A research framework called CIFQA improved answers to calculation-heavy financial questions by keeping arithmetic and business rules outside the language model. Specialized agents interpret a request, choose a route, extract parameters, plan the calculation and write the response, while deterministic Python tools handle rates, dates and formulas.
The researchers implemented the system for fixed-deposit questions, where apparently simple answers can depend on exact tenure, changing rate cards, rolling-year adjustments and penalties for early withdrawal. On a curated benchmark, CIFQA reached 95.54% accuracy for calculation-intensive questions and 90.87% overall. A 17-billion-parameter open model inside the framework reportedly outperformed much larger models given the same formulas and financial information.
Ablation tests found that exact rate lookup, tenure computation and withdrawal logic were important contributors rather than decorative tool calls. The result supports a practical architecture for regulated or numerical applications: let a model understand language and explain an outcome, but require tested software to execute rules that must be exact. The evidence is limited to a curated fixed-deposit benchmark, not all financial advice, and a deterministic calculator is only as reliable as its rates and rules. Production deployments would still need current data, audit logs and tests for ambiguous requests before presenting an answer to a customer.