Numerical-Reasoning-Guarded Financial Statement Agents: Auditable Ratio Analysis, Anomaly Detection, and Selective Filing-Consistency Explanations
DOI:
https://doi.org/10.61424/b6prv293Keywords:
Financial statement agents, XBRL, numerical reasoning, ratio analysis, anomaly detection, LightGBM, calibration, selective prediction, evidence verificationAbstract
Financial-statement agents must resolve accounting context before they calculate or explain a ratio. This study evaluated a guarded architecture on the U.S. Securities and Exchange Commission (SEC) Financial Statement Data Sets for 2026 Q1. The archive contained 6,169 submissions, 3,690,955 numeric facts, 91,794 tag-version records, and 733,134 presentation rows. Accession-, statement-, period-, unit-, segment-, and co-registrant-level filtering produced a 5,436-filing ratio cohort and a strict cohort of 3,612 original 10-K filings. A deterministic balance-sheet rule identified 461 observable reconciliation exceptions; this is a filing-consistency review signal, not fraud, default, bankruptcy, or an accounting adjudication. Across 8,400 ratio questions, an unguarded first-fact agent attained 20.51% tolerance-based numeric match, whereas the context-aligned XBRL engine and independent Decimal calculator attained 100%. The guarded agent released 7,827 answers (93.18% coverage); all met the numeric-match tolerance and were supported by re-queried evidence tuples. On a time-ordered test set of 759 filings, LightGBM achieved PR-AUC 0.6780 (95% bootstrap CI [0.6025, 0.7421]), ROC-AUC 0.8495, F1 0.6643, and expected calibration error 0.0125. Isolation Forest achieved PR-AUC 0.1968, close to the 0.1884 test prevalence. Strict context resolution raised numeric match from 20.51% to 100%, and tuple/formula checks verified every released answer. Calibrated supervised screening improved prioritization of reconciliation exceptions using independent filing features.
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