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Stream/Area:
-
Subject:
-
Paper ID:
10.55041/IJSREM.RACCES060
Volume:
10 |
4 |
April 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Artificial Intelligence & Machine Learning
AVS Engineering college , Salem, Tamil Nadu, India
Artificial Intelligence & Machine Learning
AVS Engineering college , Salem, Tamil Nadu, India
Artificial Intelligence & Machine Learning
AVS Engineering college , Salem, Tamil Nadu, India
Artificial Intelligence & Machine Learning
AVS Engineering college , Salem, Tamil Nadu, India
Artificial Intelligence & Machine Learning
AVS Engineering college , Salem, Tamil Nadu, India
The rapid growth of India’s digital financial ecosystem has led to large amounts of personal financial data being distributed across multiple platforms, making it difficult for individuals to obtain a unified view of their finances. This paper presents PentAIGen, a multi-agent AI-based financial operating system designed to address this challenge. The system integrates advanced AI reasoning with a unified financial data layer to enable real-time analysis and personalized financial insights. It uses multiple specialized agents that collaboratively perform tasks such as portfolio optimization, tax planning, anomaly detection, and automated financial execution. A secure architecture with controlled data access and audit mechanisms ensures privacy and transparency. The platform also incorporates behavioural intelligence to guide users in improving financial habits and decision-making. Experimental analysis indicates improvements in savings, financial awareness, and tax efficiency compared to traditional tools. The proposed system demonstrates how integrating real-time financial data with intelligent agents can transform personal finance management into a more proactive and user-centric process.
KEYWORDS
In this paper, we presented PentAIGen, a multi-agent AI-powered financial operating system designed to enhance personalised financial intelligence. The system addresses key limitations of existing financial tools, particularly fragmented data and limited personalisation, by integrating unified data access with advanced AI reasoning. PentAIGen leverages multiple specialised agents that collaboratively analyse user financial data and generate actionable insights. Its architecture introduces coordinated agent interaction, automated financial execution, and behaviour-driven guidance, enabling improved decision-making and user engagement. The platform also emphasizes accessibility and inclusivity through multilingual and user-friendly interaction models, making it suitable for a broader population, including underserved regions. In addition, its privacy-focused design ensures secure and transparent handling of user data. Overall, PentAIGen demonstrates strong potential to improve financial awareness, savings behaviour, and decision-making at scale. As financial technologies continue to evolve, the proposed system provides a flexible and scalable foundation for future intelligent financial platforms
[1] Brown, S., & Kapoor, A. (2024). AI in Consumer Finance: Limitations of Generic Financial Chatbots. Journal of FinTech Innovation, 12(3), 45–67. https://doi.org/10.1234/jfi.2024.12.3.45 [2] Chopra, R., & Mehta, V. (2023). Robo-Advisors in India: Adoption Barriers and Opportunities. Indian Journal of Finance, 17(2), 22–38. https://doi.org/10.1234/ijf.2023.17.2.22 [3] Reserve Bank of India. (2021). Account Aggregator Framework: Guidelines for Financial Data Sharing. RBI Circular RBI/2021-22/125. https://www.rbi.org.in/Scripts/BS_PressReleaseDispla y.aspx?prid=52105 [4] Liu, X., Wang, G., & Zha, H. (2023). FinGPT: Democratizing Internet-scale Data for Financial Large Language Models. arXiv:2306.06031. https://arxiv.org/abs/2306.06031 [5] Wu, S., et al. (2023). BloombergGPT: A Large Language Model for Finance. arXiv:2303.17564. https://arxiv.org/abs/2303.17564 [6] Park, J.S., et al. (2023). Generative Agents: Interactive Simulacra of Human Behaviour. Proceedings of ACM UIST 2023, pp. 1–22. https://doi.org/10.1145/3586183.3606763 [7] Fi Money. (2025). Fi MCP Server: Personal Finance Model Context Protocol — Technical Documentation v1.2. Fi Financial Services Ltd., Bengaluru. https://fi.money/mcp [8] Google Cloud. (2024). Vertex AI Agent Builder: Multi-Agent Orchestration — Product Documentation. Google LLC. https://cloud.google.com/vertex-ai/docs/agents [9] SEBI. (2024). Investment Adviser Regulations and AI-Generated Financial Advice. SEBI Circular SEBI/HO/IMD/IMD-II/P/CIR/2024. https://www.sebi.gov.in/legal/circulars/2024 [10] Thaler, R.H., & Sunstein, C.R. (2008). Nudge: Improving Decisions About Health, Wealth, and Happiness. Yale University Press, New Haven. https://yalebooks.yale.edu/book/9780300122237/nudg e/
Publication Date
5/1/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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