Post Mergers and Acquisitions Integration Solution using Machine Learning

With the rapidly transforming digital world, various smart platforms/Post M&A Integration solutions are being utilized to accelerate the transition process of M&A activities. But the procedures include considering innumerable important factors such as finding out common methodologies, gap analysis, customized planning, redaction of documents, contract analysis etc., which need to be taken care of within relatively less time as industries are growing exponentially. In our study, we developed an ML-based solution which is intended to automate and improve the process of integration and planned post-merge activities. The paper presents a solution that focuses on identifying process synergies, reducing dependencies on human manual evaluation/analysis to reduce errors and also, keeps bird-eye view on overall process in order to track the status of activities to close. ML-powered Post M&A Integration solution helps to save time which is consumed in analysis like identifying gaps or similarities by comparing huge amounts of data, reviewing hundreds of checklists. We are maintaining accurate questionnaires for various domains at granular level considering all basic as well as trending sectors to evaluate synergies between firms by comparing their responses using COSINE similarity functionality of machine learning. The calculated score projects a checklist of items to tackle for filling the gaps by both organizations in the entire process to have smooth completion of M&A activities.