Nowadays, tech companies have entered our lives in nearly every possible area of application, from smart coffee machines to algorithmic-based music recommendations. Logically, it is not a far stretch that the financial sector will also experience disruption through technology-oriented startups. The so-called FinTech’s, short for financial technology, can be independent, newly found startups, or can be implemented by existing financial institutions as a complementary sales channel and span a wide ar-ray of functions, including peer-to-peer lending and crowdfunding, cryptocurrencies and blockchain, and also, robotic investment advice. It is no surprise that this development will affect traditional financial advisory.
Mainly robo advisors are seen as one of the most disruptive technologies in the financial sector. What used to be a people’s business and strived through human connections and relationships turned digital: a robo advisor can replace all functions of traditional financial advisors at a lower cost point and while being available 24/7. Based on financial theory, the offer investors personalized portfolios – all through pressing buttons on a phone screen. Whilst promising to streamline financial investment and to make it accessible to everybody, regardless of wealth, customer adoption compared to the global financial service market has been low.
Disruptive technologies offer a lot innovative and smart features, but customers might be hesitant to try the solutions. People rely on the experience of others to build trust, and the little experience of early adopters might not be enough to influence trust to a large extent. Trust is an important factor for all services or technologies, but especially in unprecedent areas such as fully automated financial advice.
The thesis will be based on a literature review methodology and will assess the theoretical background of trust through analyzing and comparing previously done research on the matter. Additionally, a quantitative study focusing on trust-building factors in robo advisors has been used as a basis to form conclusions regarding the increase of trust. Industry insights, journal articles and conference papers build the foundation of this thesis. They were identified through the usage of scientific search engines, but also through backward and forward referencing searches. This approach provided a multitude of applicable literature from the fields of artificial intelligence and trust.
Table of Contents
1. Introduction
2. Background Information
2.1 The History of Investment
2.2 The Development of Artificial Intelligence
3. Robo Advisors
3.1 The Rise of Robo Advisors
3.2 Functions of Robo Advisors
3.2.1 Configuration Phase
3.2.2 Matching Phase
3.2.3 Maintenance Phase
3.3 Customer Structure
3.4 Robo Advisors Compared to Traditional Financial Advisory
4. The Meaning of Trust
4.1 Influences on Trust
4.2 Trust in Financial Services
4.3 Trust in Technology
4.3.1 Trust in Automation and Artificial Intelligence
4.3.2 Algorithm Aversion and Algorithm Appreciation
5. Trust in Robo Advisors
5.1 Trust-influencing Mechanisms of Cheng et. al
5.2 Humanized Product Design
5.3 Undistrust
5.4 Building Initial Trust
5.4.1 Reputation and Trust
5.4.2 User Experience and Trust
5.5 Developing Continuous Trust
6. Conclusion
Objectives & Research Focus
This thesis examines how trust-building factors influence customer adoption and usage of robo advisors, specifically comparing them to traditional financial advisory services. The study focuses on identifying mechanisms that enhance trust in fully automated financial platforms by analyzing the interplay between product design, reputation, and the investor's perspective.
- Theoretical analysis of trust within financial services and automated technology.
- Assessment of the "Trust Cycle" in the context of robo advisory services.
- Comparison of features and trust-influencing factors between human advisors and algorithms.
- Evaluation of user experience and product design as catalysts for initial trust.
- Exploration of how algorithm aversion can be mitigated to increase adoption.
Excerpt from the Book
3.2.1 Configuration Phase
The configuration phase aims to reduce information asymmetry between investor and advisor through online questionnaires. The focus lies hereby on financial goals, risk tolerance, and investment horizons (Gold & Kursh, 2017; Kaya, 2017). Questionnaires do not only reduce the duration of the onboarding, but they also give the investor a feeling of logical choice and control, as it makes the recommendation less based on a third-party recommendation and more on own opinions (Sironi, 2016, as cited in Jung et al., (2019).
Summary of Chapters
1. Introduction: Outlines the rise of robo advisors as a disruptive technology in the financial sector and establishes the research goal of identifying trust-building factors.
2. Background Information: Covers the historical evolution of investment methods and the technological development of Artificial Intelligence that enabled modern advisory tools.
3. Robo Advisors: Provides a comprehensive overview of robo advisor functions, their evolutionary stages, and a detailed comparison against traditional human financial advisors.
4. The Meaning of Trust: Analyzes the multidisciplinary nature of trust, focusing specifically on trust in financial services and the unique dynamics of trusting technology and AI.
5. Trust in Robo Advisors: Evaluates specific mechanisms that influence trust in robo advisors, exploring reputation, user experience, and strategies to overcome algorithm aversion.
6. Conclusion: Synthesizes findings to demonstrate how robo advisors can leverage their inherent advantages in accessibility and transparency to strengthen trust-worthiness.
Keywords
Robo Advisors, Financial Technology, Artificial Intelligence, Initial Trust, Continuous Trust, Algorithm Aversion, Algorithm Appreciation, Financial Services, Customer Adoption, User Experience, Reputation, Portfolio Management, Information Quality, Asset Allocation, Trust-Building.
Frequently Asked Questions
What is the primary focus of this research?
The thesis explores how trust-building factors, such as reputation and user experience, can increase investor trust and customer adoption of automated robo advisor platforms.
What are the central themes discussed in this work?
The work focuses on the intersection of finance, technology, and psychology, specifically examining trust in AI, the differences between human and algorithmic advice, and the stages of trust formation.
What is the core objective of the thesis?
The primary objective is to analyze how robo advisors can improve their product offerings and communication strategies to build trust and compete effectively with traditional financial advisors.
Which scientific methodology is applied here?
The thesis utilizes a literature review methodology to synthesize existing research, complemented by a quantitative assessment of trust-influencing factors derived from industry insights and prior studies.
What topics are covered in the main body?
The main body covers the history of investment, the technological functions of robo advisors, the multidisciplinary definition of trust, and the factors affecting trust in AI-driven financial advice.
Which keywords best characterize this work?
Key terms include Robo Advisors, trust, customer adoption, algorithm aversion, financial services, and user experience.
What is "Algorithm Aversion"?
Algorithm aversion describes the phenomenon where humans tend to trust inferior human judgments over superior but imperfect algorithmic predictions, especially after witnessing the algorithm make a mistake.
How can robo advisors overcome trust barriers?
Robo advisors can mitigate distrust by providing transparent processes, enabling user control (e.g., manual rebalancing), and maintaining high information quality to ensure investors understand the rationale behind automated decisions.
- Citation du texte
- Alina Riecker (Auteur), 2020, Robo Advisors. How to increase trust in Artificial Intelligence compared to traditional financial advisory, Munich, GRIN Verlag, https://www.grin.com/document/978317
