The rapid advancement of large language models (LLMs) is transforming the landscape of automated financial advisory, offering the potential for scalable, personalized, and data-driven guidance. However, current solutions are constrained by significant computational demands, insufficient adaptation to individual user behavior, and the persistence of systematic biases. In this paper, we propose a comprehensive data-centric framework that fundamentally rethinks the training and deployment of personal finance LLMs. Central to our approach is the integration of behavioral finance principles at every stage of the model development pipeline, including an innovative four-phase chain-of-thought generation process that explicitly incorporates users’ psychological states and emotional cues into financial reasoning. We constructed an extensive dataset of 19,000 real-world personal finance queries across eight diverse domains—ranging from debt management to retirement planning—and used it to fine-tune a Qwen-3-8B model. Our multi-faceted evaluation, combining held-out testing, blind LLM-jury studies, and cost-benefit analysis, demonstrates that the resulting 8B-parameter model matches or surpasses the performance of models 2–4 times larger (14–32B parameters) in factual accuracy, response fluency, and degree of personalization, while reducing operational costs by 80%. This framework directly tackles the pitfalls of current agentic and general-purpose approaches, notably their architectural complexity, high maintenance burden, and poor real-world efficiency. By showing that principled behavioral integration and robust data design can substitute for brute-force computational scaling, our work paves the way for practical, accessible, and genuinely trustworthy financial AI systems applicable to a broad range of users and scenarios.