UX in AI: Trust in Algorithm-based Investment Decisions

This Thesis looks at investors’ loss tolerance with portfolios managed by a human advisor compared to an algorithm with different degrees of humanization. The main goal is to explore differences between these groups (Humanized Algorithm, Dehumanized Algorithm, Humanized Human and Dehumanized Humans) and a potential diverging effect of humanizing. The Thesis is based on prior research (Hodge et al., 2018) but incorporates new aspects such as additional variables (demographics, prior experiences) and a comparison between users and non-users of automated-investment products. The core of this research is an experiment simulating an investment portfolio over time with four different portfolio managers. Subjects were asked to decide if they want to hold or sell a declining portfolio at five points in time to measure their loss tolerance. A cox regression model shows that portfolios managed by the Humanized Human had the highest loss tolerance. Humanizing leads to higher loss tolerance for the human advisor but to lower loss tolerance for algorithmic advisors within the non-user group. Keywords: Künstliche Intelligenz; Artificial Intelligence; Behavioral Finance; Behavioral Economics; Human-Computer-Interaction; User Experience; Investmententscheidungen; Nutzervertrauen

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

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

Systematic Literature Review on Robo-Advisery Adoption towards Young People

In the past two decades, numerous studies have focused on robo-advisers in financial technology. Robo-advisers involve automated financial advice for investors, tailoring portfolios based on risk tolerance and objectives, and automatic portfolio-monitoring and rebalancing. While robo-advisers have seen progress in adoption, there are still untapped potentials. This abstract presents a systematic literature review summarising the current state-of-the-art in robo-advisery adoption. By following a detailed, systematic literature-review methodology, the review provides valuable insights for practitioners, potential investors, and researchers seeking to identify areas for further investigation in robo-advisery adoption

A Conceptual Model of Trust Influencing Factors in Robo-Advisor Products: A Qualitative Study

As an integration of e-commerce and traditional financial service, robo-advisor is a promising product that recommends portfolios to individual investors based on modern technologies. However, this industry faces many challenges such as slow adoption and distrust from customers. This paper extends prior literatures in robo-advisor by exploring trust influencing factors and their detailed sub-factors from the perspective of five dimensions of trust. In this study, we not only validated previous factors of trust in the context of robo-advisor, but also found several new factors influencing customers’ feelings. A conceptual model is further proposed. The data analysis is based on semi-structured interviews with 27 investors. Understanding trust factors of robo-advisor helps the service vendors provide a better product for individual investors and facilitates faster adoption behavior from customers, which promotes further development of the industry

Intention to use analytical Artificial Intelligence in services. The effect of technology readiness and awareness

Purpose: The automation of services is rapidly growing, led by sectors such as banking and financial investment. The growing number of investments managed by artificial intelligence (AI) suggests that this technology-based service will become increasingly popular. This study examines how customers’ technology readiness and service awareness affect their intention to use analytical-AI investment services. Design/methodology/approach: Hypotheses were tested with a data set of 404 North American-based potential customers of robo-advisors. In addition to technology readiness dimensions, the potential customers’ characteristics were included in the framework as moderating factors (age, gender and previous experience with financial investment services). A post-hoc analysis examined the roles of service awareness and the financial advisor’s name (i.e., robo-advisor vs. AI-advisor). Findings: The results indicated that customers’ technological optimism increases, and insecurity decreases, their intention to use robo-advisors. Surprisingly, feelings of technological discomfort positively influenced robo-advisor adoption. This interesting finding challenges previous insights into technology adoption and value co-creation, as analytical-AI puts customers into a very passive role and reduces barriers to technology adoption. The research also analyzes how consumers become aware of robo-advisors, and how this influences their acceptance. Originality: This is the first study to analyze the role of customers’ technology readiness in the adoption of analytical-AI. We link our findings to previous technology adoption and automated services’ literature and provide specific managerial implications and avenues for further research

Demographic and Socio-Economic Factors as Barriers to Robo-Advisory Acceptance in Poland

Theoretical background: One manifestation of the use of artificial intelligence technology in financial services is robo-advisory. Automated assistants are used in the area of communication with consumers and the sale of financial products. The development of robo-advisory services may contribute to increasing the availability of financial services and the cost efficiency of banks’ operations. So far, however, robo-advisory has not been widely used in bank services, and the reasons for this can be seen in the lack of wide acceptance of robo-advisory by bank customers, among other things.Purpose of the article: The aim of this paper is to identify barriers to the acceptance of robo-advisory in the services of banks operating in Poland. Variables relating to the demographic and socio-economic characteristics of consumers were analysed. Knowledge in this area can provide banks with a practical guideline for activities aimed at increasing acceptance of artificial intelligence technology and wider use of robo-advisory in financial services.Research methods: The paper uses the results of a survey conducted in October 2020 regarding the application of artificial intelligence technology in the banking sector in Poland. The survey included a representative sample of 911 Polish citizens aged 18–65. A multinomial logit model was employed to identify variables that represent significant barriers to robo-advisory acceptance in financial services.Main findings: The conducted research helped identify the barriers to acceptance of robo-advisory among consumers in Poland. A low propensity to use robo-advisory in bank services is characteristic of respondents from older age groups, as well as those who do not show a predilection for testing new technological solutions. Lack of experience in using investment advisory services and customer concerns about the misuse of personal data by banks are also significant barriers

Robo-Advisory and Decision Inertia - Experimental Studies of Human Behaviour in Economic Decision-Making

Investing in the stock market is a complicated and risky undertaking for private households. In particular, private investors face numerous decisions: for instance, whether to invest in stocks or bonds, buy passively or actively managed investment products, or try something new like Bitcoin. They must decide where they can get independent financial advice, and whether this advice is trustworthy. As a consequence, information systems researchers design and build financial decision support systems. Robo-advisors are such decision support systems aiming to provide independent advice, and support private households in investment decisions and wealth management. This thesis evaluates robo-advisors, their design and use and thus their ability to support financial decision-making. Addressing this research need, my thesis is organized in three parts (part I-III ) consisting of four quantitative experimental studies, two qualitative friendly-user-studies, and one qualitative interview study. In Part I, Chapter 3 examines how robo-advisors can be designed for inexperienced investors. In particular, I derive design recommendations for the development of robo-advisor solutions and evaluate them in a three-cycle design sciences process. Requirements related to the clusters ease of interaction, work efficiency, information processing and cognitive load are identified as key elements for robo-advisory design. In Part II, Chapter 4 focuses on an important bias in economic decision-making - decision inertia, the tendency to repeat a decision regardless of the consequences. As a result, a decision-maker can make repeated suboptimal investments. To understand this bias more deeply, I investigate decision inertia in a general experimental setting and identify motivational and cognitive drivers of this phenomenon. Thus, I relied on behavioural, on self-reported, and on bio-physiological measures in three laboratory studies. In Part III, Chapter 5 specifies the findings from Part II to find and evaluate strategies to reduce decision inertia in financial decision support systems. For that purpose, I investigate two nudges (design features) to reduce inertia in investment decisions. My results suggest that defaults and warning messages can help participants to overcome decision inertia. Furthermore, the results illustrate that designers have to be careful not to push decision-makers into the decision inertia bias by accident. In summary, this thesis gives design recommendations for practitioners and scholars building robo-advisors. The insights can help to develop robo-advisors, and to increase advisor quality by considering decision inertia in the system design phase and consequently, it illustrates how to counteract this malicious decision bias for private investors