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CIFQA Framework Boosts LLM Accuracy for Financial Queries

Kunjesh Parekh, Anil Kumar Tiwari, Divya Saxena· August 28, 2026 View original

Key takeaways

  • LLMs alone often fail at precise financial calculations, producing numerically incorrect answers.
  • CIFQA improves accuracy by using specialized agents and deterministic tools for calculations.
  • Architectural design is more critical than model scale for numerical reliability in financial AI.
  • The framework is generalizable beyond fixed deposit queries to other complex financial reasoning.

Who benefits

BFSIFinTechInvestment ManagementInsurance

Summary

CIFQA is a multi-agent LLM framework designed to improve the accuracy of calculation-intensive financial question answering by separating language understanding from numerical execution. It uses specialized agents and deterministic Python tools to achieve high accuracy, outperforming direct LLM baselines.

Large Language Models often struggle with the precise numerical calculations required for complex financial queries, frequently producing plausible but incorrect answers. To address this, researchers developed CIFQA, a deterministic, tool-grounded multi-agent LLM framework. This system delegates tasks like query interpretation, parameter extraction, and response generation to specialized AI agents, while deterministic Python tools handle the actual financial calculations and rule applications. Evaluated on fixed deposit queries, CIFQA achieved 95.54% accuracy on calculation-intensive questions and 90.87% overall. This significantly surpasses direct LLM performance, even when those models are given complete financial data. The study highlights that deterministic components, such as exact rate lookups and tenure computations, are crucial for reliability. Notably, an open-source 17B model within the CIFQA architecture outperformed much larger frontier models, suggesting that the architectural design is more critical for numerical reliability in financial tasks than raw model scale. While tested on fixed deposits, the framework is designed to be generalizable for other calculation-intensive financial reasoning.

Why it matters

Professionals in finance and AI development can leverage this framework to build more reliable and accurate AI systems for financial operations, reducing errors in critical calculations. It demonstrates that strategic architectural design can be more impactful than simply scaling up model size for specific, precision-demanding tasks.

How to implement this in your domain

  1. 1Evaluate existing financial query systems for numerical accuracy bottlenecks.
  2. 2Design a multi-agent architecture that separates natural language understanding from deterministic calculation.
  3. 3Integrate robust, deterministic tools (e.g., Python libraries) for all numerical and rule-based financial computations.
  4. 4Develop specialized LLM agents for tasks like query parsing, parameter extraction, and response generation.
  5. 5Benchmark the integrated system against direct LLM approaches and human-level accuracy on critical financial tasks.

Original post by Kunjesh Parekh, Anil Kumar Tiwari, Divya Saxena

"arXiv:2608.26114v1 Announce Type: new Abstract: Calculation-intensive financial question answering requires exact reasoning over structured rates, temporal conditions, numerical formulas, and rule-based constraints. Although Large Language Models (LLMs) perform strongly on natura…"

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Originally posted by Kunjesh Parekh, Anil Kumar Tiwari, Divya Saxena on X · view source

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