Transforming Financial Planning with Generative AI: A Framework for Proactive and Adaptive Strategies

Authors

  • Isham Kalappurackal Mansoor Virginia Polytechnic Institute and State University
  • Mansoor Veliyathnadu Ebrahim Leading Health Insurance Company

DOI:

https://doi.org/10.47941/jts.2652

Keywords:

Financial Planning, Generative AI, Personalized Finance, Risk Management, Scenario Modeling, Proactive Planning

Abstract

This paper explores the transformative potential of Generative AI in financial planning, addressing limitations of traditional approaches such as inflexibility, lack of scenario-based planning, and short-term focus. The research highlights how AI systems can process vast amounts of financial data to identify patterns and correlations, enabling personalized financial strategies and proactive risk management. Through analysis of current financial workflows and implementation challenges, we demonstrate that Generative AI can significantly reduce time spent on data consolidation and analysis while increasing planning accuracy through simulation capabilities. The paper presents a case study of an AI-driven financial planning assistant that creates comprehensive financial profiles from user documents. Despite challenges related to data security, privacy concerns, and algorithmic bias, we propose that with proper frameworks and ethical considerations, Generative AI has the potential to democratize financial planning, making it more accessible to everyone. This research contributes to understanding how AI technologies can enhance financial decision-making and foster greater financial inclusion and security.

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Author Biographies

Isham Kalappurackal Mansoor, Virginia Polytechnic Institute and State University

Student

Mansoor Veliyathnadu Ebrahim, Leading Health Insurance Company

Solution Architect

References

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Published

2025-04-18

How to Cite

Mansoor, I. K., & Ebrahim, M. V. (2025). Transforming Financial Planning with Generative AI: A Framework for Proactive and Adaptive Strategies. Journal of Technology and Systems, 7(2), 48–54. https://doi.org/10.47941/jts.2652

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Articles