FinTech Innovation – From Robo–Advisors to Goal Based Investing and Gamification

PAOLO SIRONI is a global thought leader for Wealth Management and Investment Analytics at IBM, where he is responsible for promoting quantitative methods, Goal Based Investing (GBI) and digital solutions for financial advisory. His expertise combines financial services and technology and spans over a number of areas, including wealth management, asset management, risk management and FinTech innovation. Prior to IBM, he founded a FinTech startup that provided GBI portfolios to wealth managers. This venture subsequently became a part of IBM following the acquisition of funding partner Algorithmics, a world leader of risk management and investment analytics. Paolo possesses a decade-long risk management expertise; he was previously head of market and counterparty risk modeling at Banca Intesa Sanpaolo. He is the author of Modern Portfolio Management: From Markowitz to Probabilistic Scenario Optimisation, and contributing author to The FinTech Book.

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