Generative AI-Driven Simulation for Post-Merger Banking Data Integration

The consolidation of banking institutions through mergers and acquisitions (M&A) often introduces significant challenges in data integration due to heterogeneous systems, regulatory requirements, and operational risks. Traditional data migration strategies are limited in their ability to predict integration bottlenecks and ensure compliance in complex financial ecosystems. This study proposes a Generative AI-driven simulation framework to model, test, and optimize post-merger banking data integration processes. By leveraging generative models, the framework creates synthetic yet realistic datasets that replicate diverse banking operations, enabling risk-free scenario testing prior to actual system consolidation. The simulation facilitates anomaly detection, regulatory compliance verification, and workflow optimization, reducing the likelihood of data loss, redundancy, or compliance violations. Empirical evaluation demonstrates that this AI-driven approach enhances integration efficiency, reduces operational downtime, and provides decision-makers with actionable insights for smoother transitions in post-merger environments. The findings highlight the transformative potential of generative AI in addressing one of the most critical pain points in financial mergers—secure, compliant, and efficient data integration.