FinVector-Market-4B: A Controlled Study of LoRA Adaptation for Structured Financial Tasks
Training a small AI model to handle financial math and reasoning reliably
A specialized financial AI model improved dramatically at structured money tasks — jumping from near-zero to 91% accuracy at producing correctly formatted output, and from 15% to 40% at getting financial math questions right. The gains came from fine-tuning a small base model on 22,000 financial examples and giving it explicit rules about what kind of output to produce.
Banks and financial apps need AI models they can actually trust to format data correctly and reason through money problems accurately. This shows that even small, efficient models can be tuned to perform reliably on financial tasks without being retrained from scratch — a cheaper path than buying or building larger models. However, the model's performance depends heavily on getting the prompting and output rules exactly right, so real-world deployment would require careful testing on your specific use cases.