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Merges and acquisitions (M&As) are widely used strategies by banks to grow and maintain their position in the financial market. In this study we aim to utilize data mining and machine learning approaches to develop interpretable predictive models that are capable of predicting the occurrence of M&As in the banking system in the United States. In addition, we aim to discover the important contributing factors that are of importance for M&As. We utilize both micro/macro-economic data and bank fundamentals for the analysis. We believe that our findings may assist banks in order to make strategic data-driven decisions regarding M&As.