Synthesizing Behaviorally-Grounded Reasoning Chains: A Data-Generation
  Framework for Personal Finance LLMs
Personalized financial advice requires consideration of user goals,
constraints, risk tolerance, and jurisdiction. Prior LLM work has focused on
support systems for investors and financial planners. Simultaneously, numerous
recent studies examine broader personal finance tasks, including budgeting,
debt management, retirement, and estate planning, through agentic pipelines
that incur high maintenance costs, yielding less than 25% of their expected
financial returns. In this study, we introduce a novel and reproducible
framework that integrates relevant financial context with behavioral finance
studies to construct supervision data for end-to-end advisors. Using this
framework, we create a 19k sample reasoning dataset and conduct a comprehensive
fine-tuning of the Qwen-3-8B model on the dataset. Through a held-out test
split and a blind LLM-jury study, we demonstrate that through careful data
curation and behavioral integration, our 8B model achieves performance
comparable to significantly larger baselines (14-32B parameters) across factual
accuracy, fluency, and personalization metrics while incurring 80% lower costs
than the larger counterparts.
68cb62df5a7803ff3be42cee
Akhil Theerthala
claimed_verified
2025-09-18T13:27:25.124Z
676c67da7bad1587f2d046e2
https://cdn-avatars.huggingface.co/v1/production/uploads/676c67da7bad1587f2d046e2/Y-aJmRj9FB49n36X1qKo9.jpeg
Akhil Theerthala
user
Akhil-Theerthala
Synthesizing Behaviorally-Grounded Reasoning Chains: A Data-Generation
  Framework for Personal Finance LLMs
Personalized financial advice requires consideration of user goals,
constraints, risk tolerance, and jurisdiction. Prior LLM work has focused on
support systems for investors and financial planners. Simultaneously, numerous
recent studies examine broader personal finance tasks, including budgeting,
debt management, retirement, and estate planning, through agentic pipelines
that incur high maintenance costs, yielding less than 25% of their expected
financial returns. In this study, we introduce a novel and reproducible
framework that integrates relevant financial context with behavioral finance
studies to construct supervision data for end-to-end advisors. Using this
framework, we create a 19k sample reasoning dataset and conduct a comprehensive
fine-tuning of the Qwen-3-8B model on the dataset. Through a held-out test
split and a blind LLM-jury study, we demonstrate that through careful data
curation and behavioral integration, our 8B model achieves performance
comparable to significantly larger baselines (14-32B parameters) across factual
accuracy, fluency, and personalization metrics while incurring 80% lower costs
than the larger counterparts.
A novel framework integrates financial context and behavioral finance to fine-tune a Qwen-3-8B model for personalized financial advice, achieving performance comparable to larger models with lower costs.
LLM end-to-end advisors supervision data reasoning dataset fine-tuning Qwen-3-8B behavioral finance factual accuracy fluency personalization metrics