Abstract
Robo advisors are used for matching financial products to customers by applying computer algorithms. Behaviours, perceptions, preferences form the development basis of these algorithms that rank, combine and recommend investment options to consumers granting better returns for a lower cost than human advisors. Trustworthiness and thoroughness are the key aspects of a competent robo-advisor. As new technological innovations appear on the market every day, citizens are increasingly exposed to malicious applications. Therefore, such systems should be developed that define an optimal balance of protecting customers and allowing robo-advisors to develop at the same time. First, this paper characterizes robo-advisors and compares them with human advisors, then a new methodology combining design thinking and ontology development methods will be introduced. Next, the process of developing an investment ontology that collects additional information on financial products and provides the foundations of recommender system (an advanced robo-advisor) is presented. A text-mining based approach used for the validation of this ontology is also discussed in this section of the paper. The purpose is to demonstrate how well-developed and validated ontology-enabled solutions can help to overcome difficulties in the use robo-advisors.
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Acknowledgement
The implementation of Project 2018-1.3.1-VKE-2018-00007 was financed by the National Research Development and Innovation Fund, which supported the Competitiveness and Excellence Cooperation Program.
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Szabó, I., Neusch, G., Vas, R. (2021). Design Thinking Based Ontology Development for Robo-advisors. In: Abraham, A., Piuri, V., Gandhi, N., Siarry, P., Kaklauskas, A., Madureira, A. (eds) Intelligent Systems Design and Applications. ISDA 2020. Advances in Intelligent Systems and Computing, vol 1351. Springer, Cham. https://doi.org/10.1007/978-3-030-71187-0_74
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