A Predictive Risk Assessment Interface with Financial Valuation Metrics for Sector-Sensitive M&A Transactions in Healthcare and Telecommunications

This study presents the design and evaluation of a Predictive Risk Assessment User Interface (UI) integrated with financial valuation metrics, tailored specifically for mergers and acquisitions (M&A) in the healthcare and telecommunications sectors. These industries pose unique challenges due to regulatory complexity, technological disruption, and capital intensity, requiring advanced analytical tools beyond traditional spreadsheet-based methods. The proposed system synthesizes multi-dimensional data inputs, including transaction characteristics, sector-specific compliance flags, and financial indicators, into a unified digital platform. It leverages machine learning algorithms for dynamic risk scoring across regulatory, market, technology, financial, and operational dimensions, while simultaneously computing deal valuations through embedded models such as discounted cash flow, comparable company analysis, and synergy uplift estimation. A real-time scenario simulation engine and explainable AI components enhance transparency and support strategic decision-making. The interface’s sector-sensitive logic automatically adjusts risk weights and valuation assumptions based on industry-specific parameters, ensuring relevant and precise outputs. Simulation results from representative healthcare and telecom M&A cases demonstrate the platform’s capacity to provide actionable insights through composite risk scores, valuation ranges, and synergy projections, thereby improving deal defensibility and due diligence efficiency. This work contributes a novel, integrated digital solution that elevates M&A analytics, fosters regulatory compliance, and streamlines cross-functional collaboration for complex, sector-sensitive transactions.