FinTech Innovation: From Robo-Advisors To Goal ...

Fintechs make money in different ways depending on their specialty. Banking fintechs, for example, may generate revenue from fees, loan interest, and selling financial products. Investment apps may charge brokerage fees, utilize payment for order flow (PFOF), or collect a percentage of assets under management (AUM). Payment apps may earn interest on cash amounts and charge for features like earlier withdrawals or credit card use.

So far, most of my professional life has been spent at the intersection between FINance and TECHnology, whose line of separation has recently been blurred by financial technology companies (FinTechs). The forces that are fostering their innovative mindset are unveiled in this book, which closely scrutinizes the revolution occurring in the wealth management industry, and particularly digital advice, personalized investing, and cognitive analytics being used to give insight into the behaviour of customers. The findings are based partially on market research and academic material, but mostly on what I owe to the hundreds of business conversations with industry leaders, innovators, entrepreneurs and colleagues. They have enriched this book, transformed any business travel that I have undertaken into a scholarly opportunity, and ultimately made my humble career, which started in risk management, an invaluable journey. Back in the 1990s, I learned to implement advanced quantitative methods to manage trading risks and I engaged periodically with top managers and regulators in search of graphical yet robust simulation methods to turn complex mathematical equations into intuitive reporting. When the wind of innovation blew at my door in the early days of the FinTech revolution, I was easily led on an entrepreneurial journey, it was my goal to change the investment experience as it existed between financial advisors and their respective clients, to allow them to speak more comfortably the intuitive language of Goal Based Investing (whose quantitative foundations are demonstrated in my previous book Modern Portfolio Theory: from Markowitz to Probabilistic Scenario Optimisation). I then had the privilege and deep learning opportunity to engage with the extensive network and client base of IBM on a global scale. This contributed to refining the strategic thinking at the heart of this book about the many challenges that small and large wealth management firms face in a disrupted landscape made of technology developments, generational shifts, changes in investors' behaviour, tighter regulation, and declining revenues in the traditional models of financial advice. Wealth managers do stand at the digital epicentre of a tectonic fault, which is disrupting their landscape that has, in many ways, been unchanged for centuries.

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While financial technology is often the subject of fanciful thinking and star-gazing, expert Paolo Sironi paints a credible and comprehensible picture of the robo-advising, goal-based investment and gamification that are transforming the investment industry. He captures the disruption happening in asset management and offers an attractive alternative business model for investment advisers and wealth managers under pressure to change. Though more examples of these innovations would have been useful, readers looking for a better handle on investment fintech will find this a good guide.

It is argued that Artificial Intelligence (AI) is one of the most promising technologies that would advance the transformation of the finance industry (Park et al., 2016). One of the most disruptive AI applications in finance has so far been the introduction of automated investment managers or digital advisors, more commonly known as robo-advisors (RAs). Based on each investor's characteristics, RAs deliver and execute portfolio allocation advice through automated algorithms on digital platforms. Purportedly, such a service is free from individual human adviser's biases but come at the cost of a one-size-fits-all problem and limitations introduced by the robo-advising algorithm (D'Accunto et al., 2019).

Sub-processes like (1) initial customer contact, (3) clarification with compliance and (4) analysis of last customer contact may be integrated since client conversations and segmentations can be analyzed in-time and updated based on the customer profile saved. Also, the sub-processes (2) identification of customer needs and (8) goal formulation merge since both can be derived with an intelligent CA. Sub-processes (9) determination of restrictions and (11) creation of risk profile are integrated applying a CA combined with PA for risk analysis. Finally, sub-processes (13), (14), (16) merge because the investment simulation and proposal generation are conducted by applying PA. While sub-process eliminations and shifts are observed through the whole process, accelerations take mostly place in the second part of the process where highly standardized and administrative tasks such as (20) settlement of contract and (24) customer reporting are located, which profit from digital interactions, digital data storage and the transmission of information and insights between the three AI-based application types.

The impact analysis results of the three AI-based applications CA, CS and PA imply a process redesign with changes in both the sub-process sequence and the system support. In contrast to the traditional PB investment advisory process, the first customer meeting can be conducted either by a CA or bank advisor. Sub-processes (1), (3) and (4) merge due to automation and efficiency gains from the use of intelligent CS and the communication via CA. An analysis of existing customer data along with a set of noncompliant examples allows to automatically estimate risks and flag fraudulent cases. The self-learning systems are continuously improved through iterative feedback to evaluate unknown cases. AI-based chatbots and voice assistants learn and memorize (2) individual client needs to support (8) goal formulation. As part of the client profiling, CA-collected data is used to assign a client segment and for (5) positioning in the right phase of life. With this, the combination of intelligent CS and CA allows (7) to identify and actively offer suitable cross-/upselling options. Further, the CA asks clients questions about risk tolerance and portfolio restrictions in the combined sub-processes (9) and (11). The recorded information is then interpreted by CS and possible market scenarios are contextualized for an accurate risk analysis. The final consolidated client profile (10) is subsequently used to offer the client an investment solution. Compared to the traditional advisory process, the sub-processes (13), (14) and (16) occur simultaneously due to efficiency gains. Here, PA is used for financial asset selection and allocation. Considering the predicted returns, risks and the client's risk tolerance, self-learning algorithms select the best fitting investments. Further suggestions for improvement made by the customer can be discussed interactively in the subprocess (19) customer approval for the overall solution using intelligent CAs. Once the investor is satisfied with the investment solution, a standardized contract can be drafted. With the conclusion of the contract (20), the consultation ends. Analogous to the traditional PB investment advisory, the following implementation of the agreed contract, i.e. (22), (23) and (24), runs without further customer interaction. In the last process step, maintenance, the forecasting capability of PA can be used for the successive assessment of the compatibility of the solution and market development (25). As proposed by Almahdi and Yang (2017), portfolio profitability is continuously checked, and alternative investment instruments are recommended to either the bank advisor or the client (Figure 5).

Machine generated contents note: Preface Acknowledgments About the Author I Personalise personal finance! 1 The theory of innovation: from Robo-Advisors to Goal Based Investing and Gamification 1.1 Introduction 1.2 A vibrant FinTech ecosystem 1.3 Some definitions, ladies and gentlemen 1.4 Personalisation is king 1.5 The theory of innovation 1.6 My Robo-Advisor is an iPod 1.7 What incumbents should consider when thinking about FinTech innovation 1.8 Conclusions II Automated long-term investing means robo-technology! 2 Robo-Advisors: neither robots nor advisors 2.1 Introduction 2.2 What is a Robo-Advisor 2.3 Automated digital businesses for underserved markets 2.4 Passive investment management with ETFs 2.5 Algorithms of automated portfolio rebalancing 2.6 Personalised decision-making, individual goals and behaviour 2.7 Single minded businesses 2.8 Principles of tax-loss harvesting 2.9 Conclusions 3 The transformation of the supply-side 3.1 Introduction 3.2 The investment management supply-demand chain 3.3 How intermediaries make money 3.4 Issuers of direct claims (debt owners) 3.5 The institutionalisation of the private banking relationship 3.6 The digital financial advisor 3.7 Asset management is being disintermediated 3.8 ETF providers and the Pirro's victory 3.9 Vertically integrated solutions challenge traditional platforms 3.10 Conclusions 4 Social and technology mega trends shape a new family of taxable investors 4.1 Introduction 4.2 Generational shift (X, Y, Z and HENRYs) 4.3 About transparency, simplicity and trust 4.4 The cognitive era 4.5 Conclusions 5 The industry's dilemma and the future of digital advice 5.1 Introduction 5.2 Wealth management firms: go digital or die! 5.3 Asset management firms: less passive, more active! 5.4 Robo-Platforms: less transactions, more portfolios! 5.5 Digital-Advisors: empowered customisation! 5.6 Robo-Advisors: be human, be virtual, mind retirement! 5.7 Conclusions: clients take centre stage, at last! III Goal Based Investing is the spirit of the industry! 6 The principles of Goal Based Investing: personalise the investment experience 6.1 Introduction 6.2 Foundations of Goal Based Investing 6.3 About personal needs, goals and risks 6.4 Goal Based Investing process 6.5 What changes in portfolio modeling 6.6 Personal values 6.7 Goals elicitation 6.8 Goals priority 6.9 Time horizons 6.10 Risk tolerance 6.11 Reporting goal-centric performance 6.12 Conclusions 7 The investment journey: from model asset allocations to goal-based operational portfolios 7.1 Introduction 7.2 Main traits of Modern Portfolio Theory 7.2.1 Assets diversification and efficient frontier 7.2.2 The Mean-Variance model portfolio 7.2.3 Final remarks about Mean-Variance 7.3 Main traits of Black-Litterman 7.3.1 The equilibrium market portfolio 7.3.2 Embedding professional views 7.3.3 The Black-Litterman's optimal portfolio 7.3.4 Final remarks on Black-Litterman 7.4 Mean-Variance and mental accounts. 7.5 Main traits of Probabilistic Scenario Optimisation 7.5.1 The PSO Process 7.5.2 The investor's risk and return profile 7.5.3 Generation of scenarios and scenario paths 7.5.4 Stochastic simulation of products and portfolios over time 7.5.5 Potential and admissible portfolios: allocation constraints 7.5.6 Adequate portfolios: risk adequacy 7.5.7 Objective function: probability maximisation 7.5.8 Final remarks on PSO 7.5.9 Conclusions 8 Goal Based Investing and Gamification 8.1 Introduction 8.2 Principles of Gamification 8.3 Gamification of wealth management 8.4 The mechanics of games 8.5 Conclusions Concluding remarks Bibliography Index.

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