Modern financial institutions operate within highly distributed enterprise ecosystems characterized by fragmented databases, heterogeneous transaction platforms, disconnected customer records, and complex cybersecurity infrastructures. The increasing volume, velocity, and diversity of financial data generated across banking systems, digital payment platforms, cloud services, fraud monitoring tools, and regulatory repositories have intensified the challenge of achieving accurate entity identification and secure data unification. Traditional entity resolution approaches frequently struggle with inconsistencies in naming conventions, duplicate records, missing attributes, cross-platform incompatibilities, and evolving cyber threats, thereby limiting operational efficiency, compliance accuracy, fraud detection, and enterprise-wide intelligence generation. This study examines AI-driven entity resolution architectures designed to unify fragmented financial data across enterprise security environments through the integration of machine learning, graph analytics, probabilistic matching, natural language processing, and adaptive trust-aware security mechanisms. The proposed framework leverages intelligent data linkage models, secure interoperability layers, behavioral correlation analytics, and zero-trust access controls to enhance data consistency, identity reconciliation, and cyber-resilience within distributed financial infrastructures. Furthermore, the study evaluates the role of automated anomaly detection, federated data governance, and privacy-preserving AI techniques in improving decision-making, regulatory compliance, anti-money laundering operations, and real-time threat intelligence. The findings demonstrate that AI-enabled entity resolution architectures significantly strengthen enterprise financial visibility, operational security, and scalable digital transformation capabilities across modern financial ecosystems.