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In regulated workflows such as Financial Planning and Analysis (FP&A), an answer must be traceable to authoritative sources, explainable in domain terms, and auditable after the fact. The work proposes evaluating retrieval-augmented generation (RAG) for enterprise finance on auditability alongside accuracy and introduces the Knowledge-Driven Analytics Framework (KDAF).",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/auditable-by-construction-an-ontology-driven-framework-for-trustworthy-llm-analytics-in-enterprise-finance/273811/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/auditable-by-construction-an-ontology-driven-framework-for-trustworthy-llm-analytics-in-enterprise-finance/273811.png","ImageObject",300,407,{"name":92,"@type":93},"Clementine","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-15",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",18,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What core trust requirement restricts LLM adoption in enterprise finance?","Question",{"text":113,"@type":114},"In regulated settings, LLM answers must be traceable to authoritative sources, explainable in domain terms, and auditable after the fact.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does the proposed framework make responses auditable by construction?",{"text":118,"@type":114},"The Knowledge-Driven Analytics Framework (KDAF) uses ontology-driven, typed, provenance-annotated evidence retrieval across six iterative stages so every retrieved fact includes relationship type, confidence, and source lineage.",{"name":120,"@type":111,"acceptedAnswer":121},"What does the FinanceBench evaluation show about auditability versus answer accuracy?",{"text":122,"@type":114},"Retrieval is necessary, but accuracy alone does not justify structured retrieval costs; auditability properties change the ordering, with KDAF achieving the highest citation traceability F1.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},273811,1789472853,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":46,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":31,"preview_page_count":4},1374391974564,"https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002","Auditable by Construction: An Ontology-Driven Framework for Trustworthy LLM Analytics in Enterprise Finance  \nSergiy Lunyakin, Senior Member IEEE  \nIndependent Researcher —Seattle, USA  \n[slunyakin@outlook.com](slunyakin@outlook.com)  \nPreprint / Working Manuscript  \nAbstract  \nEnterprise adoption of large language models (LLMs) in finance is constrained less by fluency than by trust: in Financial Planning and Analysis (FP&A) and other regulated workflows, an answer is usable only if it is traceable to authoritative sources, explainable in domain terms, and auditable after the fact. This paper argues that retrievalaugmented generation (RAG) for enterprise finance should be evaluated on auditability alongside accuracy, and presents a framework designed to make grounded responses auditable by construction. The Knowledge-Driven Analytics Framework (KDAF) builds ontology-driven knowledge systems through six iterative stages—problem-centric scoping via competency questions, ontology bootstrapping through a minimum viable graph, schema-guided knowledge extraction, contextual knowledge representation with typed relevance and provenance annotations, hybrid human-in-the-loop validation, and Context-Aware Relevance Propagation (CARP) for graph-based evidence retrieval—so that every retrieved fact carries its relationship type, confidence, and source lineage.  \nA proof-of-concept evaluation on FinanceBench (145 questions) compares KDAF against zero-context inference, BM25 sparse retrieval, concept-weighted lexical retrieval over text and tables, and ungrounded graph traversal. Three findings structure the results. First, retrieval is necessary: zero-context inference reaches 4. 1% correctness against 10-12% for every retrieval-augmented condition. Second, on answer correctness the retrieval conditions are statistically indistinguishable —KDAF and BM25 differ by-0.007 (95% CI [-0 .021, 0 .000])—so accuracy alone does not justify the cost of structured retrieval on single-document filings questions, a negative result we report explicitly. Third, on the properties auditability depends upon, the ordering reverses: KDAF attains the highest citation traceability F1 (0 .515), exceeding ungrounded graph traversal by +0.027 (95% CI [0 .006, 0.050]) and BM25 by +0.052 (95% CI [0 .024, 0.083])  \n—intervals that exclude zero. Separately, graph-structured retrieval of either kind admits no evidence from outside the question subject entity (0 of 426 and 0 of 424 items, against 16.8% and 20.2% for the lexical baselines), because the entity model supplying the provenance chain also makes that boundary expressible as an enforced constraint; every selected item resolves to a complete provenance chain. We argue that auditability, not raw question-answering accuracy, is the axis on which ontology-grounded retrieval earns its construction cost, and outline the FP&A-aligned evaluation this motivates.  \nKeywords: semantic computing, knowledge graphs, ontology engineering, enterprise finance, explainable artificial intelligence, auditability, retrieval-augmented generation, graph-RAG  \n1. Introduction  \n1.1 Motivation  \nAnalytical models used in financial decision-making operate under explicit supervisory expectations. Longstanding regulatory guidance on model risk management requires that models be documented in sufficient detail for an independent party to understand their operation, that their inputs and outputs be traceable, and that they be subject  \nto effective challenge—critical analysis by objective, informed reviewers able to identify limitations and compel change [Board of Governors of the Federal Reserve System & OCC, 2011] . Guidance specific to artificial intelligence extends the same logic: accountability, transparency, explainability, and interpretability are named characteristics of trustworthy AI systems, and the framework treats the ability to explain a system's output as a precondition for managing its risk rather than as an optional enhance","cbCaieIuj8XD1Xx4","https://ap.wps.com/l/cbCaieIuj8XD1Xx4","pdf",453485,"English","# Abstract\n# Keywords\n# 1. Introduction\n## 1.1 Motivation","[{\"question\":\"What core trust requirement restricts LLM adoption in enterprise finance?\",\"answer\":\"In regulated settings, LLM answers must be traceable to authoritative sources, explainable in domain terms, and auditable after the fact.\"},{\"question\":\"How does the proposed framework make responses auditable by construction?\",\"answer\":\"The Knowledge-Driven Analytics Framework (KDAF) uses ontology-driven, typed, provenance-annotated evidence retrieval across six iterative stages so every retrieved fact includes relationship type, confidence, and source lineage.\"},{\"question\":\"What does the FinanceBench evaluation show about auditability versus answer accuracy?\",\"answer\":\"Retrieval is necessary, but accuracy alone does not justify structured retrieval costs; auditability properties change the ordering, with KDAF achieving the highest citation traceability F1.\"}]","Auditable by Construction - An Ontology-Driven Framework for Trustworthy LLM Analytics in Enterprise Finance | PDF"]