Financial Robo-Advisor: Learning from Academic Literature

Financial Robo-Advisor is the technology that integrates machine learning and self-identification to determine investment decisions. This study explores the financial robo-advisor based on bibliometric analysis and a systematic literature review. The method used three steps: determining the keyword, bibliometric analysis of literature metadata using VOSviewer, then collecting and analysing the articles. The bibliometric analysis results show five cluster keywords defined with different colors. In the network visualization, the robo-advisor connects to other keywords: investment, fintech, and artificial intelligence. Furthermore, the systematic literature review shows that the articles are divided into seven research objectives: (1) Law, Regulation, and Policy; (2) Investment Literate and Education; (3) Offered Services; (4) Present Risk-Portfolio Matching Technology; (5) Optimal Portfolio Methods; (6) Human-Robo Interaction; (7) Theoretical Design and Gap. Furthermore, this study can be used by academicians and practitioners to find out about robo-advisors based on an academic perspective

FinTech Innovation: Review and Future Research Directions

This paper aims to survey the most recent theoretical and empirical literature on FinTech innovations in the financial sector. The purpose of this review is to investigate how FinTech Innovations are altering and reshaping the universe of financial service providers, and challenging traditional business models and infrastructures. This study summarizes the opportunities and challenges of FinTech Innovations and the implications to the legacy incumbent financial services companies. FinTech innovation fusions technological capabilities potentially provide innovative financial products and services to foster financial inclusion, streamline processes, and lower costs to clients. FinTech can bring greater competition and diversity in financial services. Further, this research interprets the findings from the lens of institutional theory to advance the theoretical understanding of social changes facilitated by FinTech innovations in revolutionary areas in banking (lending, payment), security trading (real-time settlement, automated investment), and insurance (personalized experience). The investigation points out the regulatory concerns highlighted in the scholarly works, suggesting collaboration is critical to enable multi-stakeholders to anticipate and foster pro-innovative, transparent regulations to deliver meaningful benefits to innovation and financial inclusion. Lastly, this review identifies future research areas to further enrich knowledge to create a future-proof, more efficient, and resilient financial ecosystem to enhance financial stability in the digital era

Understanding Financial Risk Tolerance. Institutional, Behavioral and Normative Dimensions

This book focuses on the contribution of financial risk tolerance in shaping the workings of financial markets. It combines very different views to understand how this concept, lying at the crossroads of different domains of study and practice, including financial regulation, scholarly studies, and financial advisory practice, has been formalized over the last 30 years. The book looks at the feedback loop among the different domains in which risk tolerance is assessed and operationalized to reorganize the current stream of research on financial risk tolerance and suggests further relevant domains in which a new risk tolerance definition will need to be defined. Using key landmark moments in the normative evolution of financial services in the European Union (MiFID and MiFID 2), this book highlights the relationship between scholarly definitions of risk tolerance, key measurement tools, and the formal requirements imposed by regulatory institutions to key market players. This book provides a snapshot of the most important dimensions in which financial risk tolerance has been analyzed and highlights the relationship between policy-making and scientific endeavor. We touch upon precursors of financial risk tolerance, reviewing key socio-demographic variables, and move on toward more dynamic versions of financial risk tolerance that include the role of life events. The different chapters focus on the debate on financial risk tolerance in specific time frames marked by regulatory events and provide an in-depth overview of two important changes in European financial markets—sustainable investment and fintech and robo advisory. A practitioner’s view section authored by the CEO of a UK-based investment firm is included as a commentary and includes relevant insights from the world of financial advisory tied to the academic debate discussed in the text

Business Model of Sustainable Robo-Advisors: Empirical Insights for Practical Implementation

The given research paper examines the characteristics of German private investors regarding the probability of using robo-advisory-services. The used data set was gathered for this purpose (N = 305) to address the research question by using a logistic regression approach. The presented logit regression model results indicate that the awareness of sustainable aspects make a significant difference in the probability of using a sustainable robo-service. Additionally, our findings show that being male and cost-aware are positively associated with the use of a sustainable robo-advisor. Furthermore, the probability of use is 1.53 times higher among young and experienced investors. The findings in this paper provide relevant research findings for banks, asset managers, FinTechs, policy makers and financial practitioners to increase the adoption rate of robo-advice by introducing a sustainable offering

Determinants and Barriers of Adopting Robo-Advisory Services

Robo-advisors enable customers to conduct automated digital investments, which could substantially transform the financial industry. However, robo-advisory use is lagging behind expectations. One reason could be potential customers’ insufficient trust. Therefore, we investigate determinants that influence trust and the intention to use robo-advisors. More specifically, we build on trust to assess use intention and explore person-al characteristics (perceived risk), organizational characteristics (trust in banks) and in-dustry characteristics (structural assurances) as antecedents to trust. The survey data are analyzed by employing a PLS-SEM (n = 246). Preliminary results show that initial trust in robo-advisors is closely related to the inten-tion to use robo-advisors. Trust is negatively linked to perceived risk but positively linked to structural assurances. Trust in banks is positively related to initial trust, how-ever, only when structural assurances are not included. In a follow-up survey, behavior and potential barriers to robo-advisory adoption will be investigated

Regulating Fintech in Canada and the United States: Comparison, Challenges and Opportunities

This article compares the regulatory frameworks, challenges and opportunities in financial technology (fintech) in Canada and the United States. Fintech is explored as a post 2008 global financial crisis phenomenon by reviewing the diverse interpretations of its definition, identifying its historical underpinnings, and noting industry trends and associated demand factors. The market environment, and regulatory approach, in Canada and the U.S. is not homogenous, and although there are similarities, each jurisdiction faces different challenges and opportunities. In the U.S., fintech has significant disintermediation potential, and supervisory structures exhibit fragmentation under a rules-based framework. Nevertheless, there is growing desire for principle-based regimes in the U.S., and several federal and state regulators have instituted “regulatory sandboxes.” Canada is characterized by principle-based regulation and features a robust sandbox in securities jurisdiction; yet fintech is largely being experienced as a bank-driven phenomenon, and bank-fintech partnerships are visible, as incumbents use fintech to enhance customer service and operations. Regulatory fragmentation does, however, present an entry cost for new consumer-facing firms in Canadian fintech sectors not falling within the ambit of federal financial institution oversight. Similarities and differences between the U.S. and Canada are explored in this article across multiple fintech sectors including fintech banking, cryptocurrency (cryptocurrency as money, cryptocurrency funds and derivatives, and initial coin offerings), fintech credit (peer-to-peer lending), payments, algorithmic wealth management (robo-advisors) and financial account aggregators. The article also discusses regulatory adaptations such as sandboxes; the status of large-scale financial blockchain implementation projects; the emergence of "regtech"; international regulatory coordination efforts; self-regulatory structures; systemic risk considerations; and optimal regulatory design principles, given continuing market and product complexity. Fintech presents opportunities - like lower costs, enhanced product and service scope, greater credit and financial inclusion – and unique new risks (which are explored in detail in the article) as well as challenges for regulators, such as creating laws that accurately capture new technology and keeping pace with constantly evolving innovations. Regulators must also balance encouraging innovation and competition with effective risk management and supervision

Artificial Intelligence and Bank Soundness: A Done Deal? - Part 1

Banks soundness plays a crucial role in determining economic prosperity. As such, banks are under intense scrutiny to make wise decisions that enhances bank stability. Artificial Intelligence (AI) plays a significant role in changing the way banks operate and service their customers. Banks are becoming more modern and relevant in people’s life as a result. The most significant contribution of AI is it provides a lifeline for bank’s survival. The chapter provides a taxonomy of bank soundness in the face of AI through the lens of CAMELS where C (Capital), A(Asset), M(Management), E(Earnings), L(Liquidity), S(Sensitivity). The taxonomy partitions opportunities from the main strand of CAMELS into distinct categories of 1 (C), 6(A), 17(M), 16 (E), 3(L), 6(S). It is highly evident that banks will soon extinct if they do not embed AI into their operations. As such, AI is a done deal for banks. Yet will AI contribute to bank soundness remains to be seen