Trust me, I am a Robo-advisor

Scherer, Bernd; Lehner, Sebastian

doi:10.1057/s41260-022-00284-y

Cited by 6 publications

(2 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Robo-advice, automatically avoids human interaction to bypass biases (Bhatia, Chandani & Chhateja, 2020;D'Acunto & Rossi, 2021). However, the application of the Robo-advice is limited to certain areas of financial planning servicesmarket timing for efficient trading, rebalancing the portfolio on time, constructing a tax-efficient portfolio and cost savings through transaction automation (Kaya, Schildbach & Schneider, 2017;Scherer & Lehner, 2022). Moreover, Robo-advice efficiently collects information from the clients and processes this information for decision-making purposes (Brenner & Meyll, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Can Artificial Intelligence (AI) Manage Behavioural Biases Among Financial Planners?

Hasan

Vaz

Athota

et al. 2023

Journal of Global Information Management

View full text Add to dashboard Cite

The main novelty of this paper is proposing artificial intelligence (AI) to manage behavioural biases in the financial decision-making process. An empirical study by Kahneman and Tversky identifies the evidence of behavioural biases in the investment decision-making process: a reversal of an established tenet in traditional finance. Financial planners are vulnerable to behavioural biases and are therefore unable to provide optimal investment solutions for their clients. Identifying the limitations of current practice, this research attempts to address how AI can help financial planners in subduing their behavioural biases and proposes the adoption of AI in financial planning services to circumvent behavioural biases. In recent years, AI has attained significant efficacy and has proven to be efficacious through supervised and unsupervised learning. Applying these AI techniques in mitigating behavioural biases, this study confirms that the backpropagation within the neural network and deep reinforcement learning can help overcome confirmation and hindsight biases.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Can Artificial Intelligence (AI) Manage Behavioural Biases Among Financial Planners?

Hasan

Vaz

Athota

et al. 2023

Journal of Global Information Management

View full text Add to dashboard Cite

show abstract

“…The literature emphasizes the significance of consumer age and technological competences [ 27 ], as well as financial knowledge, literacy and experience [ 28 – 30 ]. Some studies demonstrate the importance of trust in the technologies used in robo-advisory and the providers of robo-advisory [ 4 , 31 – 36 ], as well factors related to risk acceptance [ 37 – 39 ]. Limitations to the acceptance of robo-advisory services were also pointed out, related to reluctance to make decisions made by machines, especially when they contain an ethical component [ 40 ].…”

Section: Introductionmentioning

confidence: 99%

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

Orzeszko,

Piotrowski

2024

PLoS ONE

View full text Add to dashboard Cite

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.

show abstract