No full-text available
To read the full-text of this research,
you can request a copy directly from the author.
BlackRock Robo-Advisor 4.0: When artificial intelligence replaces human discretion
Abstract
BlackRock, the largest US investment management firm, has started to replace human stock‐pickers with the full automated investment program, based on self‐learning artificial intelligence algorithms. This is the first high‐profile case in the financial industry where artificial intelligence replaces human discretion. The advanced version of BlackRock Robo‐Advisor can also be customized to perform within the strategic planning framework. Human discretionary decision‐making jobs are at risk of being replaced by machines.
... In the past ten years, robo-advice has been a topic of increasing interest in practice and in theory, and a promising technology for the financial sector. The very definition of RAs indicates the involvement of humans as "little to none" [59] or nonexistent [60], [61]. Yet, our findings suggest that human involvement is here to stay, at least that of advisors. ...
... Several types of RAs exist today, significantly differing in maturity and service offering. For instance, RAs are defined by various stages of technological advancement, from simple questionnaire-based algorithms to artificially intelligent and conversational RAs [61], [68], [69]. To date, there is no consensus amongst researchers and professionals on the impact of RAs on human advisors. ...
... To date, there is no consensus amongst researchers and professionals on the impact of RAs on human advisors. Some papers highlight the potential of the RA to replace human advisors [44], [45], [60], [61], [66], [69], [70]. In contrast, many papers emphasize the importance of human advisor involvement in the advisory process [43], [54], [63], [71], [72], [73]. ...
The growth in intelligent machines entering the workplace continues to challenge organizations’ digital transformation efforts. Various applications of simple to complex algorithms allow computerized systems to take on and automate an increasing number of tasks previously undertaken by human workers. Robo-advisors (RAs) in the financial sector serve as an excellent example of technological versatility and what is to come. RAs are platforms defined by a set of algorithms that offer wealth management advice online. To understand how human workers are affected by progressively intelligent machines, this article looks at the impact of RAs on human financial advisors. Through a systematic review, we present state of the art literature and examine interactions of human and digital component. Our findings illustrate possible automation scenarios for financial advisors working with RAs, the human value added, and the skills the future workforce will require. We further suggest a future research agenda. This article adds to the digital transformation literature at the intersection of workplace automation, service provision, and human–machine interaction. The aim is to provide and provoke new ideas for successful implementation and use of intelligent machines along with skilled people in a supportive work environment.
... Second, because individual investors have suffered as a result of private equity firms engaging in illegal activities (e.g., yield manipulation, fraudulent transactions, and toxic asset purchases), negative perceptions of funds sold in the financial sector have spread, leading to reduced trust in the traditional financial products and managers (Tokic, 2018). Although robo-advisors are driving innovation in financial services using AI technologies, it is nearly impossible for financial services to operate without customer trust. ...
... While humans can do such investment deliberations intuitively, they can also be automated through suitable investment robots. A robo-advisor automatically makes the above three decisions according to changing market conditions (Tokic, 2018). The global COVID-19 pandemic has led to an inevitable surge in the contactless market as a megatrend. ...
... Given the growing market volatility and uncertainty, scholarly interest in robo-advisors that allow stable financial investments based on objective data analyses and verified algorithmic strategies is thriving. Extant studies on roboadvisors in fintech have often examined why such technologies are attracting attention (Belanche et al., 2019), which robo-advisors are popularly chosen by consumers for use (Shanmuganathan, 2020) and investments (Jung et al., 2018), and the future of such technologies (Tokic, 2018). Since these studies have focused on the technological usefulness of robo-advisors and their benefits to users, there has been less attention on the cognitive barriers that cause intrinsic psychological anxiety in consumers from the viewpoint of attitudes or intentions to use such technology. ...
In fintech, robo-advisors are a helpful technology for users desiring to use financial services remotely; as such, robo-advisors are being used by an increasing number of users. However, service providers of this artificial intelligence (AI)-based technology still have challenges to solve, such as issues with security, privacy, and distrust. In addition to technological benefits, we argue that if the service providers were to consider the risk-sensing behavioral attitudes of users toward AI-based robo-advisors, then more users may be attracted to this technology. By simultaneously employing the unified theory of acceptance and use of technology (UTAUT) and the theory of reasoned action (TRA), we develop a conceptual model and propose a series of hypotheses related to users' adoption of robo-advisors in fintech services. Specifically, we argue that the antecedents (i.e., performance expectancy, effort expectancy, social influence, and facilitating conditions) affect the positive attitudes that individual investors hold toward robo-advisors, and we claim that the TRA-related factors (i.e., perceived security, perceived privacy, and trust) play vital roles in encouraging the use of robo-advisors. Using large-scale survey data from 638 Chinese users having experience with robo-advisor services, we empirically tested our framework using the structural equation modeling approach. The results clearly support the proposed hypotheses concerning the direct and indirect effects of various predictors, such as performance expectancy, effort expectancy, social influence, facilitating conditions, perceived security, and perceived privacy, on user attitudes toward robo-advisors and their intention to adopt such fintech services. In addition, our results demonstrate that the majority of these relationships are indirect by virtue of the mediating roles of attitude, trust, and facilitating conditions. This study contributes to the understanding of users' adoption of robo-advisors by combining UTAUT and TRA, which is useful for exploring the relationships between attitudes and behavioral intentions to use as well as the interrelationships among security, privacy, and trust.
... The first strand of literature concerns an early attempt to classify robo-advisors. Based on the original work by Deloitte (2016), the first classification moves from the idea that the wealth management industry may employ robo-advisors with a different degree of intensity in active and passive management strategies (Tokic, 2018). In passive investment strategies, robo-advisors assist investors in asset allocation (robo-advisor 1.0), risk management (robo-advisor 2.0), and proposing portfolio rebalancing (robo-advisor 3.0). ...
... Concerning active management strategies, investors benefit from fully automated investments based on artificial intelligence algorithms as well as proposing automatic asset shifts in clients' accounts (robo-advisor 4.0). An example of a robo-advisor 4.0 is BlackRock (Tokic, 2018). ...
... Intelligent investment advisors excel not only in investment allocation and transaction execution but also in assisting investors in managing their emotional biases. This field represents one of the most extensive applications of AI technology within the financial sector [74]. In 2008 driven by Wall Street's enthusiasm for AI and big data, AI investment banking in asset management emerged as a growing market demand for wealth management. ...
... An example is the AI-powered equity exchange-traded fund (AIEQ), which leverages IBM's artificial intelligence, Watson, for fund management which consistently outperforms the S&P 500 [76]. The democratization of information and the increasing complexity of the investment landscape have driven the shift towards data-driven, AI-based approaches [74], which has led to the replacement of human advisors in actively managed equity funds. For instance, BlackRock, the world's largest asset manager, has initiated the replacement of human stock-pickers with a fully automated AI engine, called Aladin [76]. ...
Artificial intelligence (AI)-based models have emerged as powerful tools in financial markets, capable of reducing investment risks and aiding in selecting highly profitable stocks by achieving precise predictions. This holds immense value for investors, as it empowers them to make data-driven decisions. Identifying current and future trends in multi-class forecasting techniques employed within financial markets, particularly profitability analysis as an evaluation metric is important. The review focuses on examining studies conducted between 2018 and 2023, sourced from three prominent academic databases. A meticulous three-stage approach was em-ployed, encompassing the systematic planning, conduct, and analysis of the selected studies. Specifically, the analysis emphasizes technical assessment, profitability analysis, hybrid modeling, and the type of re-sults generated by models. Articles were shortlisted based on inclusion and exclusion criteria, while a rigorous quality assessment through ten quality criteria questions, utilizing a Likert-type scale was employed to ensure methodological robustness.We observed that ensemble and hybrid models with long short-term memory (LSTM) and support vector machines (SVM) are being more adopted for financial trends and price prediction. Moreover, hybrid models employing AI algorithms for feature engineering have great potential at par with ensemble techniques. Most studies only employ performance metrics and lack utilization of profitability metrics or investment or trading strategy (simulated or real-time). Similarly, research on multi-class or output is severely lacking in financial forecasting and can be a good avenue for future research.
... Oppure si pensi, ancora, alla diffusione della consulenza automatizzata in campo finanziario -trattasi della figura del c.d. robo-advisor, una sorta di consulente finanziario intelligente, a supporto della personalizzazione dei servizi e della gestione della tecnologia finanziaria (FinTech) -che ha indotto lo IOSCO (International Organizations of Securities Commissions, 2017) a proporre l'elaborazione di apposite Linee Guida per la mappatura dei rischi connessi alla diffusione di tali strumenti (Di Porto, 2017). In ogni caso, la robo-advisoryconsulenza automatizzata sugli investimenti basata sul web ( trasparenza e generale incapacità o riluttanza a confrontarsi con le questioni relative agli investimenti (Cheng, Guo, Chen et al., 2019;Jung, Dorner, Weinhardt et al., 2018;Morana, Gnewuch, Jung et al., 2020;Oehler, Horn & Wendt, 2022;Tokic, 2018;Zhang, Pentina, & Fan, 2021). ...
The "Big Data Governance and Legal Aspects" booklet delves into the governance challenges and legal implications surrounding Big Data in an era marked by the rapid evolution of Emerging and Disruptive Technologies (EDTs). It highlights the essential processes for managing data throughout its lifecycle, emphasizing collection, storage, and analysis while ensuring data security and ethical usage. The text also navigates the balance between privacy and national security, exploring the necessity for ethical and legal frameworks that can address these evolving threats.
The publication investigates the dual potential of Big Data: maximizing value for national security and minimizing privacy risks. It discusses the complexity of reconciling privacy with national security, particularly in the context of CBRNe threats. The book includes a comprehensive examination of Open Source Intelligence (OSINT) methods and the deployment of demonstrators to monitor global asymmetric threats. By analyzing regulatory landscapes and presenting case studies, it offers an integrated approach to understanding Big Data's role in contemporary security and defense, providing a valuable resource for policy makers, researchers, and security professionals.
... For investors seeking stable returns, low risk, and minimal intervention in investments, robo-advisors in passive investment strategies only need to assist investors in asset allocation, risk management, and proposing portfolio rebalancing strategies. In terms of active management strategies, roboadvisors employ fully automated investments based on artificial intelligence algorithms and propose automatic asset shifts in clients' accounts [26]. ...
Robo-advisors have emerged as a significant innovation in investment management, offering automated financial advice to investors. However, user acceptance remains a challenge, particularly among those with limited investment experience. This paper explores the potential of large language models (LLMs) to enhance the interaction attributes of robo-advisor products and increase acceptance among novice investors. The study contributes to the existing literature by exploring the application of LLMs in robo-advisors, supplementing the exploration of interaction design, and systematically reviewing the service processes of current robo-advisor products. Findings suggest that existing robo-advisor products have room for improvement in interaction attributes and algorithmic mechanisms. Through theoretical exploration, this paper proposes methods for optimizing robo-advisor products by integrating LLMs. In conclusion, this research lays the groundwork for designing robo-advisor products with integrated LLM functionality, offering theoretical references for practitioners and researchers in financial technology. Future research directions include exploring user expectations and conducting controlled experiments to analyze the impact of LLM integration on user decisions.
... (2021) conducted interviews and focus group discussions with industry experts and managers in fintech companies. Case studies majorly utilize data from company records (n=4) and provide an indepth analysis of the performance and utilization of Robo-advisors (Phoon & Koh, 2018;Rossi & Utkus, 2020;Shanmuganathan, 2020;Tokic, 2018). Apart from this, a few studies refrained from specifying the source of data collection (Agarwal & Chua, 2020;Wexler & Oberlander, 2020). ...
... The [35] discusses BlackRock's move towards integrating artificial intelligence (AI) into their financial advisory services through their "Robo-Advisor 4.0." This new system will replace human discretion in decision making processes and rely on algorithms and data analysis to make investment decisions. ...
... Human financial advisers can account for investor spending and stable income status, whereas robo-advisers only provide advice and recommendations (Capponi et al., 2022;Tokic, 2018). But recommendations and advice by robo-advisers are assumed to be objective and highly structured (Climescu, 2021;Huang et al., 2023). ...
... As AI-poweredmachines are capable of replicating human efforts [45,46] it has a potential to dramatically affect workers, businesses, nations, economies, and society as a whole [44]. AI has been examined in various disciplines and industries [47], including marketing [48] and healthcare [49]. ...
This study presents a comprehensive overview of the development process and the latest trends in technology management (TM), laying a robust foundation for further advancements in this domain. To achieve this, we analysed 1944 TM articles from the Web of Science database and 2642 articles from Scopus, spanning the last 20 years. Employing methodologies that involve scientific knowledge graphs and bibliometrics, we analysed diverse aspects such as changes in the annual publication of articles; geographical distribution among countries, institutions, disciplines, and authors; keyword co-occurrence and clusters; and timezone view. Our findings reveal a significant surge in TM's growth in recent years, showcasing its highly promising potential. The USA is the frontrunner in contributing to TM research, followed by China and the UK. TM research is relatively concentrated in the UK, while it appears more dispersed in China. The University of Cambridge had the highest volume of research, and the disciplines of Business, Management, Engineering, and Computer Science occupied the top spots. As TM evolves, a possible challenge could be the emergence of new authoritative authors. Second, TM's vibrant landscape is characterised by hotspots such as innovation, technology strategy, technology acquisition, technology application, technology standards, and sustainable development. Among these, information and medical technologies stand out as the most frequently referenced technologies. Third, the trends in TM are as follows: innovation is subdivided into technological innovation and open innovation, bibliometric analysis and patent analysis have become pivotal methods for knowledge management, the scope of TM has expanded from internal organisational processes to encompass external aspects, and TM is gradually evolving into a mature science, with its focus transitioning from macro to micro and becoming more profound and detailed. Last, Industry 4.0, artificial intelligence, big data, and the IoT represent the latest frontier technologies in the realm of TM.
... Applying ML and deep learning, Erica can provide tailor-made services [28]. The BlackRock Robo-Advisor 4.0 also uses AI and ML and can outperform human stock-pickers in the task of buying stocks whose estimated intrinsic value is higher than the market value [29]. ...
Many financial technology (fintech) applications have incorporated interactive computer agents to act as mediators between the user and the fintech system. This paper provides a comprehensive review of interactive fintech agents from technological and social science perspectives. First, we explain the general fintech landscape and define interactive fintech agents. Next, we review the major technologies involved in creating fintech: (1) artificial intelligence and machine learning, (2) big data, (3) cloud computing, and (4) blockchain; as well as the specific key technologies enabling the following aspects of interactive fintech agents: (1) intelligence, (2) understanding of users, and (3) manifestation as social actors. Following the technology review, we examine issues and theories related to human-fintech agent interaction in the following areas: (1) agents’ understanding of users, (2) agents’ manifestation as social actors (via embodiment, emotion, and personality), and (3) users’ social interaction with agents. Finally, we suggest directions for future research on fintech agents.
... As AI-poweredmachines are capable of replicating human efforts [45,46] it has a potential to dramatically affect workers, businesses, nations, economies, and society as a whole [44]. AI has been examined in various disciplines and industries [47], including marketing [48] and healthcare [49]. ...
Technology and its continuous advancement facilitate human beings to get rid of their criticality and limitation. Applied artificial intelligence (AAI) is one of the latest forms that delimited the limitation of human beings. Smartwatch acts as an applied artificial intelligence to assist various patients to check medical care without going to hospital and physicians. This (three) multiple-study research focused on the intention to use, purchase, and their satisfaction and spread positive word of mouth among others in the Ghanaian. To investigate these issues two renowned theories were underpinned- TAM theory and the Stimulus-Organism-Response (S–O-R). Total 550, 320, and 170 respondents were interviewed with Google forms due to COVID-19 using social media. AI-enabled smartwatch considering Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Perceived Credibility (PC), Perceived Self-Efficacy (PSE), and Perceived Financial Cost (PFC) were significant on intention to adoption and adoption intention on actual purchase. The final study showed device quality, its service level, their usage experience, perceived value, and the extent to which the satisfied customers made positive word of mouth to their friends and family, colleagues and followers. This research is significant in understanding the usage of AI-enabled smartwatches as a device doctor or electronic doctor (e-doctor).
... Questioning the interests of investors in roboadvisors should shed light on the future, since one of the important changes in consumer life created by the COVID-19 pandemic over the last two years is the distance service purchase. Researching the marketing and finance literature shows that studies are mostly theoretical (Wexler and Oberlander 2021;Tokic 2018), comparing trust and performance expectations for an advisor (human) and a robo-advisor (Zhang et al. 2021), examining the experience of meeting investors with a robo-advisor (Belanche et al. 2020;Jung et al. 2018a;Hildebrand and Bergner 2021;Hohenberger et al. 2019;Seiler andFanenbruck 2021, Wu andGao 2021), and trying to reveal the robo-advisor user profile (Fulk et al. 2018;Cheng 2021). D' Acunto et al. (2019) focused on the differences between investors who adopted and did not adopt robo-advisor in their studies and concluded that investors who had not adopted robo-advisor before obtained higher fund returns as a result of diversifying their investments after adopting robo-advisor. ...
Robo-advisor is one of the most up-to-date innovations in the financial world. So, the number of experienced users is also very limited, yet. The study investigates to reveal the components that determine the usage intention for robo-advisor by private pension investors who have not yet experienced the product in order to determine the real potential of robo-advisor. This study assimilated elements of the UTAUT “performance expectancy, effort expectancy, facilitating conditions, and social influence” and extended the model by adding in three vital elements “a need for interaction with service employees, financial risk tolerance, and trust.” A survey was conducted involving 265 investors in Turkey who have private pension investments and have experienced digital banking. The outcomes of this study indicate that the factors affecting robo-advisor usage intention in private pension investments are performance expectancy, social impact, facilitating conditions, financial risk tolerance, and trust. Also, trust positively affects financial risk tolerance. However, effort expectancy and the need for interaction with service employees have no effect on the intention to use a robo-advisor. This research can support robo-advisor service providers and regulators in designing services and improving the adoption of robo-advisors. The study should also shed light on the future of distance services.
... The emergence of new technology such as Artificial Intelligence (AI) can make robo-advisors provide even more cost-effective portfolio management solutions for investors (Lee, Kwon, & Lim, 2017). One example of AI implementation is the ability to recreate human decision-making in a robo-advisory solution with the help of self-learning AI algorithms (Tokic, 2018). Robo-Advisor solutions are typically based on the lack of human interactions in hopes consumers will comprehend and retain the information given without the need to ask questions (Salo, & Haapio, 2017). ...
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.
... And even today, predictions are still being made about the potential displacement of jobs in the future [6]. With the threat of Artificial Intelligence replacing human skills on the horizon [7], it follows that even white-collar workers are reasonably afraid of technology. Insecurity over unemployment threat brings stress towards the employees and inhibits their capabilities, leading to overall worse performance [8], [9]. ...
In this paper, we have examined the effects of Robotic Process Automation (RPA) on four factors that dictate employees' attitudes and behaviour-fear of job loss, wages, job characteristics, and job satisfaction. There is currently a limited body of knowledge on the topic of how RPA could affect these factors, and consequently, business performance. Drawing from the results of extant studies on RPA, on automation in general, and on theories of human resources management, we concluded that RPA adoption had mixed results on its end-users. From the findings, we emphasised that how businesses implement the adoption also plays a key role in influencing their employees attitudes and behaviours. Further research on this topic should focus on building a quantifiable framework to investigate the relationships further.
... Robo-advisor, as intelligent advisors or automated investment management system, aims to help investors manage their investment to maximize their proft by creating and managing their portfolios in different markets such as bonds and shares (Tokic, 2018). Traditionally, banks or wealth management consultants help customer to achieve their investment goals. ...
... A study conducted on Mechanical Turk (MTurk) from fall 2015 show a reduced exhibit of impulsivity bias (Fulk et al., 2018). Robo-advisor 4.0 has almost taken over jobs performed by some of the human experts in the field and there is also a possibility that robo-bosses may take over a few high-level managerial jobs (Tokic, 2018). Implementation of private information security is needed with the evolution of big data owing to the ongoing threats of information security; in addition, protection of personal data is a big concern (Zhang, 2018). ...
Purpose
The purpose of this study is to gauge the awareness and perception of Indian individual investors about a new fintech innovation known as robo-advisors in the wealth management scenario. Robo-advisors are comprehensive automated online advisory platforms that help investors in managing wealth by recommending portfolio allocations, which are based on certain algorithms.
Design/methodology/approach
This is a phenomenological qualitative study that used five focussed group discussions to gather the stipulated information. Purposive sampling was used and the sample comprised investors who actively invest in the Indian stock market. A semi-structured questionnaire and homogeneous discussions were used for this study. Discussion time for all the groups was 203 min. One of the authors moderated the discussions and translated the audio recordings verbatim. Subsequently, content analysis was carried out by using the NVIVO 12 software (QSR International) to derive different themes.
Findings
Factors such as cost-effectiveness, trust, data security, behavioural biases and sentiments of the investors were observed as crucial points which significantly impacted the perception of the investors. Furthermore, several suggestions on different ways to enhance the awareness levels of investors were brought up by the participants during the discussions. It was observed that some investors perceive robo-advisors as only an alternative for fund/wealth managers/brokers for quantitative analysis. Also, they strongly believe that human intervention is necessary to gauge the emotions of the investors. Hence, at present, robo-advisors for the Indian stock market, act only as a supplementary service rather than a substitute for financial advisors.
Research limitations/implications
Due to the explorative nature of the study and limited participants, the findings of the study cannot be generalised to the overall population. Future research is imperative to study the dynamic nature of artificial intelligence (AI) theories and investigate whether they are able to capture the sentiments of individual investors and human sentiments impacting the market.
Practical implications
This study gives an insight into the awareness, perception and opinion of the investors about robo-advisory services. From a managerial perspective, the findings suggest that additional attention needs to be devoted to the adoption and inculcation of AI and machine learning theories while building algorithms or logic to come up with effective models. Many investors expressed discontent with the current design of risk profiles of the investors. This helps to provide feedback for developers and designers of robo-advisors to include advanced and detailed programming to be able to do risk profiling in a more comprehensive and precise manner.
Social implications
In the future, robo-advisors will change the wealth management scenario. It is well-established that data is the new oil for all businesses in the present times. Technologies such as robo-advisor, need to evolve further in terms of predicting unstructured data, improvising qualitative analysis techniques to include the ability to gauge emotions of investors and markets in real-time. Additionally, the behavioural biases of both the programmers and the investors need to be taken care of simultaneously while designing these automated decision support systems.
Originality/value
This study fulfils an identified gap in the literature regarding the investors’ perception of new fintech innovation, that is, robo-advisors. It also clarifies the confusion about the awareness level of robo-advisors amongst Indian individual investors by examining their attitudes and by suggesting innovations for future research. To the best of the authors’ knowledge, this study is the first to investigate the awareness, perception and attitudes of individual investors towards robo-advisors.
... Second, while most research perceives the external environment exerting a force on a focal firm (Karimi & Walter, 2015), an interesting alternative angle is to understand how IT and DCs produce disruptions and changes in entire industries. These changes may be in the form of innovative digital business models that emerge as a result a firm's DC activation (e.g., online streaming services, ridesharing) (Song, Lee, & Khanna, 2016), or through the fused potential of IT and DCs in transforming how business activities are performed and instigating competitive and regulatory changes (e.g., automated stock trading agents) (Tokic, 2018;Wall, 2018). ...
Over the past twenty years the dynamic capabilities view (DCV) has gained prominence in the IS field as a theoretical perspective from which to explain competitive advantage in turbulent environments. While there are quite a few review studies of DCs in the strategic management domain, research on DCs in the IS area has not been synthesized nor critically analyzed. The result is that the role which IT plays in the DCV remains largely ambiguous and the way we think and conduct IS research on DCs unquestioned. Addressing this, we conducted a critical review of DCs in IS research based on 136 papers. Our review provides a synthesis of contemporary knowledge on DCs emphasizing the role of IT in this research, and a critical analysis of the assumptions underlying this literature. In addition, we develop a minimum DC definition for future research as a solution to the conceptual issues that we uncovered via the critical analysis. We further leverage the remaining findings of our critical review by providing a detailed research agenda for future investigations on DCs by IS scholars.
... Leading investment management firms, such as Black Rock, are already using Robo-advisors, i.e. AI tools for algorithmic trading based on technical and fundamentals, as well as rebalancing portfolios based on customer profile information (Tokic, 2018). Robo-advisors need to mitigate investor's biases while performing risk analysis and profiling the investors. ...
Both academic and nonacademic literature is evolving following the oscillating development of artificial intelligence (AI) and computing power's evolution in their application to finance and financial markets. The limits to economic growth encountered after the financial crisis and, successively, the recent pandemic bursts have posed new challenges to AI‐related technologies. The surveyed publications have given hope in harvesting improved efficiency, new data, information, advisory and management services, risk mitigation, and some unanswered questions regarding negative impacts on sustainable growth and increasing economic welfare.
... This observation is coherent with industry observations made before the coronavirus pandemic, "The performance of automated asset allocation and investment strategies under market downturns and severe volatility also remains untested" (Fitch Ratings, 2017). This underlying theme is underlined by Tokic (2018), using the example of BlackRock Robo-Advisor 4.0, stressed the potential limitations of the self-learning artificial intelligence programs when dealing with unexpected systematic events. However, there was also evidence to suggest that respondents were also concerned with elements incorporating privacy and security risk, which can be referred to as the third dimension of risk, namely bias. ...
Antecedents of intention to adopt artificial intelligence and robo‐advisory services from the German private investors' perspective may guide future adoption behavior. This paper raises the question to what extent German investors' is willing to use robo‐advisory services instead of a human advisor to manage their investments. The exploratory study identified the following constructs that impact the intention to use artificial intelligence to invest: perceived risk, perceived usefulness, perceived ease of use, social influences, and intention to use. Findings from this study can help inform marketers when developing strategies to foster awareness and the adoption of robo‐advisors.
... Passive funds tracked by Morning Star are worth more than those run by humans (The Economist, 2019, Oct 5). Within an investment firm such as BlackRock (a global leader), higher fees come from active funds, but clients are moving out from active funds and moving into passive funds managed by robots (Tokic, 2018). Some industry experts argue that ML might reverse this trend only temporarily as insights from ML are copied by other fund managers as they develop ML capabilities. ...
Artificial Intelligence (AI) is creating a rush of opportunities in the financial sector, but financial organizations need to be aware of the risks inherent in the use of this technology. Financial organizations are integrating AI in their operations: in‐house, outsourced, or ecosystem‐based. The growth of AI‐based fintech firms has encouraged several mergers and acquisitions among financial service providers and wealth managers as they grapple with volatility, uncertainty, complexity, and ambiguity. AI's unique promise of combined cost reduction and increased differentiation makes it generally attractive across the board. However, perhaps other than fraud detection, these benefits depend on the scale of an organization. Risk arises from nonrepresentative data, bias inherent in representative data, choice of algorithms, and human decisions, based on their AI interpretations (and whether humans are involved at all once AI has been unleashed). Risk reduction requires a vigilant division of labour between AI and humans for the foreseeable future.
... These jobs constitute many jobs in the financial services industry (Ryll et al., 2019). Some scholars expect that soon even human discretion-based jobs will be impacted in the financial sector (Tokic, 2018). However, other authors consider that the total number of jobs will not be impacted (Autur, 2015), even within the financial services industry (Chui and Malhotra, 2018), perhaps because staff will be retrained for higher value-added and more sophisticated functions such as customer interaction (Dhar et al., 2018;Dicamillo, 2019). ...
Although FinTechs and incumbents are applying artificial intelligence (AI) differently, they both expect that the status‐quo will likely be maintained through collaboration rather than competition. Both perceive BigTechs as a strategic threat given their AI capabilities and their entrance into financial services. Incumbents are experimenting with more different kinds of AI than FinTechs: FinTechs use the technologies for new products and services while incumbents are using them for incremental innovations to existing products and services. The incumbents expect that adopting AI will lead to a loss in jobs of 9% over the next 10 years and, because these companies represent a large percentage of the workforce (median company size surveyed has more than 10,000 employees), this loss in jobs cannot be compensated by the 19% increase in jobs provided by existing FinTechs (median company size surveyed has less than 50 employees). AI can reduce and increase risk, and most incumbents and FinTechs agree that there will be no effect on risk at the organizational level but that there will be an increase in risk at the societal level. While both FinTechs and incumbents agree on the relative importance of legal and human hurdles and consider the biggest hurdle is related to data and regulations concerning data, FinTechs perceive these hurdles to be greater than do incumbents.
... It requires a comprehensive complementary and infrastructure driven technology to deliver for higher productivity with optimised use of resources, and automation of manual & routine tasks (Karaby, 2019). AI replaces human discretion as 'BlackRock Robo' advisors are now performing strategic planning in US investment firm BlackRock (Tokic, 2018). At the same time, instead of reducing human jobs, it minimises human work errors, mistakes and intentional misconduct by automating various tasks and activities (Iafrate, 2018). ...
Artificial intelligence technology in the agricultural sector can reduce carbon emissions from agrarian activities and revitalize the whole industry. The Indian agricultural sector has become costly, time‐consuming, and outdated owing to the global warming process. The deployment of AI technology in Indian agriculture is still a distant dream, given the peculiarities like small‐size farms, traditional farming methods, lack of credit, storage facilities, and the decision maker's risk‐seeking attitude. AI solutions need to be delivered at the farmers' doorstep in their local language, with proper training, input support, and in collective/cooperative manner for realizing sustainable and green agriculture.
... MySQL is a relational database management system. e relational database uses the structured query language SQL as the most commonly used standard language to store data in various tables and access [22]. It can be used as individual application in the network environment of client and server. ...
With the rapid development of the market economy, there are more and more projects in the financial industry, and their complexity and technical requirements are getting higher and higher. The development of computer technology has promoted the birth of robot consultants, and it is of great significance to use robot consultants to manage and supervise financial industry projects. In order to further analyze the development and supervision of robo-advisors under the digital inclusive financial system, this paper uses complex systems and clustering algorithms as technical support to carry out research. First, the traditional K-means algorithm is used to select the initial clustering center, to improve the noise and outlier processing capabilities, and to build a data mining system based on the improved algorithm. Then, a product design model for robo-advisors is built and the risks of robo-advisors are analyzed from three aspects: technology, market, and law. Analyzing the performance of the improved K-means algorithm, in the operation of the experimental dataset B, the accuracy of the clustering result after 6 iterations reached 97.08%, which shows that the algorithm has good performance. During the trial operation of the data mining system, the four types of customers of financial institutions were accurately clustered, and it was concluded that the main type of customers who brought benefits to financial institutions was high-income customers accounting for 10.75%. Robo-advisory product models are used to build five risk-level investment portfolios and conduct risk backtests. Except for the growth and income portfolio, other portfolios have consistently outperformed the performance benchmark during the analyzed time period. Running the research system of this paper in a financial institution, comparing the capital budget before and after the operation, found that the system can improve the accuracy of the budget and reduce the risk of the robo-advisor for the financial institution.
1. Introduction
1.1. Background Significance
In the operation of financial activities, there are many projects and tasks in parallel, which brings great impact to the traditional financial management concept. As an important application of financial technology in the field of wealth management, the mode of intelligent investment adviser is more complex. In the era of big data, there are many problems in the development and supervision of intelligent investment advisers [1]. Complex systems can be said to be all over every corner of daily life. Complexity science is an emerging research form that reveals the operation laws of complex systems [2]. The development and supervision of robo-advisors in the digital age is also an extremely complex research object. Therefore, it is a unique and meaningful new idea to study the development and supervision of digital inclusive finance and robo-advisors from the perspective of complex systems.
1.2. Related Work
Complex systems have become the focus of research in various fields due to their complexity and extensiveness. Lehuta et al. focused on handling uncertainties by optimizing model complexity for management goals and technical issues to increase confidence in complex system models. They reviewed how the complex system model fits into the existing institutional and legal environment of the current European fishery decision-making framework [3]. Although their research is of reference significance, their research methods lack innovation. Inclusive finance plays an important role in improving the income gap and improving the living standards of the poor and disadvantaged groups, so it is the object of key research. Yan et al. studied the impact of digital financial inclusion (DFI) on the stabilization of household consumption in China. They used the data from the two “Chinese Family Forum” studies from 2010 to 2016. They divided household income shocks into permanent and temporary parts and assessed whether digital financial inclusion can help families resist income shocks [4]. Their research data are very representative but lack certain accuracy in processing the data. The risk analysis of robo-advisors has always been the focus of attention in the financial field. Jung et al. determined the needs of robo-advisors, derived design principles, and evaluated it through algorithm iterations in a controlled laboratory study [5]. Their research has given us a deeper understanding of robo-advisors, but they have not made constructive suggestions for the improvement of its supervisory system.
1.3. Innovative Points in This Paper
In order to build a more complete robo-advisory supervision system, reduce risks, and improve the digital level of inclusive finance, this paper studies the development and supervision of robo-advisors based on complex systems and clustering algorithms. The innovations of this research are as follows. (1) Improve the traditional K-means algorithm, optimize the selection of its initial clustering center, reduce the influence of noise, and improve the processing ability of isolated points. (2) A data mining system is constructed based on the improved algorithm. The functions of the system include opening files, importing data, data preprocessing, data clustering, and result query. (3) This paper constructs the product design model of intelligent investment consultant and uses the model to construct five risk-level portfolios for risk backtesting. This paper analyzes the risks of intelligent investment advisers from three aspects of technology, market, and law and puts forward suggestions to improve the supervision of intelligent investment advisers.
2. Complex Systems and Technologies Related to Digital Inclusive Finance
2.1. Complex System
2.1.1. Characteristics of Complex Systems
Complex systems exist in every corner of human life. Ecosystem, population system, and global economic system belong to the category of the complex system. They all have the same characteristics as the complex system. Complex systems are systematic first, which is not the superposition of simple systems and organizations. Therefore, it is not possible to study complex systems with traditional system analysis methods [6, 7]. The elements of a complex system are in a nonlinear relationship. Simple partial stacking cannot represent the whole. The local laws are not the same as the overall laws. Therefore, a new system theory is needed to consider the logical relationship between complex systems.
Complex systems are also hierarchical and interactive. The hierarchical nature of the complex system is mainly embodied in the nested relationship of different levels of interconnectedness [8]. Therefore, in the research of complex systems, it is necessary to update the traditional concept of hierarchy and analyze the research objects from the level of complex system theory. Complex systems and the external environment always interact. Different complex systems together form a larger and more complex system. When studying a complex system, we must fully consider the internal environment and external environment, study the information exchange between them, and consider the self-adjustment of the complex system in the complex external environment.
Complex systems have emergence and development. Complex systems are composed of various subsystems and local subsystems which are composed of various combinations and correlations. If the format and functional structure of the subsystem and the local subsystem are different, the complex system will no longer be the sum of the subsystem functions [9]. Complex systems may have a variety of new features, so when studying complex systems, we must consider the original features and the various new features that may appear. The self-renewability of complex systems is mainly reflected in the continuous development of the system, which is also the fundamental reason for biological evolution and the development of human society. Complicated systems become intelligent due to internal hierarchical, systematic and external interactivity, and emergence, so they can adapt to the needs and changes of the environment.
2.1.2. Agent Complex System
The complex adaptive system is based on the characteristics of the complex system and further develops the Agent theory. Agent is an independent individual or subsystem in a complex system, with a life cycle, which can perceive and adapt to the environment, run autonomously in the environment, and even change the environment. The structure of Agent generally includes environment perception, reasoning, control decision-making, knowledge base, and communication [10].
Agent has the characteristics of autonomy, social ability, initiative, learning, and adaptability [11]. Agents can use their own state and knowledge to make decisions independently, without relying on outside help. Agents can achieve a certain degree of communication, negotiate and cooperate to resolve conflicts, and complete complex tasks. Agents can judge their own situation according to the external environment and actively make choices that are beneficial to themselves at the right time. Agents can also continue to learn, adjust their own state and behavior, and adapt to the constantly changing external environment.
A single Agent has autonomous capabilities to a certain extent, but a single Agent cannot complete work in a complex and changeable environment. A multiagent system emerged at the historic moment, in which each Agent can communicate with each other. There are two or more Agents in a multiagent system, each with autonomy but limited capabilities [12]. The multiagent system does not have complete global control. A single Agent has its own judgment and status. The calculation of the entire system is asynchronous, concurrent, or parallel. The coordination methods between Agents in the multiagent system are classified as follows.
As shown in Figure 1, the coordination methods of multiagent systems can be divided into two categories: explicit coordination and implicit coordination. The explicit coordination includes complete centralized coordination, complete distributed coordination, and centralized and distributed combined coordination, and implicit coordination includes social rules and filtering strategies.
... Robo-advisors are a fairly new phenomenon in the burgeoning fintech scene. Targeting investors with little financial knowledge or those with a laid-back, hands-off investing style, successive iterations have seen the development of increasingly sophisticated robo-advisors with artificial learning and machine learning capabilities, further diminishing the role of human advisors (Tokic, 2018). The main features of robo-advisors are summarized by Investopedia (2019): ...
The burgeoning financial technology scene in Singapore has seen the emergence of robo-advisors, which aim to disrupt traditional financial advisories by using algorithms to automate client advising and investment recommendations. Using an ecologies concept to explore how lay investors are articulated into global financial networks through robo advisors, this paper contributes to studies on the "financialization of everyday life". It argues that investors are rendered passive by the disciplinary tools of algorithms, contemporary finance theories and elements of robo-advisor platforms that feed into these sociotechnological assemblages. The state's role in embedding citizen investors in these human-machine relationships is considered. The fragmented landscape of free, nonprofessional online financial advice and the opaque qualities of investing algorithms make investor subject formation incomplete and uncertain, especially when markets are highly volatile. This paper explores how both financial inclusion and exclusion operate simultaneously in robo-advisors and argues that robo-advisors may weaken efforts to promote financial literacy and education.
... The use of intelligent systems is starting to increase in unstructured environments (Di Ciccio et al. 2015) for non-routine decisions (D'Acunto et al. 2019;Huang and Rust 2018;Schildt 2017;Tokic 2018) that are consequential for strategic outcomes (Ayoub and Payne 2016;Berman and Dalzell-Payne 2018;Kiron and Schrage 2019;Reeves and Ueda 2016;von Krogh 2018) and essential for the development of competitive advantages (Davenport 2016;Grover et al. 2018;Lado and Zhang 1998). Intelligent decision systems that are supported by the entire organization outperform humans in accomplishing quantitative targets (Kolbjørnsrud et al. 2017) against measurable criteria (Parry et al. 2016) and help to reduce the degree of uncertainty in strategic decision-making in two ways in the period under review. ...
As artificial intelligence (AI) is enabling the automation of many facets of management and is increasingly used in a wide range of strategic tasks, it is necessary to better understand its relevance for strategic management. However, research on the interplay of AI and strategic management is unbalanced and lacks a coherent structure due to its multidisciplinarity. This article contributes to the emerging academic discussion by systematically reviewing and categorizing the substantial amount of research that has been conducted since the first article in the field was published in 1979. Furthermore, it introduces a comprehensive framework that integrates and synthesizes existing concepts. The framework displays the structure of the research field by classifying 58 relevant articles into two research scopes: condition-oriented research, which explores antecedents for leveraging the use of AI in strategic management, and outcome-oriented research, which studies the consequences of AI in strategic management at both the individual and the organizational level. Given the exponential potential of AI to reshape the field in its current form and the need for a realistic assessment of its impact, this review proposes promising research avenues for studying the quantifiable effects of the interplay of AI and strategic management based on the developed framework.
... Major basic applications are natural language processing (NLP) and computer vision. These find use beyond the playing of games in a large array of sectors such as the health care in surgery (Mirnezami & Ahmed, 2018), medical image processing and interpretations (Gandhi, Mosleh, Shen & Chow, 2018) or robot-assisted therapy (Ohtani, 2016), biology (de Ridder, 2018), chemical engineering (Venkatasubramanian, 2018), robo-advising in finance (Tokic, 2018), human resource decisions, criminal justice (Kugler, 2018) or service centers (Kirkpatrick, 2017;Wirtz et al., 2018). ...
This paper presents a comprehensive investigation into the transformative landscape of wealth management through the lens of robo-advisors. The evolution of automated wealth management strategies is examined, tracing the historical trajectory from inception to technological advancements. A comparative analysis of traditional wealth management practices and robo-advisor-driven approaches is conducted, assessing their efficacy, performance, and risk management capabilities. Furthermore, this research explores the market adoption rates, demographic trends, and geographical variances in the utilization of robo-advisors, shedding light on the factors influencing their widespread acceptance. Technological innovations such as artificial intelligence, machine learning algorithms, and predictive analytics are scrutinized for their pivotal This comprehensive exploration provides a foundational understanding of the evolution, impact, and implications of robo-advisors in finance, offering insights that contribute to the ongoing dialogue on the future of wealth management.
Automation of accounting is a trend which requires deep research to mitigate possible risks and to make legal background for standardization. On the real sample of 10 SMEs from Slovakia, we have investigated possibilities of implementation of automatic processing of received invoices including automatic AI solutions capable to withdraw relevant data from an invoice and import withdrawn data into accounting software for further processing. There are several available solutions capable to perform the task. By evaluation of possibilities for small SMEs we came to the conclusion that such solutions are possible and may be viable even for SMEs. As automation of processing received invoices may be viable for SMEs, it can become an industry standard. This research also shows that in Slovakia current accounting act prevents implementation of accounting automation based on AI due to not considering possible error rate caused by such automation.
Robo-advisors, also known as robo-advisory services, significantly reshape customer service in financial advisory industries, transforming retail investor markets by substituting human financial advisory experts with artificial intelligence empowered services. However, existing literature remains scattered across disciplines, with theories on financial customer service predominantly focused on Internet banking, neglecting artificial intelligence empowered interactions. Thus, service providers need a framework for implementing robo-advisors in frontline service and researchers require an advanced agenda to stimulate future research. Through a systematic, interdisciplinary literature review based on Belanche et al.’s service robot framework, this article contextualizes service robot theories into financial advisory services, synthesizing knowledge on artificial intelligence empowered customer service. We contribute to literature on service robots by contextualizing, refining, and extending the original framework by Belanche et al. and by developing a research agenda with critical perspectives. Moreover, the study yields practical and theoretical insights into artificial intelligence empowered financial advisory services.
Against the background of sustainable finance, this study aims to address whether global asset management firms started transforming towards more environmentally friendly investment policies according to the Agenda for Sustainable Development launched by the United Nation General Assembly in 2015. We apply qualitative, explorative research methods through the development of the case study of BlackRock Inc. (USA). Addressing sustainable finance, we compare the op-eds communication strategy of BlackRock against real life for the period from 2015 until today. The op-eds communication strategy by BlackRock is multi-faceted targeting to develop a leading sustainable reputation supported by fine-grained relationships to business and politician makers. We empirically proof that there is a discrepancy between BlackRock's op-eds communication contends concerning sustainable finance and the reality. Among others we found that BlackRock still invests in fossils and increasingly launches passively managed funds with limited transparency standards in terms of sustainable finance.
The paper examines the adoption of a data‐driven decision‐making (DDDM) process in organizations from purposefully selected European Union (EU) countries. It determines what organizational changes are required to adopt this process in organizations. This study uses a mixed‐method approach to identify organizational changes required for DDDM adoption. The responses from quantitative research in 10 EU countries (1091 respondents) and qualitative research with 20 C‐level managers are analyzed. The study offers the following organizational changes needed to implement DDDM in organizations: culture and mindset changes, digitalization, process improvements, new competencies, re‐organization, and legal requirements. This research contributes to a better understanding of the usage and adoption of DDDM globally and suggests specific organizational changes required to adopt this process.
The study assesses the effect of technology transfers on subjective well-being in 29 African countries. We adopt the GMM and the IV Tobit models and employ the Lifeladder indicator of happiness as a measure of subjective well-being. The findings reveal a positive significant effect of technology transfers on subjective well-being in Africa after accounting for the problems of autocorrelation, endogeneity, and heteroskedasticity. After controlling for the limited range of the subjective well-being indicator, the findings still indicate a positive significant effect of both residents' and abroad patent applications on the Lifeladder index. Employing scientific and technical article publication as an alternative indicator of technology transfers, the findings still indicate that subjective well-being in Africa is significantly improved by technology transfers and investments in academic research. The study suggests African economies should invest more in both human capital and technology infrastructure while developing patentable inventions that allow technology transfers to boost the continent's development and well-being.
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.
Artificial intelligence (AI) spring of the past decade created an increased interest into the topic in business as well as in academia. This resulted in an upward trend in academic publications, not only in computer science but also in management. This article presents a computational literature review with an abstract-based sampling approach to investigate the status of the management literature to take stock of academic research of the past two decades. We analyze 6324 papers from 1990 to 2020 published in five management-related domains and identify 41 distinct topics. We present the evolution of research pre and post AI spring, emerging topics as well as saturated areas. The findings show that the previously disjointed topic network structure is fully connected by early 2010s and the upward trend in management research starts in the period of 2014–2015. The results provide a comprehensive insight into the potential of AI in management versus underdeveloped areas, and presents, for management scholars and practitioners, suggestions about effective adoption of AI practices.
Financial robo-advisors (FRAs) are a cutting-edge financial technology offering automated services that relies on artificial intelligence. However, few studies have investigated how young retail investors (YRIs) build trust in FRAs. Another neglected area is whether FRAs’ universal (“one-size-fit-all”) model will succeed or should take account of cultural dimensions. Based on the initial trust perspective and the unified theory of acceptance and use of technology, this study develops a conceptual model of YRIs’ initial trust in FRAs, testing it in two countries from different parts of the world, i.e., Malaysia and Sweden. The results indicate that trust propensity, performance expectancy, and hedonic motivation address initial trust in FRAs, which in turn drives behavioural intention to use this technology. Although most results are similar in the two countries, there are indications of cultural differences regarding what influences YRIs’ initial trust in FRAs.
This chapter explores the trends in financial asset management. We summarize the global asset management industry, and discuss current trends across multiple regions, including North America, Europe, and China to illustrate the differences. We also discuss ESG, blockchain, and robo-advisor, which push the market forward as newer technology.
This cumulative dissertation consists of and critically discusses five peer-reviewed and co-authored publications by me about the digitalization and automation of financial advice, so-called Robo-Advice (RA). The dissertation thereby covers two main research areas. The first research area addresses the positioning of RA in science and practice providing a structure for more in-depth analyses. From a scientific perspective the relevant literature about RA is systematized in a new Organizing Framework for RA Research. The practical positioning of RA in the broader landscape of FinTech is approached in the context of mobile personal finance applications. The second research area addresses the focused analysis and design of certain RA components. One study focuses on the business model of RA, aiming at understanding their distinct elements and finding major similarities and differences. A second study focuses on RA portfolio recommendations, providing an understanding of how they differ, especially in structure, selected products, performance, and risk. Lastly, a third study presents meta-requirements and design principles for RA addressing the problem of unethical behavior that can decrease trust and the adoption of RA.
Purpose
This conceptual paper explores the implications for the sociology of the professions of robo-advice (RA) provided by robo-advisors (RAs) as an early example of successfully programmed algorithmic knowledge managed by artificial intelligence (AI).
Design/methodology/approach
The authors examine the drivers of RAs, their success, characteristics, and establish RA as an early precursor of commercialized, programmed professional advice with implications for developments in the sociology of the professions.
Findings
Within the lens of the sociology of the professions, the success of RAs suggests that the diffusion of this innovation depends on three factors: the programmed flows of automated professional knowledge are minimally disruptive, they are less costly, and attract attention because of the “on-trend” nature of algorithmic authority guided by AI. The on-trend nature of algorithmic governance and its increasing public acceptance points toward an algorithmic paradox. The contradictions arise in the gap between RA marketed to the public and as a set of professional practices.
Practical implications
The incursion of RA-like disembodied advice into other professions is predicted given the emergence of tech-savvy clients, the tie between RA and updatable flows of big data, and an increasing shift to the “maker” or “do-it-yourself” movements.
Originality/value
Using the success of RAs in the financial industry, the authors predict that an AI-managed platform, despite the algorithmic paradox, is an avenue for growth with implications for researchers in the sociology of the professions.
Purpose
The objective of this study is to provide a systematic review of the literature on artificial intelligence (AI) in customer-facing financial services, providing an overview of explored contexts and research foci, identifying gaps in the literature and setting a comprehensive agenda for future research.
Design/methodology/approach
Combining database (i.e. Scopus, Web of Science, EBSCO, ScienceDirect) and manual journal search, the authors identify 90 articles published in Australian Business Deans Council (ABDC) journals for investigation, using the TCCM (Theory, Context, Characteristics and Methodology) framework.
Findings
The results indicate a split between data-driven and theory-driven research, with most studies either adopting an experimental research design focused on testing the accuracy and performance of AI algorithms to assist with credit scoring or investigating AI consumer adoption behaviors in a banking context. The authors call for more research building overarching theories or extending existing theoretical perspectives, such as actor networks. More empirical research is required, especially focusing on consumers' financial behaviors as well as the role of regulation, ethics and policy concerned with AI in financial service contexts, such as insurance or pensions.
Research limitations/implications
The review focuses on AI in customer-facing financial services. Future work may want to investigate back-office and operations contexts.
Originality/value
The authors are the first to systematically synthesize the literature on the use of AI in customer-facing financial services, offering a valuable agenda for future research.
The evolution of Artificial Intelligence (AI) has revolutionized many aspects of human life, including healthcare. Amidst the Covid-19 pandemic, AI-enabled smartwatches are being used to help users to self-monitor and self-manage their health. Using a framework based on Stimulus-Organism-Response (S–O-R) theory, this present study aimed to explore the use of AI-enabled smartwatches for health purposes, in particular the effects of product quality, service quality, perceived convenience, and perceived ease of use on user experience, trust and user satisfaction. Based on a purposive survey sample of 486 smartphone users in Bangladesh, data collected was analyzed using SPSS software for elementary analyses and PLS-SEM for hypotheses testing. The findings showed that the predictors, namely product quality, service quality, perceived convenience, and perceived ease of use, significantly affected user experience and trust. Similarly, user experience and trust were influential on user satisfaction and played partial mediating roles between predictors and user satisfaction. Besides, gender and age moderate the relationships of experience and trust with customer satisfaction. These findings support the S–O-R theoretical framework and have practical implications for brand and marketing managers of smartwatches in developing product features and understanding users' attitudes and behaviours.
Financial price fast forecasting for high‐frequency algorithmic trading can be improved profitably by using simplified artificial intelligence models. There are several different methodologies for working in algorithmic trading, each specific to the context. The method proposed here is designed to prioritize better performance. The methodology uses a combination of different artificial intelligence techniques, some of which have been revised and simplified to maximize performance. The forecasting part is assigned to a formula based on the Petrelli‐Cesarini (2018) index, specifically designed to speed up the calculation, allowing its use in operating environments with low time frames and high‐frequency trading.
Purpose
This conceptual paper draws together an interdisciplinary approach to robo-advisors (RAs) as an example of an early and successful example of automated, programmed professional services.
Design/methodology/approach
Little is known about the forces driving this change in the delivery of professional service. This work explores the drivers of RAs, the degree of disruption incurred by the introduction of RAs, and how, as RAs advance, trust in algorithmic authority aids in legitimating RAs as smart information.
Findings
From the firms' perspective, the drivers include rebranding occasioned by the financial crisis (2008), the widening of the client base and the “on-trend” nature of algorithmic authority guided by artificial intelligence (AI) embedded in RAs. This examination of the drivers of RAs indicates that professional service automation is aligned with information society trends and is likely to expand.
Practical implications
Examining RAs as an indicator of the future introduction of programmed professional services suggests that success increases when the algorithmic authority in the programmed serves are minimally disruptive, trustworthy and expand the client base while keeping the knowledge domain of the profession under control of the industry.
Originality/value
Treating RAs as an early instance of successfully embedding knowledge in AI and algorithmically based platforms adds to the early stages of theory and practice in the monetization and automation of professional knowledge-based services.
This study focuses on the actual and potential implications of artificial intelligence (AI) based applications and technical issues that are related to behavioural finance. However, there have being an enormous growth in the field of AI-based application within the financial services industry, especially in behavioural finance. This paper addresses the recent developments in AI applications related to algorithms in financial advisory services. Its performance through a theoretical framework based learning model in the financial context, which are effective in producing reliable portfolios based on investors’ behaviour and known as robo-advising. In recent years, the traditional financial services have been replaced by robo-advisors in wealth management industry due to new generation of clients who have the technical know-how of the digital technologies, prefer to have active and ongoing control over their investments while relying on the information from multiple (mainly on-line) sources. Currently, robo-advisors are recognised as most disruptive trend in asset and wealth management. Thus, a robo-advisor can be defined in an automated investment platform which utilises quantitative algorithms to manage investors’ portfolios along with easily accessible to customers through on-line. This paper provides a longitudinal case study based on robo-advisors and behavioural financial decision-making process by investors, because the notion that those behavioural financial decisions are important for successful execution of a customer’s financial portfolios.
With the development of the global economy and the further improvement of artificial intelligence research, the relationship between artificial intelligence and finance is increasingly close. The booming financial sharing services have greatly improved the efficiency of relevant financial work. At present, the global financial sharing service platform has excellent performance in reducing labor cost and improving operation efficiency, but it has not fully exerted the function of creating value of financial work. The establishment of financial sharing requires a large amount of data research, which requires the help of artificial intelligence to carry out relevant data statistics. Using big data and artificial intelligence technology to improve the financial sharing service platform can further liberate human resources and promote capital integration, which is an important way for the financial sharing service center to play its role in creating value. Finally, the improvement work should properly deal with the balance between centralization and decentralization, actively extend the business and expand the professional skills of financial personnel. However, we must see its two sides in the development process and avoid the problems in the research.
The literature suggests that excessive speculation is hard to define, and thus difficult to regulate. The aim of this article is to provide a framework to differentiate between legitimate speculation and excessive speculation, using the efficient market mechanism as a guide (or blueprint). Specifically, I argue that all speculative strategies that improve market efficiency, based on public information, with no predatory/manipulative elements, can be classified as legitimate speculation, while all speculative strategies that purposely cause market inefficiencies, with predatory/manipulative behavior, can be classified as excessive speculation. Accordingly, I classify rational arbitrage, discretionary trading and systematic trend-following as legitimate speculation, which should be encouraged. Using the same framework, I classify insider trading, rational destabilizing speculation and arbitraging arbitragers as excessive speculation, which must be regulated.
Thesis (Ph. D.)--University of Chicago, Graduate School of Business, June, 1964. "Reprinted from the Journal of business of the University of Chicago, XXXVIII, no. 1 (January, 1965), 34-105." Includes bibliographical references. Microfilm. Chicago : Dept. of Photoduplication, University of Chicago Library,
The U.S. stock market belongs to bots
- D Burger
Are you ready for robot colleagues?MIT Sloan Management Review Digital
- F Klotz
The Pros and Cons of Robot Managers
- T Chamorro-Premuzic
- G Ahmetoglu
At BlackRock machines are rising over managers to pick stocks.The New York Times
- L Thomas
The Expansion of Robo-Advisory in Wealth Management
- Deloitte
MIT Sloan Management Review Digital
- F Klotz