Guardrails in Generative AI for Retail Financial Planning
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V7I2P106Keywords:
Generative Artificial Intelligence (Genai), Financial Planning Systems, Retail Investors, AI Guardrails, Responsible AI, Financial Advisory Automation, Risk Management, Regulatory Compliance, Bias Mitigation, Hallucination Prevention, Investment Recommendation Systems, Personal Finance Management, Wealth Management, Explainable AI (XAI), Policy Enforcement Layers, Risk Scoring Models, Financial Data Analytics, AI Governance Frameworks, Model Validation, Ethical AI In FinanceAbstract
Generative Artificial Intelligence (GenAI) is increasingly being adopted in financial planning systems that assist retail investors with investment decisions, budgeting, and long‑term wealth management. While large language models can interpret financial data and provide personalized recommendations, they also introduce risks such as hallucinated advice, regulatory non‑compliance, and biased financial recommendations. Guardrail mechanisms are therefore essential to ensure that AI‑generated financial advice remains safe, compliant, and aligned with financial best practices. This research paper explores the role of guardrails in GenAI‑driven financial planning systems for retail consumers. The study presents the architecture of a guardrail‑enabled financial planning system, discusses datasets used in financial planning models, and explains the calculation logic that supports responsible investment recommendations. Real‑world case studies illustrate how guardrails can prevent harmful outputs and maintain regulatory compliance. The paper also analyzes the integration of policy enforcement layers, risk scoring models, and validation mechanisms in financial AI systems. The findings demonstrate that guardrail frameworks significantly improve reliability and trustworthiness in AI‑driven financial advisory systems. The paper concludes by highlighting the importance of combining machine learning, financial analytics, and governance frameworks to build safe and trustworthy AI‑powered financial planning platforms.
References
1. Kamballi, M., Sanghi, S., Kagalkar, A., Varma, S. C. G., & Gupta, S. (2025, August). AI and Predictive Analytics in Financial Process Engineering. In 2025 International Conference on Sustainability, Innovation & Technology (ICSIT) (pp. 1-5). IEEE.
2. Y. Zhang et al., “Attention-based neural networks for time-series risk forecasting,” IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 2, pp. 1567–1581, 202
3. J. Villegas and S. Haberman, “On the modeling and forecasting of mortality improvement rates,” Journal of the Royal Statistical Society: Series C, vol. 72, no. 3, pp. 566–590, 202
4. M. Richman and M. Wüthrich, “A neural network extension of the Lee–Carter model,” Scandinavian Actuarial Journal, vol. 2022, no. 6, pp. 1–25.
5. S. Debón, F. Martínez-Ruiz, and F. Montes, “Machine learning approaches for mortality modelling: A review,” European Actuarial Journal, vol. 13, pp. 345–378, 20
6. Kagalkar, A., Kabade, S., Chaudhari, B. B., Sharma, A., & Maurya, S. (2025). Artificial intelligence-supported financial planning tool for personalized optimization of pension income (German Utility Model No. DE202025107023U1). Deutsches Patent- und Markenamt. https://patents.google.com/patent/DE202025107023U1/en
7. J. J. Cairns, D. Blake, and K. Dowd, “A two-factor model for stochastic mortality with parameter uncertainty,” ASTIN Bulletin, vol. 52, no. 1, pp. 1–35, 2022.
8. M. Richman and M. Wüthrich, “A neural network extension of the Lee–Carter model,” Scandinavian Actuarial Journal, vol. 2022, no. 6, pp. 1–25.
9. S. Pakkanen et al., “Uncertainty quantification in long-term mortality forecasting,” Insurance: Mathematics and Economics, vol. 114, pp. 65–83, 2024.
