Evaluation of Vertex AI Agent for Investment Product Support: Enhancing Customer Service in Asset Management

This study explores the application of a Vertex AI agent designed to support asset management customers by providing detailed information about investment products through a natural language interface. We discuss the agent's architecture, which integrates an advanced language model and API, enabling user-friendly access to data on asset classes, performance metrics, fees, risks, and liquidity. The paper evaluates the agent's accuracy, error-handling capabilities, and iterative improvements, illustrating how targeted training enhances the agent?s performance in responding to customer inquiries. Future improvements for addressing calculation-based queries and data fabrication issues are also proposed.

Authors

Publication Details

Journal
International Journal of Science and Research (IJSR)
Published
2024-11-05
DOI
https://doi.org/10.21275/sr241112035318
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
0.00
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article

Evaluation of Vertex AI Agent for Investment Product Support: Enhancing Customer Service in Asset Management

Ananth Majumdar
International Journal of Science and Research (IJSR)
Big Data and Business Intelligence
article

Evaluation of Vertex AI Agent for Investment Product Support: Enhancing Customer Service in Asset Management

Ananth Majumdar
article en

Abstract

This study explores the application of a Vertex AI agent designed to support asset management customers by providing detailed information about investment products through a natural language interface. We discuss the agent's architecture, which integrates an advanced language model and API, enabling user-friendly access to data on asset classes, performance metrics, fees, risks, and liquidity. The paper evaluates the agent's accuracy, error-handling capabilities, and iterative improvements, illustrating how targeted training enhances the agent?s performance in responding to customer inquiries. Future improvements for addressing calculation-based queries and data fabrication issues are also proposed.

International Journal of Science and Research (IJSR)Vol. 13(11)
Openalex Percentile: Top 67%
Big Data and Business Intelligence
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