Sachin Dixit*

Solutions Architect, Financial Systems Engineering, Stripe Inc, USA

*Corresponding Author:: Sachin Dixit, Solutions Architect, Financial Systems Engineering, Stripe Inc, USA.

Received: January 02, 2025; Published: March 06, 2025

Abstract

The advent of generative artificial intelligence (AI) in the financial technology (FinTech) sector has created unprecedented op portunities for automating and enhancing financial advisory systems. This research focuses on the application of generative AI to de velop automated financial advisory platforms, integrating natural language processing (NLP) and reinforcement learning (RL) for the formulation of personalized investment strategies. Traditional financial advisory models, often characterized by manual processes, human bias, and limited scalability, are increasingly unable to meet the demands of a fast-paced and diverse investment landscape. In response, AI-driven systems present a transformative approach, leveraging the power of generative models to process vast amounts of data and provide real-time, tailored financial recommendations to both retail and institutional investors.
This study delves into the technical mechanisms underpinning the integration of generative models with NLP and RL frameworks. Generative models, including variational autoencoders (VAEs) and generative adversarial networks (GANs), play a critical role in simulating complex financial scenarios and generating investment strategies that reflect dynamic market conditions. By synthesizing vast amounts of historical market data, these models create high-dimensional representations of financial environments, which are then used to train reinforcement learning agents. The RL agents learn optimal investment strategies through continuous interaction with these simulated environments, dynamically adjusting to new market information and user preferences. This ability to simulate and optimize investment decisions allows for more sophisticated, personalized strategies, as compared to conventional rule-based systems.
Natural language processing enhances the system by enabling it to process unstructured data from various sources, including financial news, reports, and social media, which can significantly impact market trends. NLP models, particularly transformer-based architectures like BERT and GPT, are employed to extract, interpret, and summarize relevant textual information in real-time, feed ing it into the generative and RL models. This integration allows the financial advisory system to understand and respond to both quantitative and qualitative factors affecting financial markets. Moreover, the NLP component supports direct interaction between the AI-driven system and users, facilitating personalized communication and user-specific strategy recommendations. This two-way communication is pivotal in enhancing customer engagement, as users can input preferences, risk tolerance, and financial goals, which the system continuously refines and incorporates into its investment strategy formulation.
Reinforcement learning plays a pivotal role in the adaptive learning process, allowing the system to improve its decision-making over time by receiving feedback from the environment, such as market performance and user satisfaction. Specifically, model-free RL approaches like Q-learning and policy gradient methods are applied to optimize investment strategies. These approaches enable the system to evaluate multiple potential actions in real-time and select those with the highest expected return, given the current market state and individual user profile. Over time, the RL agent learns to maximize cumulative returns by balancing exploration of new strategies with the exploitation of known profitable actions. By leveraging RL in tandem with generative models, the system can autonomously adjust its strategy in response to changing market conditions and user requirements, thereby delivering a highly customized investment plan that evolves with the investor’s financial landscape.
The potential of these AI-driven advisory systems lies not only in their technical sophistication but also in their ability to democ ratize financial planning. Traditionally, high-quality financial advisory services have been accessible primarily to affluent individuals or large institutions due to the high cost of personalized financial advice. By automating the advisory process through AI, these sys tems can provide personalized financial planning at scale, making high-quality investment strategies accessible to a broader range of users, including those with limited financial literacy or smaller investment portfolios. This democratization of financial services is particularly significant in the context of retail investors, who can now access sophisticated financial insights and recommendations that were previously reserved for institutional clients.
Furthermore, this paper explores the broader implications of AI-driven financial advisory systems on investor behavior and deci sion-making. By providing real-time, data-driven insights and personalized investment strategies, these systems have the potential to mitigate common cognitive biases in financial decision-making, such as overconfidence, loss aversion, and herd behavior. Through continuous learning and adaptation, AI-driven systems can guide users towards more rational, objective investment decisions, po tentially improving overall financial outcomes for both retail and institutional investors. However, the paper also addresses the chal lenges associated with the deployment of AI in financial advisory systems, including issues of data privacy, algorithmic transparency, and the need for robust regulatory frameworks to ensure the ethical and responsible use of AI in financial decision-making.

Keywords: Generative AI; Financial Advisory Systems; Natural Language Processing; Reinforcement Learning; Personalized Invest ment Strategies; FinTech; AI-Driven Advisory; Democratization of Financial Services; Market Simulation; Cognitive Biases

References

1. HH Ali., et al. “Automated Financial Advisory System Based on Artificial Intelligence and Machine Learning”. IEEE Access 8 (2020): 55028-55041.
2. A Alzahrani. "Natural Language Processing Applications in Finance: A Survey”. IEEE Transactions on Computational Social Systems3 (2020): 1016-1025.
3. H K K Arun and V S K Vardhan. “Generative Adversarial Networks in Financial Risk Management”. IEEE Transactions on Neural Networks and Learning Systems4 (2020): 1240-1250.
4. J H Lee., et al. “Deep Reinforcement Learning for Portfolio Management”. IEEE Transactions on Emerging Topics in Computational Intelligence1 (2017): 1-11.
5. Y Wang., et al. “A Survey on Deep Reinforcement Learning in Financial Applications”. IEEE Transactions on Systems, Man, and Cybernetics: Systems4 (2021): 1934-1946.
6. Y Zhou., et al. “Financial News Analysis Based on NLP and Machine Learning”. IEEE Access 9 (2021): 56570-56582.
7. J Baker and J C B J DeLeo. “AI in Wealth Management: Past, Present, and Future”. IEEE Engineering Management Review3 (2020): 45-56.
8. Y Li., et al. “A Personalized Financial Advisory Framework Using Generative Models”. IEEE Transactions on Knowledge and Data Engineering12 (2021): 4783-4796.
9. R M Ahmad., et al. “Cognitive Biases in Financial Decision-Making: Implications and AI Solutions”. IEEE Access 8 (2020): 95023-95034.
10. PHM Rehmat and A G K Madani. “Leveraging NLP for Financial Sentiment Analysis”. IEEE Transactions on Big Data2 (2020): 309-320.
11. KCL Wong., et al. “Reinforcement Learning for Algorithmic Trading”. IEEE Transactions on Financial Technology1 (2021): 29-40.
12. A A Ameen and M A B A Soliman. “Evaluating the Effectiveness of AI in Financial Advisory Services”. IEEE Access 8 (2020): 18725-18735.
13. M H M Rahman and A F A Rahman. “Dynamic Portfolio Optimization Using Reinforcement Learning”. IEEE Transactions on Neural Networks and Learning Systems7 (2020): 2950-2962.
14. J S Tsai. “Machine Learning Applications in Financial Services”. IEEE Transactions on Emerging Topics in Computational Intelligence5 (2020): 672-682.
15. A A Younis. “NLP and AI in the FinTech Industry: Opportunities and Challenges”. IEEE Access 9 (2021): 8700-8710.
16. M K I Karim and Y M M Ibrahim. “Generative Models for Financial Data Simulation”. IEEE Transactions on Neural Networks and Learning Systems12 (2019): 3546-3559.
17. H K K Kim and S H Lee. “Real-Time Portfolio Optimization Using Reinforcement Learning”. IEEE Access 9 (2021): 10896-10907.
18. B Liu., et al. “Ethical Considerations in AI-Driven Financial Services”. IEEE Transactions on Computational Social Systems4 (2020): 1027-1039.
19. K Gupta and R K Gupta. “Financial Technology and the Role of AI in Wealth Management”. IEEE Transactions on Systems, Man, and Cybernetics: Systems8 (2020): 2930-2942.
20. R S M Alavi and S H H Salama. “Challenges and Opportunities in the Integration of AI in Financial Advisory”. IEEE Access 9 (2020): 30345-30356.

Citation

Citation: Sachin Dixit. “Generative AI-Driven Automated Financial Advisory Systems: Integrating NLP and Reinforcement Learning for Personalized Investment Strategies in FinTech Applications".Acta Scientific Computer Sciences 7.1 (2025): 11-22.

Copyright

Copyright: © 2025 Sachin Dixit. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.