The author argues against letting Large Language Models (LLMs) make final decisions on financial transactions like taxes, discounts, and pricing. While LLMs are being integrated into checkout flows, their probabilistic nature is unsuitable for deterministic financial choices. Benchmarks like TaxCalcBench show even advanced models struggle with accuracy and consistency on tax calculations. This unreliability extends to other money-moving decisions such as promo eligibility and discount stacking. The proposed solution is a division of labor: LLMs translate natural language rules into auditable specifications, which are then executed by sound, deterministic engines. This approach ensures accuracy and auditability by separating the fuzzy language understanding from the precise logical execution. The LLM acts as a translator, and a separate engine serves as the judge, applying the translated rules consistently. This method has been validated across numerous experiments and model families, consistently improving accuracy on rule-based problems. The key requirement for this approach is a capable LLM for the formalization step; a weak model will produce incorrect specifications, leading to confident but wrong answers from the deterministic engine. The method is applicable when a verifiable "gold oracle" exists, rules are specifiable, and the task is finite and decidable. Tasks like tax and payroll calculations, where accuracy and auditability are paramount, are prime candidates for this hybrid approach.