When Classical Models Are Preferable to LLMs for Structured Financial Data
For numerical classification, regression, scoring, and forecasting on financial tables, classical statistical models, Random Forest, and especially GBDT should usually be evaluated before LLMs. They are preferable with limited samples, a fixed feature schema, and strict requirements for arithmetic accuracy, calibration, latency, reproducibility, and auditability. LLMs provide more value at the language boundary of the task – working with reports, natural-language queries, and SQL or Python orchestration – but that is a comparison of systems, not just models.