Leveraging Artificial Intelligence for Advanced Deal Sourcing in U.S. Mergers and Acquisitions to Improve Financial Efficiency

This paper explores how artificial intelligence technologies improve deal sourcing efficiency in U.S. merger and acquisition activities. It addresses the limitations of traditional manual research methods that often lead to missed opportunities and inefficient capital allocation. Using a mixed-methods approach that combines quantitative analysis of 150 U.S. financial institutions over three years with qualitative case studies of twelve firms adopting AI-driven sourcing platforms, the research examines applications of machine learning algorithms, natural language processing, and predictive analytics in identifying acquisition opportunities. The findings show significant improvements in financial efficiency: automated screening reduced deal identification time from six weeks to eight days, transaction search costs dropped by 42 percent, AI systems processed 40 times more potential targets than manual methods, and revenue per deal sourcing professional increased by 31 percent after implementation. Natural language processing tools effectively analyzed unstructured financial data from various sources, while machine learning algorithms achieved 78 percent accuracy in predicting successful deal completion. Nonetheless, challenges include data integration needs, algorithm bias toward specific sectors, and the ongoing need for human oversight in strategic relationship management. The study concludes that AI-driven deal sourcing fundamentally transforms M&A efficiency in U.S. capital markets by enabling faster opportunity identification and more effective resource allocation. However, successful implementation requires a balanced integration of artificial intelligence capabilities with human strategic judgment, with broader implications for financial market competitiveness and regulatory policy adaptation.