Decision Making and Management in Business Organizations for Maximizing the Productive Benefits using Dilated Gated Recurrent Unit with Attention Mechanism

Foreign direct investment usually includes a lack of information, risk, decisions, and uncertainty that depend on a variety of institutional processes. In the international surroundings, Multinational Enterprises (MNEs) have more chances for survival with poor management. In the decision-making procedure, the financial consignment is assisted with the business disclosures. Hence, the automated trader and the financial investor consulted before the ownership was exercised in stock. The major complexity for the decision support system based on the computerization struggles with the natural language ambiguity. The deep learning approach provides a possible remedy that solves the problems of various traditional methods. In this paper, the deep learning network is used for financial decision support that helps to improve the financial performance of the business. The data is collected from the standard database and fed to the Dilated Gated Recurrent Unit with Attention Mechanism (DGRU-AM) for making strategic decisions that improve the organization's efficiency. Strategically decision making held to establish new methods and provide new services that minimize the negative outcome and maximize the productivity benefits. The proposed model achieves higher accuracy as compared to traditional deep learning methods.