In this paper we address the problem related to determination of the most suitable candidates for an M&A (Merger &Acquisition) scenario of Banks/Financial Institutions. During the pre-merger period of an M&A, a number of candidates may be available to undergo the Merger/Acquisition, but all of them may not be suitable. The normal practice is to carry out a due diligence exercise to identify the candidates that should lead to optimum increase in shareholder value and customer satisfaction, post-merger. The due diligence ought to be able to determine those candidates that are unsuitable for merger, those candidates that are relatively suitable, and those that are most suitable. Towards achieving the above objective, we propose a Fuzzy Data Mining Framework wherein Fuzzy Cluster Analysis concept is used for advisability of merger of two banks and other Financial Institutions. Subsequently, we propose orchestration/composition of business processes of two banks into consolidated business process during Merger &Acquisition (M&A) scenario. Our paper discusses modeling of individual business process with UML, and the consolidation of the individual business process models by means of our proposed Knowledge Based approach.
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机译:Active Comparison as a Means of Promoting the Development of Abstract Conditional Knowledge and Appropriate Choice of Diagrams in Math Word Problem Solving
机译:企业所有制属性、组织认同度与员工创新能力研究 —基于航天产业视角 =Enterprise Ownership Attributes, Organizational Identity and Employees' Innovation: Based on the Perspective of Aerospace Industry