Financial institutions face stringent regulatory mandates regarding analytical models used in corporate decision-making. Longstanding model risk management guidelines demand complete input-output traceability, comprehensive documentation, and effective challenge by independent reviewers. Although large language models exhibit strong linguistic fluency, standard retrieval-augmented generation systems routinely fail in Financial Planning and Analysis workflows because their generation mechanisms conceal evidence lineages.
The Auditability Deficit in Standard RAG
Independent researcher Sergiy Lunyakin, a Senior Member of the IEEE, addresses this structural shortcoming in a preprint evaluating the Knowledge-Driven Analytics Framework. The architecture aims to make financial language model answers auditable by construction across six developmental stages: competency-question scoping, minimum viable graph bootstrapping, schema-guided extraction, contextual knowledge representation with typed relevance and provenance annotations, hybrid human-in-the-loop validation, and Context-Aware Relevance Propagation for graph-based retrieval.
Traditional vector and lexical retrieval engines act like index searchers that pull isolated fragments without verifying entity relationships, often returning text snippets belonging to unrelated corporate entities.
When standard retrieval models feed unstructured context into an LLM, the model synthesizes plausible answers while masking extraction errors and cross-entity confusions. To establish rigorous oversight, every fact retrieved by the analytical framework carries an explicit relationship type, a confidence score, and a full source lineage.
Measuring Provenance Over Raw Accuracy
A proof-of-concept benchmark conducted on 145 questions from the FinanceBench dataset reveals the exact empirical trade-offs of structured semantic retrieval. Baseline zero-context inference produced a correctness score of 4.1%, confirming that external retrieval is necessary. However, across retrieval-augmented conditions, raw answer correctness remained statistically indistinguishable: the Knowledge-Driven Analytics Framework and BM25 sparse retrieval differed by -0.007 with a 95% confidence interval spanning -0.021 to 0.000, establishing that structured retrieval does not improve accuracy on isolated single-document financial filing questions.
The real operational divergence appears in verification metrics. The Knowledge-Driven Analytics Framework achieved a citation traceability F1 score of 0.515, outperforming ungrounded graph traversal by +0.027 with a 95% confidence interval of 0.006 to 0.050, and surpassing BM25 by +0.052 with a 95% confidence interval of 0.024 to 0.083. Furthermore, lexical baselines admitted out-of-boundary evidence in 16.8% and 20.2% of cases, whereas graph-structured methods admitted zero external items across 426 and 424 retrieved elements because explicit entity boundaries function as enforceable retrieval constraints.
What this means
The empirical findings demonstrate that the true business value of ontology-driven retrieval lies in rigorous provenance enforcement and boundary compliance rather than raw question-answering accuracy gains. The primary limitation of the benchmark rests on its single-document filing scope, leaving open questions about performance across messy multi-period ledgers and cross-filing audits. For enterprise FP&A teams, adoption comes down to an explicit trade-off: justifying the heavy upfront engineering overhead of bootstrapping domain ontologies against the non-negotiable compliance costs of ungrounded model risk.