Verifiable Financial Enterprise Question Answering via Inference-Time Grounding and Traceability

Abstract

Financial enterprise AI systems deployed in high-stakes settings require responses that are verifiable, traceable, and auditable. We introduce a modular, model- and data-agnostic inference-time control framework, together with a deployment-aware evaluation strategy for verifiable financial enterprise question answering. Our method enforces faithfulness at inference time without retraining or changes to retrieval infrastructure. We deploy our method in a production financial enterprise assistant and evaluate it using a combination of intrinsic faithfulness metrics, baseline comparisons, and real-world user feedback. Our approach improves groundedness by 29% over baselines, reduces hallucinations to near-zero levels, and achieves near-perfect document-span traceability. Together, our results demonstrate that modular pipeline design combined with detailed, deployment-aware evaluation provides a practical and effective path toward verifiable financial enterprise QA systems.

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