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  Published Paper Details:

  Paper Title

An AI-Driven System for Portfolio Management

  Authors

  SUNDARESA RAGAVAN S,  HARSHAVARDHAN SURYA BS,  INBAKUMAR R,  SANTHOSHKUMAR K,  MR.D.MAGESH

  Keywords

Skills assessment, AI-driven analytics, Datadriven approach, Pattern recognition

  Abstract


Employee Portfolio Management, fuelled by advanced artificial intelligence (AI) techniques, stands out as a transformative strategy that has the potential to reshape how businesses optimize their human resources. In this context, Employee Portfolio Management refers to the systematic and data-driven process of understanding, organizing, and leveraging the skills, experiences, and aspirations of each employee within an organization. Unlike traditional approaches that often focus solely on job roles and responsibilities, Employee Portfolio Management takes a holistic view of individuals, acknowledging their diverse talents and potential contributions. The integration of AI-driven techniques into Employee Portfolio Management introduces a game-changing element. These AI algorithms can analyze vast amounts of data related to each employee, ranging from their educational and professional background to their on-the-job performance and skill development. By processing this data, AI can uncover patterns, correlations, and insights that might not be immediately apparent through manual methods

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2405712

  Paper ID - 260747

  Page Number(s) - g625-g627

  Pubished in - Volume 12 | Issue 5 | May 2024

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  SUNDARESA RAGAVAN S,  HARSHAVARDHAN SURYA BS,  INBAKUMAR R,  SANTHOSHKUMAR K,  MR.D.MAGESH,   "An AI-Driven System for Portfolio Management", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 5, pp.g625-g627, May 2024, Available at :http://www.ijcrt.org/papers/IJCRT2405712.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


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ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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