Consumer Acceptance and Adoption of AI Robo-Advisors in Fintech Industry

Sabir, Asrar Ahmed; Ahmad, Iftikhar; Ahmad, Hassan; Rafiq, Muhammad; Khan, Muhammad Asghar; Noreen, Neelum

doi:10.3390/math11061311

Cited by 13 publications

(4 citation statements)

References 134 publications

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“…The literature identifies factors that significantly influence the acceptance of robo-advisory among consumers. Very often they refer to technological and operational aspects of robo-advisory: level of automation, transparency, and control [17], comprehensibility [18], perceived usefulness [19], privacy [20], perceived innovativeness [21], data security [22], ease of interaction and work efficiency [23][24][25][26]. Demographic and socio-economic factors are also important determinants of the acceptance of robo-advisory.…”

Section: Introductionmentioning

confidence: 99%

Prediction of robo-advisory acceptance in banking services using tree-based algorithms

Orzeszko,

Piotrowski

2024

PLoS ONE

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The banking sector is increasingly recognising the need to implement robo-advisory. The introduction of this service may lead to increased efficiency of banks, improved quality of customer service, and a strengthened image of banks as innovative institutions. Robo-advisory uses data relating to customers, their behaviors and preferences obtained by banks from various communication channels. In the research carried out in the work, an attempt was made to obtain an answer to the question whether the data collected by banks can also be used to determine the degree of consumer interest in this type of service. This is important because the identification of customers interested in the service will allow banks to direct a properly prepared message to a selected group of addressees, increasing the effectiveness of their promotional activities. The aim of the article is to construct and examine the effectiveness of predictive models of consumer acceptance of robo-advisory services provided by banks. Based on the authors’ survey on the use of artificial intelligence technology in the banking sector in Poland, in this article we construct tree-based models to predict customers’ attitudes towards using robo-advisory in banking services using, as predictors, their socio-demographic characteristics, behaviours and attitudes towards modern digital technologies, experience in using banking services, as well as trust towards banks. In our study, we use selected machine learning algorithms, including a decision tree and several tree-based ensemble models. We showed that constructed models allow to effectively predict consumer acceptance of robo-advisory services.

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Section: Introductionmentioning

confidence: 99%

Prediction of robo-advisory acceptance in banking services using tree-based algorithms

Orzeszko,

Piotrowski

2024

PLoS ONE

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“…In the literature on the subject, there are few works devoted to the analysis of factors shaping the attitude towards robo-advisory. However, most of them focus on a relatively narrow group of determinants in relation to the technological and operational aspects of the service (Sabir et al, 2023;Nguyen et al, 2023) and the issue of trust in this technology (Yi et al, 2023;Nourallah et al, 2023) with less attention paid to consumer characteristics and experiences.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence and customers’ intention to use robo-advisory in banking services

Piotrowski,

Orzeszko

2023

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Research background: Robo-advisory is a modern and rapidly developing area of implementing artificial intelligence to support customer decision-making. The current significance of robo-advisory to the financial sector is minor or marginal, and boils down to formulating recommendations and implementing investment strategies. However, the ongoing digital transformation of the economy leads us to believe that in the near future this technology will also be much more widely used with banking products. This makes it necessary for banks and other financial institutions to be prepared to offer this service to their customers. Purpose of the article: The aim of this paper is to identify factors significantly influencing bank customers’ intention to use robo-advisory. Identification of robo-advisory acceptance factors may increase the effectiveness of banks' promotional activities regarding such a service. Methods: Empirical data was obtained through a survey conducted on a representative sample of 911 Polish respondents aged 18–65. Using a multilevel ordered logit model and methods based on machine learning algorithms, the authors identified variables relating to the demographic and socio-economic characteristics, behaviors, and attitudes of consumers that primarily determine respondents’ adoption of robo-advisory. Findings & value added: The results of the study indicate that the variables regarding the respondents' attitude towards the use of artificial intelligence in banking services turned out to be the most important from the point of view of acceptance of robo-advisory. Next in terms of importance were the variables presenting respondents' assessments of the ethics of financial services. An important finding is that experience in using basic financial services is not a significant factor when accepting robo-advisory. From the practical perspective, the article provides recommendations on the use of artificial intelligence technology in finance and ethical aspects of the provision of such services by banks.

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“…The findings grouped into three recurrent vital themes: the factors influencing Robo-Advisors' acceptance (lack of trust in AI and data security), the role of digital skills in implementation were identified and explored, highlighting their significance and interconnectedness within the context of the South African financial landscape, and these will be discussed below. (Sabir et al, 2023;Yi et al, 2023).…”

Section: Resultsmentioning

confidence: 99%

“…Professionals in financial advisory services, such as financial advisors and consultants, play a crucial role in educating and guiding individuals on the use and benefits of robo-advisory services. Their expertise can enhance financial knowledge and build trust in the technology, ultimately influencing the willingness to adopt roboadvisory services (Sabir et al, 2023). Employing professionals within financial advisory services plays a crucial role in influencing the adoption and utilisation of robo-advisory services, particularly in enhancing financial knowledge and providing personalised guidance (Yi et al, 2023).…”

Section: The Impact Of Employment Within Financial Advisory Servicesmentioning

confidence: 99%

The Impact Of Robo-Advisors On Decision Support Management In A South African Financial Sector

Mangena,

Sithole

EPiC Series in Education Science

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The financial sector is undergoing significant transformation due to technological advancements, particularly the rise of Robo-advisors, a subset of Artificial Intelligence (AI) that provides and enhances the efficiency of financial advisory services. This study investigates the transformative influence of robo-advisors on decision-support management in the South African financial sector, addressing pertinent challenges and opportunities. Notable findings from a comprehensive literature analysis reveal a discourse centered on applied digital skills within specific sectors such as education, accounting and health. However, there are limited discussions surrounding the challenges and opportunities of implementing robo-advisors in the South African financial sector. The study's findings reveal several key insights, including the factors influencing Robo- Advisors' acceptance (lack of trust in AI and data security), the role of digital skills in implementation, and the impact of employment within financial advisory services. These findings highlight informed decisions for effective adoption and integration of Robo- advisors in the South African financial sector, fostering efficiency, productivity and competitiveness. Future research should continue to explore the evolving role of Robo- advisors and their implications for the financial industry.

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Consumer Acceptance and Adoption of AI Robo-Advisors in Fintech Industry

Cited by 13 publications

References 134 publications

Prediction of robo-advisory acceptance in banking services using tree-based algorithms

Prediction of robo-advisory acceptance in banking services using tree-based algorithms

Artificial intelligence and customers’ intention to use robo-advisory in banking services

The Impact Of Robo-Advisors On Decision Support Management In A South African Financial Sector

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