, where the middle class is instead growing. Wealth is also about to be passed to younger generations which are more techno-literate (e.g., Millennials), if not digital-native. This redistribution is creating a more diverse elite, new groups of investors, and a modification of the primary needs for the savings of families. Second, the internet has become fairly ubiquitous and this extraordinary level of connectivity, fostered by the affirmation of smartphones, has allowed us to learn new forms of social life and professional engagement (e.g., “uber-ization”). The change in consumers' behaviour is challenging traditional firms, which are embracing new technology (e.g., behavioural analytics) to reposition business strategies along the alleys of the digital village. Third, the enfolding of the global financial crisis has been economically painful for the majority of investors, reducing their appetite for risk-taking on equity markets and deteriorating their trust in financial institutions. Policy-makers have responded to the public outcry by rolling out breakthrough market regulation (e.g., FINRA rules, MiFID II, RDR, FoFA), which fosters higher transparency on intermediation costs and the packaging of risk, attempts to enhance investor protection, and realigns the basic incentives of the industry to the ultimate financial goals of individuals. Such regulatory tightening is affecting the traditional asymmetry of information of the supply-demand chain, and opens doors for a broader democratization of the investment relationship which FinTechs have quickly exploited. The combination of these forces helps to understand the rise of Robo-Advisors and the strategic relevance of Goal Based Investing, as represented in Figure 4.1.

This chapter sketches out the main traits of these revolutionary trends, and delves deeper to highlight how breakthrough technology interacts with a resulting new set of personal values.

4.2 Generational Shift (X, Y, Z, and HENRYs)

The financial industry expanded significantly after the Second World War as major economies enjoyed an unprecedented period of prosperity. Decades-long market growth accompanied the strengthening of the middle class by disposable wealth and numbers (e.g., Baby Boomers), as indicated in Figure 4.2 by the historical dynamics of the S&P 500 index.

However, today's financial environment is very different. Less than prudent financial innovation has fostered contagion effects among globalized markets and transferred an unprecedented amount of risk to individuals (e.g., the sub-prime crises). The imbalances created by market exuberance have become more pronounced, fuelled by the exponential growth of capital inflows and the speed of electronic trading. Price corrections are more frequent and severe, questioning the validity of common assumptions like the long-term relationship between the performance of stocks and fixed income markets. As individuals and companies have learned to depend more on financial markets, particularly in the US, market crises have widespread impact on the real economy and sometimes social security. All of this happens at the very same time that the Baby Boomers have started to retire, draw down from their wealth at various points of the market cycle, or pass it to the next generations of more tech-savvy taxable investors. A very large portion of the affluent and mass affluent population will depend upon reduced or uncertain retirement income, because the progressive deterioration of the economic cycle and the contraction of the active workforce have started to put government sponsored pension plans under unsustainable stress. Policy-makers have addressed this problem by requiring individuals to be more responsible during their active working life and invest in private plans which are usually financed by compulsory employers' contributions and tax-deferral advantages. Yet, the unintended consequence of the shift from defined benefits to defined contributions has been to add long-term financial risk to the potential performance of retirement savings, which further exacerbates the dependance of basic social security on the fate and cycle of the financial markets. A demographic divide is also building since the world population has been growing steadily but not evenly: developing countries are adding to most of the increase, while mature economies are experiencing very low birth rates. This is creating a dichotomy between the needs of an ageing population in the US, Canada, Europe, Australia, and Japan, compared to the “youth dividend” of growth markets, particularly in Asia. Therefore, incumbent institutions are challenged to expand fast and reposition their brands and operations in diverging environments which are more challenging than ever before, and have to contend with: a severe loss of reputation caused by the global financial crisis; a shrinking middle class, which has a lower portion of available income to invest in financial products, or can no longer afford the fees of traditional services; an ageing population, which needs to de-cumulate from pre-retirement investments; a change of ownership of financial resources from traditional Baby Boomers to younger and more tech-savvy cohorts (X and Y); and a diverging world stage. The United Nations Joint Staff Pension Fund report (2013) provides an insightful description of the behavioural differences among generations:

• Traditionalists (1925 to 1945): the Veterans' generation is made up of individuals who experienced economic and political uncertainty culminating in the Second World War, which taught them to become hard working, financially conservative, and cautious. Typically, they like rules, do not like change, and are fairly risk-averse.
• Baby Boomers (1945 to 1965): this generation grew up in a healthy post-war economy. They generally value hard work and own the bulk of middle-class wealth in mature economies.
• Generation X (1965 to 1980): this generation witnessed the birth of the information age and grew up with a high rate of mixed-culture, mixed-race, and blended families. While Boomers literally “lived to work”, Generation X “has been working to live”, as they were reared in the shadow of more prominent older generations. Since work life is more a means to an end, they are quite goal-orientated and dedicated, yet value the freedom to do it their way which is reflected in being more self-directed when it comes to financial investments. They also express higher techno-literacy.
• Generation Y or Millennials (1980 to 2000): this generation has been supported for longer by their parents, due to rising costs of housing and education. They have been encouraged to be opinionated, yet with a higher degree of relativism, which makes them more open to challenging the status quo, established brands, and incumbent institutions. More than Generation X, they have grown up with computers and the internet as an important part of their lives. Due to their experience in a global and networked society, they are highly connected through social networks, instant messaging systems, and blogs. They tend to like diversity more, might lack the skills for dealing with difficult situations, and hence favour immediacy and simplicity.
• Generation Z or digital-native (after 2000): Google already existed when they opened their eyes for the first time. Too young to be a target of financial services, they pose a series of long-term concerns to traditional wealth managers because, being more than techno-literate, they are truly digital beings. They are therefore even more open to accepting a full disintermediation of financial services by newcomers powered by robo-technology.

Generations X and Y also tend to experience many more life events compared to conservative Baby Boomers: they have their first children later in life, they change jobs and relocate more frequently, they might not own a house, they might have to take care of their children for longer, and at the same time assist elderly parents. This seems to lead to more varied investment requirements, or a broader set of investment goals to be fulfilled at once. Moreover, Millennial HENRYs (High Earning, Not Rich Yet) account for a significant portion of wealth owned by new generations and seem to feature even lower dependency on human engagement when it comes to financial advice, with a higher propensity to adopt leaner, digital, and “any time, anywhere” investment solutions such as Robo-Advisors can offer. Since new generations are more aspirational in their approach to life and consumption, traditional firms are asked to revise their long-term approaches to investment and relationship management, and adopt more transparent and engaging customer experiences to position their services along the lines of the generational shift. The disruptive self-directed approach of Robo-Advisors, the personalization of Goal Based Investing, and the emotional engagement of Gamification seem to provide valuable answers to the needs and values of these generations, and allow the creation of a captive digital experience.

4.3 About Transparency, Simplicity, and Trust

The changes ushered in by the generational shift are not confined to a different propensity and ability to use digital tools and communicate virtually, but extend to the values that individuals possess and which modify their expectations when dealing with personal investments and financial advisors. Social networks have made peering more flexible, so that people are more likely to associate with professional or social networks and trust “people like me”. As news streams in at unprecedented speed, opinions and values can be forged and exchanged fast (e.g., viral messages). Therefore, there is a modification of how trust in organizations is built within communities, businesses, and brands. Trust can be established with digital marketing, but can be easily destroyed by word of mouth and negative sentiment on social media. Financial institutions, which suffered a severe loss of reputation during the GFC, are struggling to rebuild a trustworthy image and seem to be fairly slow to embrace social media to their advantage, compared to other industries. FinTechs instead have demonstrated that banking brands can be challenged with lean budgets, digital solutions, and smart marketing. Personal financial advisors themselves have the opportunity to use social media to establish professional trust by creating blogs and sharing actionable content, thus enlarging their network and engaging their clients more effectively. The web is a key marketplace for peering, prospecting, and content sharing. Regulators are imposing higher transparency on investment costs and individuals are learning to compare services in terms of their full costs and added value. The internet is clearly facilitating these comparisons and favours businesses whose offers come across as simpler and more intuitive. In such an environment, user-friendly digital access and upfront asset allocations seem to be winning propositions. The complexity of investment decision-making can be simplified and represented graphically to enhance intuitive understanding of otherwise complex mathematical relationships. Also tabular representation of risks and opportunities seems to be more effective than verbose legalese. Yet, financial institutions clearly struggle to find the right balance between compliance and red-tape, digital ergonomics, mitigation of legal risks, and intuitiveness of investment propositions.

The “time-squarization” of financial news has become a clear problem. The overabundance of financial data is not optimal, and can confuse investors and affect their decision-making. As news bounces on radio channels, televisions, social media, billboards, and magazines, individuals cannot easily filter out what is relevant from what is noise. Smart spin doctors can convey messages and prop up perceptions that exploit or generate sentiment, and hence influence public opinion. Financial services are not exempt and often find themselves in the middle of the storm as markets and economies go through the cycle. Every uptick of the market, no matter how exuberant, is welcomed as inevitable while every downtick is described as the destruction of public value. Wealth managers are therefore required to filter information conveyed to their respective customers, and make sure that their message is properly received and that clients can focus only on those elements which are relevant and actionable. Most Robo-Advisors attempt to engage customers in long-term investing, tempering the emotional impact of market news and directing investors' attention to their long-term message instead.

Advisory firms need to focus on two key principles to mitigate the “time-squarization” of financial news: information needs to be personal (hence relevant) and actionable.

• Personalization: hints conveyed to investors must be relevant given existing portfolios, what they search for on the web, declared or most likely goals, personal characteristics, and behaviour of their peers. This would allow wealth managers to approach clients with relevant content at the right time of their life, enhancing the probability that such communication is impactful and adds value to the relationship.
• Actionability: any piece of information conveyed to investors should lead to the potential generation of a trade, or a request for more financial advice, particularly if human advisors cannot act as a filter or translator for financial news, which is the case with self-directed investors.

Therefore, personalizing the informative context can significantly enhance experience to generate more business. This can be achieved by deploying behavioural analytics:

• log-in sensors: firms can learn customers' habits such as preferred log-in time and frequency, to reach out with the most appropriate schedule.
• “googling” sensors: institutions can track what investors search for (e.g., products, news, documents) when they engage with applications, and customize the display to show similar elements in subsequent web sessions.
• investment relevance: applications can highlight financial news which is related to the risks and opportunities affecting the bets in clients' portfolios.
• peer relevance: investors can be made aware of what their peers buy, sell, or search for in order to reinforce a desired behaviour or any commercial message.
• social media: analytics can follow clients on social media and garner insights into their mood, topics of interest, and relationships by means of deep learning and analytics for personality insights. This would help to create the right personalized content to reach out and engage at the right time.

It's about content, of course! Yet, the most important element of content customization would be the personalization of the heart and soul of the investment experience itself; that is why and what we should buy or sell. That is portfolio modelling based on Goal Based Investing principles.

4.4 The Cognitive ERA

The ambition of creating a knowledge power house is not new to human history and finds a germane example in the Library of Alexandria, built in the Hellenistic period which followed the life of Alexander the Great (356–323 BC), whose inspiration and visionary belief in a multi-cultural society transformed his world into a cosmopolitan stage. The Library was the apex of a knowledge based intelligentsia, which attempted to consolidate into a single space an impressive quantity of data, knowledge, and scientific insights. Today it would not be possible to store within a single centre the amount of data that humans and their machines generate, nor would it be possible to distinguish with clarity what is relevant for individuals or decision-makers. Big Data analytics seem to be the solution to the challenging tasks of deriving insights from such an impressive informative space. From being silent servants of human-designed processes, computers are turning into business partners, virtually capable of understanding human narrative, interpreting images, and learning to draw non-obvious correlations across an immense amount of unformatted data. Cognitive computing can embrace all aspects of the Internet of Things, as Big Data analytics create the logical relation among any pieces of information that our digital world exchanges and generates. Banks themselves are creating new job functions, such as data scientists, to tackle Big Data and optimize their commercial propositions to final investors. Nowadays, cognitive expertise can go hand in hand with human advice.

“What is Big Data?”

Big Data is a broad term to indicate information sets which are so large or complex that they make traditional processing tools inadequate. We primarily refer to the business challenges to exploiting data abundance and achieving more accurate predictions of market trends and investor behaviour: collecting, searching, analysing, reducing, visualizing, and complying with privacy rules. Industry analysts usually define Big Data by referring to the 3Vs work of Douglas Laney (2001): Volume, Velocity, and Variety.

• Volume: fast growing data volumes cover the storage of transaction based data, social media streaming of unstructured data, sensor based and machine-to-machine inputs and outputs. As storage costs have been decreasing over recent years, today's main problem posed by excessive data volume refers to the determination of relevance within large datasets and how to use analytics to create value.
• Velocity: most organizations face time challenges, as data streams in at unprecedented speed and must be dealt with in a timely manner.
• Variety: data can be generated as structured representations in traditional databases such as financial transactions, or unstructured formats such as pdf documents, tweets, emails, videos, and audios. Governing such a variety is something many organizations still grapple with.

The Big Data revolution is changing the way wealth management institutions shape their strategic approach to decision-making and business intelligence, as shown in Figure 4.3.

Enterprise Resource Planning tools (ERP) have been used traditionally to optimize cost/income ratios by focusing on detailed transaction data to increase sales volumes or profitability. Since the supply-demand chain of the wealth management industry is not a straightforward mechanism, greater importance had been assigned to the use of Client Relationship Management tools (CRM), which provide a standardized approach to storing and sharing information about investors' data and their interactions with advisors. Thorough analysis of such data has traditionally been performed to increase the effectiveness of advisory campaigns and human relationships, to improve customer retention and make customer relations more efficient. Big Data has introduced a new approach to business intelligence, which broadens the spectrum of customer analytics to all possible information about individuals and families as they are part of communities, social media platforms, demographic cohorts, or peer groups. Therefore, the investigation of such an enlarged dataset can strengthen the positioning of the wealth management offer and enables one to act on sentiment. Clearly, such an analytical challenge cannot be handled by traditional business management systems based on relational databases, desktop statistics, or visualization packages. Businesses require new forms of data analytics to uncover large hidden values from datasets that are diverse and on a massive scale. Machine learning appears to be the most revolutionary approach, since it does not require filtering data, but attempts to detect patterns or correlations among pieces of information, to identify the most adequate answer to a well-defined knowledge based problem. Digital technology makes visualization problems more approachable, allowing us to contextualize cognitive answers and drill down into Big Data datasets with graphical representations so that a new terminology seems to be arising within the business community: Smart Data.

Since Robo-Advisors were born at the intersection between finance and technology, they are digital solutions by birth and speak the language of social media. Their positioning in front of the broader public is fairly agile compared to traditional wealth managers. Most importantly, they have learned to garner information about target customers by tiering them not solely on disposable wealth, but primarily by analysing their investment behaviour, their level of techno-literacy and their social media interaction. Therefore, they are well poised to benefit the most from Smart Data and behavioural analytics, and they can rank first adopters of machine learning to strengthen clients' on-boarding mechanisms: by plugging in cognitive dialogues and replacing the current “tick-the-box” type of profiling questionnaires. They can be first adopters of blockchain technology to retrieve information about an individual's demographics and turn account opening and aggregation into a much faster and less painful experience for taxable investors.

4.5 Conclusions

Robo-technology and Goal Based Investing have been gaining momentum due to a concomitance of factors (i.e., mega trends) ranging from innovation in technology, demographical shifts, higher fiduciary standards, to the progressive digitalization of everyday life. We have presented what Robo-Advisors are, we have discussed the threats faced by the industry, and we have provided insights into some mega trends. Having discerned the forces at play under the crustal plate of the wealth management fault, the next chapter attempts to draft its future landscape above ground while the tremors of the digital earthquake are still occurring.