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

A Systematic Review on Robo-Advisors in Fintech

Technology has been the main driver for the financial sector. Fintech tools emerged to support the provision of financial services, especially Robo-Advisors (RAs), which allow the automation of the investment management process. The main functions are the creation of an investment portfolio and allocating assets, and daily management of investment portfolios based on a machine learning algorithm. This paper presents a literature review to summarise the importance of RAs in the financial sectors as well as the perception of investors. Also, this literature review presents the main algorithm’s characteristics behind the intelligence of RAs and the primary concerns. The Scopus and Web of Science databases revealed 114 research papers. It was found that investor acceptance of these technologies is affected by aspects of high volatility, which includes financial markets. The algorithm\u27s mathematical models and system architecture might be improved so that this instrument can better suit the needs of investors

A Systematic Review on Robot-Advisors in Fintech

Martins, M. N., & Ashofteh, A. (Accepted/In press). A Systematic Review on Robot-Advisors in Fintech. Paper presented at 23.ª Conferência da Associação Portuguesa de Sistemas de Informação, Beja, Portugal.Technology has been the main driver for the financial sector. Fintech tools emerged to support the provision of financial services, especially Robot-Advisors (RAs), which allow the automation of the investment management process. The main functions are the creation of an investment portfolio and allocating assets, and daily management of investment portfolios based on a machine learning algorithm. This paper presents a literature review to summarise the importance of RAs in the financial sectors as well as the perception of investors. Also, this literature review presents the main algorithm’s characteristics behind the intelligence of RAs and the primary concerns. The Scopus and Web of Science databases revealed 114 research papers. It was found that investor acceptance of these technologies is affected by aspects of high volatility, which includes financial markets. The algorithm's mathematical models and system architecture might be improved so that this instrument can better suit the needs of investors.authorsversioninpres

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

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

Artificial intelligence – a key success factor for wealth management industry

The Private Banking & Wealth Management (PWM) industry is generally seen as embodying traditional, old- fashioned and even archaic values. Upheld for centuries, its business model, which is based on intensive, comprehensive and discreet personal interactions between financial advisors and wealthy clients, is put to the test today. In today's dynamic and highly connected world, a large number of HNWIs (High Net Worth Individuals) want faster and more convenient value propositions and a cutting-edge digital experience – a trend that the pandemic has amplified many times over. In order to meet the increased expectations of this clientele, private banks and other institutions in the sector are increasingly investing in a number of new technologies and tools, artificial intelligence (AI) taking a leading place among them. In addition to enabling a more complete and qualitative satisfaction of user needs, AI promises benefits for PWM companies in a number of other areas: risk management, compliance, cost reduction, etc

Artificial Intelligence in Asset Management

Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways

Manipulation of Online Reviews: Analysis of Negative Reviews for Healthcare Providers

There is a growing reliance on online reviews in today’s digital world. As the influence of online reviews amplified in the competitive marketplace, so did the manipulation of reviews and evolution of fake reviews on these platforms. Like other consumer-oriented businesses, the healthcare industry has also succumbed to this phenomenon. However, health issues are much more personal, sensitive, complicated in nature requiring knowledge of medical terminologies and often coupled with myriad of interdependencies. In this study, we collated the literature on manipulation of online reviews, identified the gaps and proposed an approach, including validation of negative reviews of the 500 doctors from three different states: New York and Arizona in USA and New South Wales in Australia from the RateMDs website. The reviews of doctors was collected, which includes both numerical star ratings (1-low to 5-high) and textual feedback/comments. Compared to other existing research, this study will analyse the textual feedback which corresponds to the clinical quality of doctors (helpfulness and knowledge criteria) rather than process quality experiences. Our study will explore pathways to validate the negative reviews for platform provider and rank the doctors accordingly to minimise the risks in healthcare

Operations Management

Global competition has caused fundamental changes in the competitive environment of the manufacturing and service industries. Firms should develop strategic objectives that, upon achievement, result in a competitive advantage in the market place. The forces of globalization on one hand and rapidly growing marketing opportunities overseas, especially in emerging economies on the other, have led to the expansion of operations on a global scale. The book aims to cover the main topics characterizing operations management including both strategic issues and practical applications. A global environmental business including both manufacturing and services is analyzed. The book contains original research and application chapters from different perspectives. It is enriched through the analyses of case studies

Adoption of AI-based Information Systems from an Organizational and User Perspective

Artificial intelligence (AI) is fundamentally changing our society and economy. Companies are investing a great deal of money and time into building corresponding competences and developing prototypes with the aim of integrating AI into their products and services, as well as enriching and improving their internal business processes. This inevitably brings corporate and private users into contact with a new technology that functions fundamentally differently than traditional software. The possibility of using machine learning to generate precise models based on large amounts of data capable of recognizing patterns within that data holds great economic and social potential—for example, in task augmentation and automation, medical diagnostics, and the development of pharmaceutical drugs. At the same time, companies and users are facing new challenges that accompany the introduction of this technology. Businesses are struggling to manage and generate value from big data, and employees fear increasing automation. To better prepare society for the growing market penetration of AI-based information systems into everyday life, a deeper understanding of this technology in terms of organizational and individual use is needed. Motivated by the many new challenges and questions for theory and practice that arise from AI-based information systems, this dissertation addresses various research questions with regard to the use of such information systems from both user and organizational perspectives. A total of five studies were conducted and published: two from the perspective of organizations and three among users. The results of these studies contribute to the current state of research and provide a basis for future studies. In addition, the gained insights enable recommendations to be derived for companies wishing to integrate AI into their products, services, or business processes. The first research article (Research Paper A) investigated which factors and prerequisites influence the success of the introduction and adoption of AI. Using the technology–organization–environment framework, various factors in the categories of technology, organization, and environment were identified and validated through the analysis of expert interviews with managers experienced in the field of AI. The results show that factors related to data (especially availability and quality) and the management of AI projects (especially project management and use cases) have been added to the framework, but regulatory factors have also emerged, such as the uncertainty caused by the General Data Protection Regulation. The focus of Research Paper B is companies’ motivation to host data science competitions on online platforms and which factors influence their success. Extant research has shown that employees with new skills are needed to carry out AI projects and that many companies have problems recruiting such employees. Therefore, data science competitions could support the implementation of AI projects via crowdsourcing. The results of the study (expert interviews among data scientists) show that these competitions offer many advantages, such as exchanges and discussions with experienced data scientists and the use of state-of-the-art approaches. However, only a small part of the effort related to AI projects can be represented within the framework of such competitions. The studies in the other three research papers (Research Papers C, D, and E) examine AI-based information systems from a user perspective, with two studies examining user behavior and one focusing on the design of an AI-based IT artifact. Research Paper C analyses perceptions of AI-based advisory systems in terms of the advantages associated with their use. The results of the empirical study show that the greatest perceived benefit is the convenience such systems provide, as they are easy to access at any time and can immediately satisfy informational needs. Furthermore, this study examined the effectiveness of 11 different measures to increase trust in AI-based advisory systems. This showed a clear ranking of measures, with effectiveness decreasing from non-binding testing to providing additional information regarding how the system works to adding anthropomorphic features. The goal of Research Paper D was to investigate actual user behavior when interacting with AI-based advisory systems. Based on the theoretical foundations of task–technology fit and judge–advisor systems, an online experiment was conducted. The results show that, above all, perceived expertise and the ability to make efficient decisions through AI-based advisory systems influence whether users assess these systems as suitable for supporting certain tasks. In addition, the study provides initial indications that users might be more willing to follow the advice of AI-based systems than that of human advisors. Finally, Research Paper E designs and implements an IT artifact that uses machine learning techniques to support structured literature reviews. Following the approach of design science research, an artifact was iteratively developed that can automatically download research articles from various databases and analyze and group them according to their content using the word2vec algorithm, the latent Dirichlet allocation model, and agglomerative hierarchical cluster analysis. An evaluation of the artifact on a dataset of 308 publications shows that it can be a helpful tool to support literature reviews but that much manual effort is still required, especially with regard to the identification of common concepts in extant literature