LLM-Inspired Ontology-Based Semantic Enrichment for FinTech M&A Intelligence in SEC Structured Disclosures

Abstract

This study develops an ontology-based event-intelligence framework for FinTech merger-and-acquisition evidence in U.S. Securities and Exchange Commission (SEC) structured disclosures. Five quarterly SEC Financial Statement Data Sets from 2025Q1 through 2026Q1 contain 32,254 filings, 7,335 registrants, and 18,312,494 numerical XBRL facts; validation adds a 1,800-filing full-text Form 8-K sample and 192 FDIC events. A deterministic ontology maps XBRL tag names, labels, and documentation to acquisition, disposition, valuation, integration, risk, and payment concepts. The method is therefore LLM-inspired semantic enrichment rather than direct LLM extraction. Logistic regression, decision tree, and random forest classifiers are evaluated in three expanding forward-quarter tests with training-only preprocessing and threshold selection. A proximal protocol retains semantically close predictors, whereas a strict protocol excludes label-generating variables and deterministic descendants. Across the rolling tests, proximal logistic-regression M&A detection attains mean F1 = 0.981941, ROC-AUC = 0.999517, and average precision = 0.998315; strict performance falls to F1 = 0.735640, ROC-AUC = 0.930870, and average precision = 0.824107. FinTech M&A F1 declines from 0.921198 to 0.449503. Strict random forests yield F1 = 0.798129 for integration risk and 0.753987 for valuation signals. In independent full text, strict main-text-plus-exhibit F1 is 0.297482; among 24 automatically linked FDIC events in rolling test quarters, 9 are detected. Near-perfect scores therefore describe ontology reconstruction, not transaction-level accuracy.