INTELLIGENT AUTOMATION IN POST-MERGER INTEGRATION: LEVERAGING AI FOR ENTITY MATCHING, DATA MAPPING, AND DEDUPLICATION

Abstract

Post-Merger Integration (PMI) processes face persistent challenges in harmonizing heterogeneous datasets across systems with disparate schemas, inconsistent entity identifiers, and significant record duplication. Manual integration pipelines are inherently non-scalable and prone to semantic mismatches, undermining the velocity and reliability of M&A outcomes. This research presents a machine learning–driven automation framework for entity matching, schema-based data mapping, and deduplication tailored for PMI scenarios. The proposed architecture leverages a hybrid approach combining supervised learning, natural language processing (NLP), and rule-based heuristics to extract, normalize, and reconcile business entities across legacy enterprise systems. For entity resolution, we employ vectorized token similarity models (TF-IDF, word embeddings) with ensemble classifiers (Random Forest, XGBoost) trained on labeled entity-pair datasets. Data mapping is supported by transformer-based models for semantic field alignment, while deduplication leverages hierarchical clustering and active learning strategies for adaptive thresholding. Experimental validation using synthetic and anonymized merger datasets shows up to 92% precision and 89% recall in entity matching, a 65% reduction in integration time, and a 40% improvement in deduplication efficiency compared to rule-based baselines. This work demonstrates the efficacy of intelligent automation in accelerating post- merger data harmonization and sets the stage for scalable data consolidation architectures in complex enterprise integrations.