CONVERSATIONAL AI FOR PERSONALIZED WEALTH MANAGEMENT IN CLOUD-BASED CRM SOLUTIONS
Main Article Content
Abstract
Conversational AI has emerged as a transformative tool for personalized wealth management within cloud-based CRM solutions [1]. With the increasing complexity of financial services, clients demand personalized interactions and real-time insights tailored to their investment goals [2]. This paper explores the design and implementation of AI-driven virtual assistants to enhance client engagement, automate advisory services, and improve operational efficiency [3]. The proposed approach leverages Natural Language Processing (NLP), Machine Learning (ML), and cloud computing technologies to provide predictive financial insights and personalized recommendations [4]. A hybrid AI-human collaboration model is introduced to optimize decision-making processes, ensuring a balance between automation and human expertise [5]. The evaluation framework considers response accuracy, client satisfaction, operational efficiency, and regulatory compliance [6]. Our experimental results demonstrate that the proposed system enhances customer retention by 35%, improves response accuracy by 20%, and reduces operational costs by 25% [7]. This study also discusses potential challenges, including data privacy concerns, ethical implications, and scalability considerations [8]. Future directions include integrating reinforcement learning techniques and expanding multi-language capabilities to cater to a global audience.
Downloads
Article Details
COPYRIGHT
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.