Journal articles: 'Robo-advisor'

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Filiz, Ibrahim, Jan René Judek, Marco Lorenz, and Markus Spiwoks. "Algorithm Aversion as an Obstacle in the Establishment of Robo Advisors." Journal of Risk and Financial Management 15, no. 8 (August 8, 2022): 353. http://dx.doi.org/10.3390/jrfm15080353.

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Within the framework of a laboratory experiment, we examine to what extent algorithm aversion acts as an obstacle in the establishment of robo advisors. The subjects had to complete diversification tasks. They could either do this themselves or they could delegate them to a robo advisor. The robo advisor evaluated all the relevant data and always made the decision which led to the highest expected value for the subjects’ payment. Although the high level of efficiency in the robo advisor was clear to see, the subjects only entrusted their decisions to the robo advisor in around 40% of cases. In this way, they reduced their success and their payment. Many subjects orientated themselves towards the 1/n-heuristic, which also contributed to their suboptimal decisions. As long as the subjects had to make decisions for others, they noticeably made a greater effort and were also more successful than when they made decisions for themselves. However, this did not have an effect on their acceptance of robo advisors. Even when they made decisions on behalf of others, the robo advisor was only consulted in around 40% of cases. This tendency towards algorithm aversion among subjects is an obstacle to the broader establishment of robo advisors.

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Kim, Seongsu David, Marty Cotwright, and Swarn Chatterjee. "Who Are Robo-Advisor Users?" Journal of Finance Issues 18, no. 2 (December 31, 2019): 33–50. http://dx.doi.org/10.58886/jfi.v18i2.2225.

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The purpose of this study is to explore the demand for robo-advising services by analyzing the participants’ behavioral characteristics and investment patterns. With the 2015 Financial Industry Regulatory Authority Investor data, we found that robo-advisor users were younger investors with high risk tolerance, whose self-assessment of financial knowledge is comparatively higher than their actual knowledge, and were independent decision-makers. By controlling for those behavioral attributes of robo-advisor users, we also found that robo-advisor users were reluctant to invest in individual stocks, while they showed the largest preference for investing in pooled investment products such as Exchange Traded Funds. Implications of this study’s findings can be beneficial to financial planning practitioners, academics, and regulators.

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Anshari, Muhammad, Mohammad Nabil Almunawar, and Masairol Masri. "Digital Twin: Financial Technology’s Next Frontier of Robo-Advisor." Journal of Risk and Financial Management 15, no. 4 (April 2, 2022): 163. http://dx.doi.org/10.3390/jrfm15040163.

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This research examines the concept of a robo-advisor with digital twin capabilities for personal financial management. Using an exploratory study, the researchers developed an interactive and interpretive model that analyses the most critical variables to consider when designing the next level of financial robo-advisor through integrating digital twin concepts and applications. Primarily, it conducts an assessment and then reviews the data to propose a model that can serve as a baseline for future research. Related literature was explored, including peer-reviewed journal articles, case studies, periodicals, newspaper articles, and books. This study aims to assess the concept of digital twin (DT) as the next frontier of robo-advisor as a new wave of intelligent financial advisors in supporting the personalisation and customisation of financial technology (FinTech) services and management. Individuals who use a DT-enabled robo-advisor may find a significantly greater value for their financial management and well-being. A robo-advisor with DT enabled will no longer be an ad hoc financial advisory service but will evolve into a comprehensive and dynamic financial advisory service for users. The research presents several critical insights on financial robo-advisory with DT capabilities, transforming and optimising smart financial advisory.

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Cheng, Xusen, Fei Guo, Jin Chen, Kejiang Li, Yihui Zhang, and Peng Gao. "Exploring the Trust Influencing Mechanism of Robo-Advisor Service: A Mixed Method Approach." Sustainability 11, no. 18 (September 9, 2019): 4917. http://dx.doi.org/10.3390/su11184917.

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As a typical application of fintech, the robo-advisor has increasingly gained attention over the last decade. However, most research regarding the robo-advisor has focused on its development issues such as performance improvement and regulation, while limited research has paid attention to trust. This research extends the literature by investigating the trust influencing mechanism of robo-advisors by a mixed method approach. Specifically, we identified six salient trust influencing factors by qualitative interviews and proposed the research model based on trust transfer theory. This model was tested via a survey of 230 investors. Our study finds the significant influencing role of supervisory control and validates the relationships among trust influencing factors, trust in technologies, trust in vendor and trust in robo-advisor. Moreover, several differences between junior investors and senior investors are also found in our research. This study examines trust transfer theory in the new context of the robo-advisor and contributes to further development of this increasingly utilized service.

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Faradynawati, Ida Ayu Agung, and Inga-Lill Söderberg. "Sustainable Investment Preferences among Robo-Advisor Clients." Sustainability 14, no. 19 (October 4, 2022): 12636. http://dx.doi.org/10.3390/su141912636.

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The increasing role of individual investors in supporting the achievement of sustainable development goals through sustainable investment has gained growing interest from financial authorities and the research community. Digitalization in the financial sector, e.g., robo-advisors, enables lay-investors to make sustainable investments in a simple and convenient way. This study investigates whether investment-related attitudes and demographic profiles are related to robo-advisor clients’ sustainable investment choices. This paper describes an empirical study that uses a logistic regression model to investigate sustainable investment preferences at the individual investor level. Cross-sectional data consisting of 27,771 individual investors in Sweden, Norway, and Finland, who purchased investment products through a robo-advisor application, are used in this study. The results suggest that, concerning investment-related attitudes, robo-advisor clients with low-risk tolerance and a short investment horizon are more likely to choose to become sustainable investors. Furthermore, sustainable investments are preferred by robo-advisor clients who are less wealthy, female, and older.

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Kim, Chang Ki. "Robo-Advisor and Risk Management." Global Financial Review 02, no. 01 (March 31, 2021): 47–72. http://dx.doi.org/10.51265/gfr.2021.2.1.47.

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Yi, Tan Zi, Noor Ashikin Mohd Rom, Nurbani Md Hassan, Mohamad Shaharudin Samsurijan, and Andrew Ebekozien. "The Adoption of Robo-Advisory among Millennials in the 21st Century: Trust, Usability and Knowledge Perception." Sustainability 15, no. 7 (March 30, 2023): 6016. http://dx.doi.org/10.3390/su15076016.

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Robo-advisor has become the new personal wealth management and investment method. Nonetheless, certain predicaments are faced by robo-advisor companies as a tech-savvy young group of individuals seems to be less willing to adopt robo-advisory. This study investigates millennials’ adoption of robo-advisory in terms of financial knowledge, trust and usability perception in the 21st century to enhance sustainability. This quantitative study focuses on individuals belonging to the millennial generation who were born between 1981 and 1996. The findings indicate that the millennials who possess financial knowledge, as well as perceived usability and trust have a significant positive effect on the willingness to embrace robo-advisory as a tool for wealth management. The higher the financial knowledge of an individual, the more likely they are willing to adopt a robo-advisor. Government may provide appropriate avenues to enhance financial knowledge, and credible and user-friendly platforms with resources to boost the millennials’ usage of robo-advisors for their wealth management. With robust artificial intelligence, robo-advisory continues to support users, especially millennials, through three dimensions of sustainable development: environment, society, and economy.

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Hasanah, Eneng Nur, Sudarso Kaderi Wiryono, and Deddy P. Koesrindartoto. "Financial Robo-Advisor: Learning from Academic Literature." Jurnal Minds: Manajemen Ide dan Inspirasi 10, no. 1 (February 14, 2023): 17–40. http://dx.doi.org/10.24252/minds.v10i1.33428.

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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.

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Sabir, Asrar Ahmed, Iftikhar Ahmad, Hassan Ahmad, Muhammad Rafiq, Muhammad Asghar Khan, and Neelum Noreen. "Consumer Acceptance and Adoption of AI Robo-Advisors in Fintech Industry." Mathematics 11, no. 6 (March 8, 2023): 1311. http://dx.doi.org/10.3390/math11061311.

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Artificial intelligence (AI) has provided significant help in many fields of life. This study proposed a framework that helped in understanding customers’ attitudes about the adoption of Robo-advisors. The role of the Technology Readiness Index moderated as one of the primary relationships. A total of 208 potential users of Robo-advisor services provided the data that confirmed the validity of the model. This model provided the input for structural equation modeling and analysis of the study hypotheses. The results indicated that consumers showed positive attitudes about Robo-advisor services, with the moderating effect of Technology Readiness Index dimensions, namely, contributors and inhibitors. Perceived ease of use, perceived usefulness, and perceived convenience influenced consumers in developing positive attitudes about this service. Financial businesses can design better AI Robo-advisor services to fulfill the requirements of a wide range of consumers. This proposed framework contributes to the consumers’ understanding of behavioral intentions for the use of Robo-advisors in FinTech.

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Junhui, WENG. "Comparison of Fund Robo-advisor and Manual Decision-making Focused on China Securities Examples." Advances in Economics, Management and Political Sciences 44, no. 1 (November 10, 2023): 140–50. http://dx.doi.org/10.54254/2754-1169/44/20232210.

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As a major achievement in the field of Fintech, intelligent investment has gradually become popularized in China due to its low-cost and low-barrier advantages. However, few studies have compared the income gap between robo-advisor and manual decision-making. As a well-known securities company in China, China Securities is at the forefront of the development of intelligent investment and has achieved good results in fund business and intelligent investment business. This study compares fund products managed by China Securities' fund manager with those on the robo-advisor trading platform "Qingting Butler". By comparing its cumulative rate of return during the period and daily yield, calculating the Adjusted R-square, average value, and overall variance, using the unary linear regression analysis method, to explore the gap between manual decision-making and robo-advisor. Based on the comparison between the two major decisions, it will preliminarily explore the development of robo-advisor in the Chinese market and provide a quantitative basis for investors when facing artificial and intelligent investment choices.

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Hohberger, Tobias. "Mit einem Robo Advisor Geld anlegen." Bankfachklasse 43, no. 3 (March 2021): 12–19. http://dx.doi.org/10.1007/s35139-021-0568-8.

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Rogowski, Wojciech. "Świt wirtualnego doradztwa finansowego (robo-advisor)." e-mentor 2017, no. 4(71) (October 31, 2017): 53–63. http://dx.doi.org/10.15219/em71.1315.

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Rudolf, Bernhard. "Robo Advisor erleichtert Vermittlern die Arbeit." Versicherungsmagazin 67, no. 4 (March 27, 2020): 14–15. http://dx.doi.org/10.1007/s35128-020-0331-x.

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Figà-Talamanca, Gianna, Paola Musile Tanzi, and Eleonora D’Urzo. "Robo-advisor acceptance: Do gender and generation matter?" PLOS ONE 17, no. 6 (June 29, 2022): e0269454. http://dx.doi.org/10.1371/journal.pone.0269454.

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Robo-advice technology refers to services offered by a virtual financial advisor based on artificial intelligence. Research on the application of robo-advice technology already highlights the potential benefit in terms of financial inclusion. We analyze the process for adopting robo-advice through the technology acceptance model (TAM), focusing on a highly educated sample and exploring generational and gender differences. We find no significant gender difference in the causality links with adoption, although some structural differences still arise between male and female groups. Further, we find evidence that generational cohorts affect the path to future adoption of robo-advice technology. Indeed, the ease of use is the factor which triggers the adoption by Generation Z and Generation Y, whereas the perceived usefulness of robo-advice technology is the key factor driving Generation X+, who need to understand the ultimate purpose of a robo-advice technology tool before adopting it. Overall, the above findings may reflect that, while gender differences are wiped out in a highly educated population, generation effects still matter in the adoption of a robo-advice technology tool.

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Irawan, Bambang, Maimun Maimun, Liky Faizal, and Muhammad Zaki. "TINJAUAN HUKUM EKONOMI SYARIAH TENTANG PENGGUNAAN ROBO ADVISOR DALAM ISLAMIC WEALTH MANAGEMENT (Studi Pustaka Robo Advisor di Indonesia Tahun 2022)." AL-MANHAJ: Jurnal Hukum dan Pranata Sosial Islam 5, no. 2 (July 27, 2023): 1157–74. http://dx.doi.org/10.37680/almanhaj.v5i2.3214.

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Robo advisor is a technology that can assist investors in managing their portfolios. In the context of Islamic finance, transactions and business relationships must ensure compliance with Sharia principles in order to preserve wealth and meet needs. The use of robo advisors in this context has not been extensively researched. This study aims to determine the use of robo advisors in Islamic wealth management. The research method used is literature review with a normative approach and descriptive-analytical analysis. The data used are from literature on the use of robo advisors and Islamic wealth management in Indonesia over the past five years. Based on the workings of robo advisors, various benefits and limitations are found in their use. The conclusion of the analysis of maṣlaḥah (public interest), functionality, and the urgency of using robo advisors shows that their use in Islamic wealth management can be considered permissible as a means for retail investors and beginners to safeguard wealth (ḥifẓ al-mâl) by applying filters for Sharia-compliant investment instruments and adhering to prohibitions on riba (usury), gharar (uncertainty), maysir (gambling), and haram (forbidden). The research recommendation is to conduct further research on the technical aspects and algorithms used by robo advisors in selecting Sharia-compliant investment instruments, and a sound framework of supervision and regulation is needed to ensure the compliance of robo advisors with Sharia principles and the protection of investors' interests.

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Belanche, Daniel, Luis V. Casaló, and Carlos Flavián. "Artificial Intelligence in FinTech: understanding robo-advisors adoption among customers." Industrial Management & Data Systems 119, no. 7 (August 12, 2019): 1411–30. http://dx.doi.org/10.1108/imds-08-2018-0368.

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Purpose Considering the increasing impact of Artificial Intelligence (AI) on financial technology (FinTech), the purpose of this paper is to propose a research framework to better understand robo-advisor adoption by a wide range of potential customers. It also predicts that personal and sociodemographic variables (familiarity with robots, age, gender and country) moderate the main relationships. Design/methodology/approach Data from a web survey of 765 North American, British and Portuguese potential users of robo-advisor services confirm the validity of the measurement scales and provide the input for structural equation modeling and multisample analyses of the hypotheses. Findings Consumers’ attitudes toward robo-advisors, together with mass media and interpersonal subjective norms, are found to be the key determinants of adoption. The influences of perceived usefulness and attitude are slightly higher for users with a higher level of familiarity with robots; in turn, subjective norms are significantly more relevant for users with a lower familiarity and for customers from Anglo-Saxon countries. Practical implications Banks and other firms in the finance industry should design robo-advisors to be used by a wide spectrum of consumers. Marketing tactics applied should consider the customer’s level of familiarity with robots. Originality/value This research identifies the key drivers of robo-advisor adoption and the moderating effect of personal and sociodemographic variables. It contributes to understanding consumers’ perceptions regarding the introduction of AI in FinTech.

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Liu, Ruilin. "Research on Financial Risks of Robo-Advisor Platforms." E3S Web of Conferences 218 (2020): 01035. http://dx.doi.org/10.1051/e3sconf/202021801035.

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With the vigorous development of China’s Internet finance and financial technology, Roboadvisors have become more and more popular among investors. This new investment and financial management model that relies on Internet platforms, artificial intelligence, quantitative trading technologies will have a greater impact on the capital market once the financial risks caused. How to standardize the Robo-advisory platform, effectively control the financial risks in the investment process and protect the interests of investors is an urgent problem in the Robo-advisory industry. Firstly, this article analyzes the development status of domestic Robo-advisory platforms. Then it puts forward suggestions on how the Robo-advisory industry can face the dual pressures of survival and profit in the context of stricter supervision by analyzing various financial risks and causes of the platform. It aims to promote the sound development of the investment advisory market in the context of financial technology.

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Spitschan, Daniel, and Yves Wüppenhorst. "Der Robo Advisor reift zum intelligenten Kollegen." Bankmagazin 69, no. 9 (August 31, 2020): 46–49. http://dx.doi.org/10.1007/s35127-020-0315-5.

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Gohil, Mr Mihir, Mr Akhil Shetty, and Mr Sandesh Akre. "Robo-Advisory in India: The Upcoming Game Changer in Indian Financial Markets." MET MANAGEMENT REVIEW 08, no. 01 (2020): 50–57. http://dx.doi.org/10.34047/mmr.2020.8108.

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A Robo-Advisor is a digital platform that provides financial planning services which is automated and algorithm-driven and it has little or no supervision of a human. Globally the robo-advisory fintech service is expected to grow at a CAGR of 53.54% from 2020 to 2025. The primary advantage of robo-advisors is that the costs are very low (0.2%-0.5%) compared to traditional advisors (around 1%-2%). Another advantage is their 24x7 accessibility since it is on a digital platform. The clients can execute the trades by pressing a few buttons rather than calling the advisor, explaining the needs and the returns. The third advantage is there is no minimum client investment needed in robo-advisors since traditional advisors require minimum ticket size of atleast Rupees 2.5 Lakhs. With increased penetration of the internet in the country, faster growing population with expected share of more than 33% of the millennials and the youth in total population, shifting perspective of Indian investors from fixed deposits to mutual funds and equities, and growing acceptance of FinTech services, Robo-advisors are the perfect game changers in the Indian investment scenario in the next 20 years.

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Kim, A. Reum, and Hae Kyung Yang. "An Exploratory Study on the Robo－advisor Users’ Decision Making Process." Korean Business Education Review 36, no. 1 (February 28, 2021): 55–83. http://dx.doi.org/10.23839/kabe.2021.36.1.55.

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Choi, Wonwoo, and Doojin Ryu. "A Case Study on the Hybrid Robo-advisor." Korea Business Review 22, no. 3 (August 31, 2018): 33–52. http://dx.doi.org/10.17287/kbr.2018.22.3.33.

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Quiroga-Garcia, Raquel, Mar Arenas-Parra, and Héctor Rico-Pérez. "Scientific Development of Robo-Advisor: A Bibliometric Analysis." Review of Economics and Finance 20 (2022): 776–86. http://dx.doi.org/10.55365/1923.x2022.20.87.

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Caballero-Fernández, Rodrigo, Klender Aimer Cortez-Alejandro, and David Ceballos-Hornero. "Fintech y la transformación de los servicios financieros utilizando robo-advising: Una revisión a la literatura." Vinculatégica EFAN 6, no. 2 (December 18, 2020): 1087–104. http://dx.doi.org/10.29105/vtga6.2-522.

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Este artículo pretende cumplir con dos objetivos: el primero es realizar una revisión a la literatura relacionada con las plataformas de robo advisor, clasificando los distintos estudios que se han realizado para tener una perspectiva general de dichas investigaciones. El segundo objetivo es mostrar las características principales de plataformas de robo advisor para entender su funcionamiento. La metodología utilizada para el primer objetivo fue por medio de una búsqueda avanzada en scopus donde se clasificaron los diversos estudios por tema. Para el Segundo objetivo se hizo una búsqueda exhaustiva de diferentes plataformas de Estados Unidos para entender la manera en la que gestionaban la cartera de inversión. Los robo advisors son una pieza clave en la estructura Fintech con gran potencial de crecimiento derivado a los beneficios que otorga a las personas minoristas y que, por lo tanto, se han empezado a realizar estudios significativos con relación a las plataformas, sin embargo, aún queda un gran camino por recorrer.

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Tsai, Shuo-Chang, and Chih-Hsien Chen. "Exploring the Innovation Diffusion of Big Data Robo-Advisor." Applied System Innovation 5, no. 1 (January 24, 2022): 15. http://dx.doi.org/10.3390/asi5010015.

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The main objective of this study was to explore the current practical use of an AI robo-advisor algorithmic technique. This study utilizes Roger’s innovation diffusion theory as a basis to explore the application of robo-advisors for forecasting in the stock market by using an abductive reasoning approach. We used literature reviews and semi-structured interviews to interview representatives of fund companies to see if they had adopted AI big data forecasting models to invest in stock selection. This study summarizes the big data stock market forecasts of the literature. According to the summary, the accuracy of the prediction models of these scholars ranged from 52% to 97%, with the prediction results of the models varying significantly. Interviews with 21 representatives of these fund companies revealed that the stock market forecast model of big data robo-advisors have not become a reference basis for fund investment candidates, mainly because of the unstable model prediction rate, and the lack of apparent relative advantages and observability, as well as being too complex. Thus, from the view of innovation diffusion, there is a lack of diffusion for the robo-advisor. Knowledge occurs when an individual is exposed to the existence of innovation, and gains some understanding of how it functions. Thereby, when investors become more familiar with neural network-like stock prediction models, this novel AI stock market forecasting model is expected to become another indicator of technical analysis in the future.

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Zhang, Lixuan, Iryna Pentina, and Yuhong Fan. "Who do you choose? Comparing perceptions of human vs robo-advisor in the context of financial services." Journal of Services Marketing 35, no. 5 (February 3, 2021): 634–46. http://dx.doi.org/10.1108/jsm-05-2020-0162.

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Purpose This study aims to investigate the differences in consumers’ perceptions of trust, performance expectancy and intention to hire between human financial advisors with high/low expertise and robo-advisors. Design/methodology/approach Three experiments were conducted. The respondents were randomly assigned to human advisors with high/low expertise or a robo-advisor. Data were analyzed using MANCOVA. Findings The results suggest that consumers prefer human financial advisors with high expertise to robo-advisors. There are no significant differences between robo-advisors and novice financial advisors regarding performance expectancy and intention to hire. Originality/value This pioneering study extends the self-service technology adoption theory to examine adoption of robo-advisors vs human financial advisors with different expertise levels. To the best of the authors’ knowledge, it is among the first studies to address multi-dimensionality of trust in the context of artificial intelligence-based self-service technologies.

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Fan, Lu, and Swarn Chatterjee. "The Utilization of Robo-Advisors by Individual Investors: An Analysis Using Diffusion of Innovation and Information Search Frameworks." Journal of Financial Counseling and Planning 31, no. 1 (March 30, 2020): 130–45. http://dx.doi.org/10.1891/jfcp-18-00078.

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This study examines the roles of internal and external search characteristics and attitudinal factors in investors' decisions to utilize robo-advisor-based platforms. Using the 2015 state-by-state National Financial Capability Study and Investor Survey, this study finds that the need to free up time, higher risk tolerance, higher subjective financial knowledge, and higher amounts of investable assets were positively associated with individual investors' adoption of robo-advisors. Additionally, the results from the interaction model indicates that individuals under 65 with a higher risk tolerance and greater perceived investment knowledge were more likely to use robo-advisors. Implications of the key findings for scholars, practitioners, and industry leaders are included.

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Au, Cam-Duc, Lars Klingenberger, Martin Svoboda, and Eric Frère. "Business Model of Sustainable Robo-Advisors: Empirical Insights for Practical Implementation." Sustainability 13, no. 23 (November 24, 2021): 13009. http://dx.doi.org/10.3390/su132313009.

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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.

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Schütz, Tobias, Cindy Schröder, and Carsten Rennhak. "Acceptance of Automated Investment Advisory: An Experimental Study of the Relevance of Trust Attributes of a Robo-Advisor." Die Unternehmung 77, no. 2 (2023): 185–201. http://dx.doi.org/10.5771/0042-059x-2023-2-185.

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The influence of trust on the adherence to investment recommendations in the context of robo-advisors is under-researched. This relationship needs to be better understood because robo-advice lacks a critical element of trust: human interaction. Theory suggests that ability, integrity, and benevolence are key factors in building trust in human advisors. Using an experimental study design, our research examines the relationship between a robo-advisor's trust attributes and the acceptance of its investment advice. The results show that trust in a robo-advisor increases the propensity to follow its recommendations. While ability and integrity are significant, benevolence is not. The study contributes to the research on technology acceptance, trust, and the adoption of technology-based recommendations by improving the understanding of the relationship between trust and the acceptance of automated investment recommendations.

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Lim, Hyejin, Doojin Ryu, and Heejin Yang. "Economic Analysis of Robo-advisor Industries: A Case Study." korean management review 47, no. 3 (June 30, 2018): 725–49. http://dx.doi.org/10.17287/kmr.2018.47.3.725.

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Fulk, Martha, John E. Grable, Kimberly Watkins, and Michelle Kruger. "Who uses robo-advisory services, and who does not?" Financial Services Review 27, no. 2 (November 14, 2023): 173–88. http://dx.doi.org/10.61190/fsr.v27i2.3390.

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The purpose of this study was to compare the demographic, attitudinal, and behavioral character- istics of U.S. consumers in their current and expected use of robo-advisory services, traditional financial planning services, or a combination of the two services. Findings showed a difference between those who used robo-advisory services and those who used traditional financial planning services. Overall, those who used a traditional financial planner were older and reported higher levels of net worth, while users of robo-advisors, on average, reported lower levels of net worth. In addition, those who used traditional financial planning services reported a larger percentage of their total net worth from an inheritance, whereas a lower percentage of net worth from an inheritance was reported by robo-advisor users. Results showed that users of robo-advisory services generally (1) had lower income, (2) had lower net worth, (3) had received no or less inheritance, and (4) were less impulsive financially.

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Lisauskiene, Nomeda, and Valdone Darskuviene. "Linking the Robo-advisors Phenomenon and Behavioural Biases in Investment Management: An Interdisciplinary Literature Review and Research Agenda." Organizations and Markets in Emerging Economies 12, no. 2 (December 22, 2021): 459–77. http://dx.doi.org/10.15388/omee.2021.12.65.

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Technological advancements bring continuous changes into the investment industry. The paper aims to provide insights on future research agenda based on a review of the current stance of research on the links between the Robo-advisors phenomenon and behavioural biases of individual investors. A qualitative investigation method has been applied for literature review on Robo-advisors and their impact on behavioural biases. The key findings indicate that Robo-advisors can help users to make better informed and less biased decisions. However, Robo-advisors activate the investors’ automatic system processes. The resulting passive investment approach could lead to alienation of the investors from the stock market, decreasing their understanding of the investment process that could widen a gap between different clusters of investors. The paper makes several contributions to the literature. First, it provides arguments on why a dual process theoretical framework in the relationship between financial advisory and investment behavioural biases is applicable. Second, it studies the Robo-advisor phenomenon and proposes a comprehensive definition of Robo-advisors. Third, the literature review suggests drivers of the Robo-advisors effect on the changes of behavioural biases as a future research direction.

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Ji, Gwangwoon. "Insurance Distribution using Robo-advisors and Legal Issues." Korean Insurance Law Association 16, no. 3 (October 31, 2022): 187–213. http://dx.doi.org/10.36248/kdps.2022.16.3.187.

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The environment surrounding the insurance industry is likely to become even more digital than nowadays. In particular, robo-advisors are expected to be used effectively in the insurance distribution channel. Although there is no legal definition for robo-advisors in the related insurance act in Korea. Traditional insurance distribution channels can use them as sales aids. In this case, the robo-advisor type is likely to be used as a product recommendation type and an information provision type. In light of the current technological development of robo-advisors, it is not possible to equate robo-advisors with AI insurance consultant. In this case, as examined in the examples of the United States, Australia, and Germany, if insurance intermediaries utilize robo-advisors to sell insurance products within existing legal systems, there will be problems in applying existing insurance law provisions. It should be noted that the same regulatory principles apply because there is no such rule. Considering that it is premature to establish a regulatory system for AI insurance distribution channels that make fully automated decisions without any human intervention, insurance sales channels utilizing robo-advisors should be considered as regulations for traditional distribution channels. The same regulations should be set. Considering this point, this paper defines robo-advisor as an algorithm-based ``automated online insurance product advisory device''. Also the paper proposed a plan to regulate this under the insurance industry regulation. On the other hand, this paper also considered measures to establish model standards for insurance distribution using robo-advisors and discipline them in the form of self-regulation. In addition, non-face-to-face sales and operator intervention are permitted with an algorithm-based system that evaluates customer opinions through algorithms and big data analysis using computer programs as an improvement plan for related acts and makes personal recommendations based on them. A proposal was made to prepare a provision to define it as an insurance product advisory device. In the case of this kind of regulation, in addition to fulfilling the duty of explanation imposed on insurance distribution channels under the existing legal system, the details of the duty of explanation to distribution channels that use robo-advisors should be determined in accordance with the principle of good faith. Therefor it is proposed in the paper to take measures for a plan to add within the scope of the obligation. Finally, regarding the judicial responsibility system, if liability arises due to incomplete distributions using robo-advisors, the person behind it will be held responsible for tortious acts associated with breach of obligations under the principle of good faith within the current legal system.

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Chen, Xiaofei, Shujun Ye, and Chao Huang. "Cluster-Based Mutual Fund Classification and Price Prediction Using Machine Learning for Robo-Advisors." Computational Intelligence and Neuroscience 2021 (December 17, 2021): 1–14. http://dx.doi.org/10.1155/2021/4984265.

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The rise of FinTech has been meteoric in China. Investing in mutual funds through robo-advisor has become a new innovation in the wealth management industry. In recent years, machine learning, especially deep learning, has been widely used in the financial industry to solve financial problems. This paper aims to improve the accuracy and timeliness of fund classification through the use of machine learning algorithms, that is, Gaussian hybrid clustering algorithm. At the same time, a deep learning-based prediction model is implemented to predict the price movement of fund classes based on the classification results. Fund classification carried out using 3,625 Chinese mutual funds shows both accurate and efficient results. The cluster-based spatiotemporal ensemble deep learning module shows better prediction accuracy than baseline models with only access to limited data samples. The main contribution of this paper is to provide a new approach to fund classification and price movement prediction to support the decision-making of the next generation robo-advisor assisted by artificial intelligence.

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Thottoli, Mohammed Muneerali, Badria Hamed Alruqaishi, and Arockiasamy Soosaimanickam. "Robo academic advisor: Can chatbots and artificial intelligence replace human interaction?" Contemporary Educational Technology 16, no. 1 (January 1, 2024): ep485. http://dx.doi.org/10.30935/cedtech/13948.

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Purpose: Chatbots and artificial intelligence (AI) have the potential to alleviate some of the challenges faced by humans. Faculties frequently swamped with teaching and research may find it difficult to act in a parental role for students by offering them individualized advice. Hence, the primary purpose of this study is to review the literature on chatbots and AI in light of their role in auto-advising systems. The authors aimed to gain insights into the most pertinent topics and concerns related to robo academic advisor and identify any gaps in the literature that could serve as potential avenues for further research. Design/methodology/approach: The research employs a systematic literature review and bibliometric techniques to find 67 primary papers that have been published between 1984 and 2023. Using the Scopus database, the researchers built a summary of the literature on chatbots and AI in academic advice. Findings: Chatbot applications can be a promising approach to address the challenges of balancing personalized student advising with automation. More empirical research is required, especially on chatbots and other AI-based advising systems, to understand their effectiveness and how they can be integrated into educational settings. Research limitations/implications: This research’s sample size may restrict its findings’ generalizability. Furthermore, the study’s focus on chatbots may overlook the potential benefits of other AI technologies in enhancing robo academic advising systems. Future research could explore the impact of robo academic advisors in diverse societal backgrounds to gain a more comprehensive understanding of their implications. Practical implications: Higher educational institutions (HEIs) should establish a robo academic advising system that serves various stakeholders. The system’s chatbots and AI features must be user-friendly, considering the customers’ familiarity with robots. Originality/value: This study contributes to a better understanding of HEIs’ perceptions of the adoption of chatbots and AI in academic advising by providing insightful information about the main forces behind robo academic advising, illuminating the most frequently studied uses of chatbots and AI in academic advising.

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Maeng, Soo Seok. "Investor Protection Plans in Security Transaction through Robo-Advisor(RA)." Commercial Law Review 38, no. 4 (February 29, 2020): 253–82. http://dx.doi.org/10.21188/clr.38.4.7.

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Xue, Jingming, En Zhu, Qiang Liu, and Jianping Yin. "Group Recommendation Based on Financial Social Network for Robo-Advisor." IEEE Access 6 (2018): 54527–35. http://dx.doi.org/10.1109/access.2018.2871131.

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Jung, Dominik, Verena Dorner, Christof Weinhardt, and Hakan Pusmaz. "Designing a robo-advisor for risk-averse, low-budget consumers." Electronic Markets 28, no. 3 (December 1, 2017): 367–80. http://dx.doi.org/10.1007/s12525-017-0279-9.

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Tokic, Damir. "BlackRock Robo-Advisor 4.0: When artificial intelligence replaces human discretion." Strategic Change 27, no. 4 (July 2018): 285–90. http://dx.doi.org/10.1002/jsc.2201.

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Huang, Zengyi, Chang Che, Haotian Zheng, and Chen Li. "Research on Generative Artificial Intelligence for Virtual Financial Robo-Advisor." Academic Journal of Science and Technology 10, no. 1 (March 26, 2024): 74–80. http://dx.doi.org/10.54097/30r2kk80.

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This research explores the intersection of artificial intelligence and finance, focusing on the emergence of intelligent investment advisers, commonly known as Robo-advisers (RAs). These RAs utilize robust computer models and artificial intelligence algorithms to deliver personalized asset management investment plans for users. Notably, Wealthfront is highlighted as a prominent platform in this field, offering automated investment management services aimed at optimizing investment returns. The study investigates the impact of users' past investment performance on their adoption of intelligent advisers, considering factors such as previous defaults and recent investment performance. It reveals that frequent adjustments to the use of intelligent advisers may hinder long-term investment objectives, emphasizing the importance of consistent usage to fully capitalize on their benefits. Furthermore, the research emphasizes the significance of transparency, user-friendly interaction design, and tailored financial services to foster user trust and enhance the optimization of intelligent advisers' design.

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Saputra, Yogi, Ela Siti Nurpajriah, Siti Kustinah, and Novianti Indah Putri. "Perancangan Strategis Sistem Informasi Financial Planning Management dengan Robo-Advisor." Jurnal Accounting Information System (AIMS) 6, no. 2 (September 17, 2023): 127–36. http://dx.doi.org/10.32627/aims.v6i2.787.

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The pace of information technology growth in the modern day is so rapid that financial fraud has now spread to renewable technologies. The only approach to guarantee future financial security is to invest. However, the vast array of investment options—including stocks, gold, and other investments—often makes it difficult for people to make the best decision. Many millennials are still apprehensive about investing. This results from a lack of understanding about effective investing. A machine learning information system is necessary to assist in the selection of investment products in order to boost the community's and millennials' interest in investing. The Markowitz and K-Nearest Neighbor algorithms were used in the system's construction. Finding recommendations for investment portfolios that match the risk profile can be aided using the K-Nearest Neighbor approach, which is a machine learning technique. Based on a comparison of the sharpe ratio findings from system calculations and manual calculations, the accuracy level of the Markowitz and KNN approaches, which was set at 99.15%, was established.

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Ibrahim, Muhammad Azman, Rafiatul Adlin Mohd Ruslan, Mohamad Fariz Abdullah, and Arman Hj Ahmad. "Navigating the Future: Exploring the Nexus between Robo-Advisor Service Quality and Customer Satisfaction." Information Management and Business Review 15, no. 3(SI) (September 17, 2023): 351–58. http://dx.doi.org/10.22610/imbr.v15i3(si).3491.

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Despite the plethora of research studies conducted on service quality and customer satisfaction in the realm of artificial intelligence, there remains a significant gap in the literature when it comes to robo advisors. Robo advisors, automated platforms that provide algorithm-based financial advice and investment management, are gaining popularity as an alternative to traditional financial advisors. Numerous research studies have been conducted concerning service quality and customer satisfaction in the context of artificial intelligence, yet none have specifically addressed robo-advisors. This research aims to fill the gap and shed light on the crucial aspects of service quality and customer satisfaction within the context of robo advisors. The study will examine the key determinants of service quality offered by robo advisors, considering factors such as the accuracy of financial advice, user interface design, response time, transparency, and communication efficiency. Furthermore, the research seeks to investigate the factors influencing customer satisfaction in their interactions with robo advisors. It will explore the users' perception of robo advisors' reliability, trustworthiness, personalized experience, and the level of control they have over their investments. The outcomes of this research are expected to contribute valuable insights into understanding the strengths and weaknesses of robo-advisors concerning service quality and customer satisfaction. This study is expected to benefit financial service providers, policymakers, and investors by identifying the areas of improvement for robo advisors and enhancing the overall customer experience.

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Waliszewski, Krzysztof, and Marta Zięba-Szklarska. "Robo-advisors as Automated Personal Financial Planners – SWOT Analysis." Finanse i Prawo Finansowe 3, no. 27 (September 30, 2020): 155–73. http://dx.doi.org/10.18778/2391-6478.3.27.09.

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Automatic financial consulting (robo-advice) is a financial innovation in the area of personal financial planning, and in particular investment consulting classified as fin-tech (financial technology). The main disadvantage of traditional investment advice is limited availability due to the required amount of assets and high management costs. These disadvantages are answered by robo-advice, using artificial intelligence and algorithms without the participation of a physical adviser, thereby reducing or abolishing minimal assets and reducing costs [Kocianski 2016]. The purpose of the article is to analyze strengths and weaknesses as well as opportunities and threats of robo-advisors, especially when compared to traditional financial advisors. This paper is an analysis of a history, regulations, application, functionality and development of Robo-Advice. The concept of Robo-Advice was critically analysed, with presentation of current strength and weaknesses, as well as opportunities and threats. We aimed showing that Robo-Advice should be innovative, feature a new approach, and should transform the current financial consulting. There are still, however, many opportunities and challenges in this field, which await discovering. The article uses the SWOT analysis method, analysis of the literature and reports. As indicated by the analysis, robo-advisors are not a threat to traditional financial advisors, but their complement, which makes the hybrid model connecting a physical advisor assisted by technological solutions (robo-advisors) most likely. This thesis is also confirmed by the small scale of robo-advisors’ activity compared to traditional advisors measured by the market penetration rate (in 2023 about 2% assets under management).

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Guo, Ruihan. "Fintech and Digital Transformation: Accelerating Innovation in Financial Services." Highlights in Business, Economics and Management 15 (June 28, 2023): 140–44. http://dx.doi.org/10.54097/hbem.v15i.9330.

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This paper focuses on the progress made in the field of financial technology as a result of digital development. The study highlights several major areas of financial technology and analyzes its risk level and future prospects. The research indicates that the areas of online payment and P2P lending are advancing successfully, and their respective legal and regulatory systems are relatively well-established. However, the development of Robo-Advisor and Blockchain faces significant challenges due to uncertainty and a lack of clarity regarding regulatory frameworks. The emergence of ChatGPT has disrupted the traditional understanding of Robo-Advisor, presenting new opportunities for development. On the other hand, the development of products based on blockchain technology poses a great risk as it is challenging to accurately determine their intrinsic value. Overall, digital development has greatly impacted the financial technology industry, and the study emphasizes the importance of understanding the risks associated with new technologies. The research recommends that regulators should work towards creating clear and comprehensive regulatory frameworks to promote responsible innovation in the financial technology sector.

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Son, Min Hee. "An Exploratory Study on Factors Influencing Consumers’ Trust on and Intention to Use Robo-advisor : Focusing on the Mediating Role of Trust on Robo-advisor." E-Business Studies 22, no. 5 (October 31, 2021): 69–92. http://dx.doi.org/10.20462/tebs.2021.10.22.5.69.

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Chauhan, Vanshika. "Analysis of the Effectiveness of Robo- Advisors in Wealth Management." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (April 30, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem32701.

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This thesis analyzes the effectiveness of robo-advisors in wealth management. Robo-advisors are automated investment platforms that utilize algorithms to build and manage investment portfolios. Their rise has challenged the traditional model of human financial advisors. This research explores the potential of robo-advisors and their impact on wealth management. Key Areas of Investigation: Performance and Returns: The study examines how robo-advisors perform compared to traditional advisors in terms of risk-adjusted returns and asset allocation strategies. It investigates whether robo-advisors can deliver competitive returns with lower fees. Accessibility and Affordability: A core feature of robo-advisors is their accessibility. The research explores how robo-advisors cater to a wider range of investors, including those with lower account minimums and lower fees compared to traditional advisors. Client Suitability and Risk Tolerance: The thesis analyzes how effectively robo-advisors assess client suitability and risk tolerance through questionnaires and automated algorithms. It compares this approach to the in-depth financial planning offered by human advisors. Technology and Automation: The research delves into the technological aspects of robo- advisors, including portfolio rebalancing, tax-loss harvesting, and automated investment strategies. It evaluates the efficiency and effectiveness of these automated features. Human Interaction and Financial Planning: While robo-advisors offer automation, some platforms provide access to human advisors for complex financial planning needs. The study explores the role of human advisors in conjunction with robo-advisors and how this hybrid model might enhance wealth management. Methodology: This thesis will likely employ a combination of research methods, including: Literature review: Analyzing existing research on robo-advisors, performance data, and wealth management strategies. Case studies: Examining specific robo-advisor platforms and their functionalities. Comparative analysis: Comparing the performance and features of different robo-advisors. Client surveys: Understanding investor experiences and preferences regarding robo-advisors. Expected Outcomes: By analyzing these factors, the thesis aims to provide a comprehensive evaluation of the effectiveness of robo-advisors in wealth management. The findings will offer valuable insights for: Investors: Understanding the potential benefits and limitations of using robo-advisors for wealth management. Financial institutions: Assessing the role of robo-advisors within their wealth management offerings. Regulatory bodies: Considering any regulatory implications associated with the growth of robo- advisors. Ultimately, this research will contribute to a better understanding of how robo-advisors are transforming the landscape of wealth management.

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ROSA SUSILA, JAYA KUNCARA, PUJO LAKSONO, and MUHAMMAD AFIT. "Rancang Bangun Robo-Advisor untuk Pendanaan Rumah Syariah Berbasis Aplikasi Bergerak." MIND Journal 7, no. 1 (June 29, 2022): 98–110. http://dx.doi.org/10.26760/mindjournal.v7i1.98-110.

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ABSTRAKPembiayaan properti berbasis syariah saat ini meningkat seiring dengan kesadaran ummat muslim dalam menjalankan agamanya. Persetujuan pembelian properti syariah lebih diniatkan untuk membantu ummat mendapatkan rumah namun ini menjadi kendala karena ditemukan ada cicilan yang macet setelah beberapa waktu. Perlu dibangun sistem pendukung keputusan yang berfokus pada pendampingan dan rekomendasi skema pembiayaan properti syariah. Parameter masukan sebanyak 12 variabel profil calon pembeli yang diambil dari formulir surat persetujuan pembelian rumah (SPPR), sedangkan targetnya adalah persetujuan pembelian yang diperoleh dari sistem kecerdasan artifisial. Untuk mendapatkan model yang optimal, dilakukan perbandingan 3 model, yaitu logaritmic regression, decision tree, dan random forest. Random forest memiliki tingkat akurasi tertinggi, yaitu 90,831%. Framework Flask digunakan sebagai aplikasi web yang dikonversi menjadi aplikasi bergerak.Kata kunci: kecerdasan artifisial, robo-advisor, sistem pendukung keputusan, random forest, flaskABSTRACTSharia-based property financing is currently increasing along with the awareness of Muslims in practicing their religion. The approval for the purchase of sharia property is intended to help the community get a house, but this is an obstacle because it is found that there are installments that are stuck after some time. It is necessary to build a decision support system that focuses on mentoring and recommending sharia property financing schemes. The input parameters are 12 profile variables of prospective buyers taken from the house purchase approval letter form (SPPR), while the target is purchase approval obtained from an artificial intelligence system. To get the optimal model, a comparison of 3 models was made, namely logarithmic regression, decision tree, and random forest. Random forest has the highest accuracy rate, which is 90.831%. The Flask framework is used as a web application that is converted into a mobile application.Keywords: artificial intelligence, robo-advisor, decision support system, random forest, flask

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Yang, Young Sik, and Soo Seok Maeng. "A Legal study to enhance the usefulness of Robo-Advisor Testbed." Commercial Law Review 37, no. 3 (November 30, 2018): 333–68. http://dx.doi.org/10.21188/clr.37.3.8.

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Zhang, Budan, Zhongyu Chen, Weilu Li, and Jingyu Li. "The robo-advisor for ETF funds in China: influences and determinants." Procedia Computer Science 221 (2023): 1029–35. http://dx.doi.org/10.1016/j.procs.2023.08.084.

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Mohammad Hatta, Mohammad Firdaus, Nur Diniey Ezzati Zainorin, Syuhada Jalaludin, Norzanah Mat Nor, and Suriana Ramli. "FINANCIAL ROBO-ADVISOR SAVINGS2U FOR SHARIAH-COMPLIANT SAVINGS AND INVESTMENT APPLICATION." Journal of Information System and Technology Management 7, no. 29 (December 1, 2022): 70–85. http://dx.doi.org/10.35631/jistm.729006.

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The growing usage of artificial intelligence such as Robo-Advisor has become one of the biggest developments in the global economy and reduces the level of poverty in the country with a safe, affordable and frictionless environment. In practice, Malaysians have low awareness and not financial discipline about personal savings and financial management. Most of Malaysian people have problems with their retirement planning because they really rely on their EPF funds as their primary source of income. The purpose of this product is to create awareness about the importance of savings and investment for all generations, reduce future financial issues faced by consumers, and encourage people to build wealth based on customer preference. As a result, customers will be more likely to develop a good habit of saving their money for the future and achieving their financial goals. This study used quantitative and secondary data which is collected from The World Bank website. We develop an application of financial Robo-Advisor known as Savings2u that focus on savings and Shariah compliance investment features based on customer risk preference such as conservative or aggressive then let the money grow digitally. In conclusion, Savings2u will help the customer to create and manage the portfolio automatically using the data provided. On the other hand, an increase in total savings would have a beneficial effect on the economic growth, which includes significant investments and an improvement in GDP of the country. Furthermore, countries with deeper and established financial systems also experienced stronger economic growth.

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Semko, Roman. "Machine learning for robo-advisors: testing for neurons specialization." Investment Management and Financial Innovations 16, no. 4 (December 9, 2019): 205–14. http://dx.doi.org/10.21511/imfi.16(4).2019.18.

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The rise of robo-advisor wealth management services, which constitute a key element of fintech revolution, unveils the question whether they can dominate human-based advice, namely how to address the client’s behavioral biases in an automated way. One approach to it would be the application of machine learning tools during client profiling. However, trained neural network is often considered as a black box, which may raise concerns from the customers and regulators in terms of model validity, transparency, and related risks. In order to address these issues and shed more light on how neurons work, especially to figure out how they perform computation at intermediate layers, this paper visualizes and estimates the neurons’ sensitivity to different input parameters. Before it, the comprehensive review of the most popular optimization algorithms is presented and based on them respective data set is generated to train convolutional neural network. It was found that selected hidden units to some extent are not only specializing in the reaction to such features as, for example, risk, return or risk-aversion level but also they are learning more complex concepts like Sharpe ratio. These findings should help to understand robo-advisor mechanics deeper, which finally will provide more room to improve and significantly innovate the automated wealth management process and make it more transparent.

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Journal articles on the topic 'Robo-advisor'

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