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Paper Type
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Abstract
Robotic advisors (RAs) are investment programs, which use trading algorithms to automate investments by users’ risk/return preferences. Despite many advantages (e.g., RAs are less costly than traditional financial advisors), RAs are increasingly criticized for using oversimplified self-assessment questionnaires that result in inaccurate assessments of users’ risk profiles and, hence, investments that do not live up to investors’ expectations. We tackle this issue by proposing a voice-enabled RA for personalized wealth management, capable of recognizing users’ speech and sentiments due to Natural Language Processing that is moreover capable of reacting on real-time market developments due to direct access and processing of financial market information. We conduct a thorough requirements analysis that guides the design of a conceptual model and the ecosystem surrounding our voice-enabled RA. While future research needs to develop and evaluate advanced prototypes, our work constitutes the first step toward an RA, capable of providing truly personalized investment proposals.
Recommended Citation
Ostern, Nadine Kathrin; Schöler, Joe; and Moormann, Jürgen, "Toward Voice-Enabled Robotic Advisory for Personalized Wealth Management" (2020). AMCIS 2020 Proceedings. 1.
https://aisel.aisnet.org/amcis2020/ai_semantic_for_intelligent_info_systems/ai_semantic_for_intelligent_info_systems/1
Toward Voice-Enabled Robotic Advisory for Personalized Wealth Management
Robotic advisors (RAs) are investment programs, which use trading algorithms to automate investments by users’ risk/return preferences. Despite many advantages (e.g., RAs are less costly than traditional financial advisors), RAs are increasingly criticized for using oversimplified self-assessment questionnaires that result in inaccurate assessments of users’ risk profiles and, hence, investments that do not live up to investors’ expectations. We tackle this issue by proposing a voice-enabled RA for personalized wealth management, capable of recognizing users’ speech and sentiments due to Natural Language Processing that is moreover capable of reacting on real-time market developments due to direct access and processing of financial market information. We conduct a thorough requirements analysis that guides the design of a conceptual model and the ecosystem surrounding our voice-enabled RA. While future research needs to develop and evaluate advanced prototypes, our work constitutes the first step toward an RA, capable of providing truly personalized investment proposals.
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