JoWUA

Abstract

The application of Artificial Intelligence (AI) and Natural Language Processing (NLP) in Management Information System (MIS) has automated data interpretation and its real-time processing, accuracy, and decision automation have significantly improved. Like other systems in finance, Supply Chain Management (SCM), healthcare, and even market trend analysis, traditional MIS systems do not scale well due to the dependency on real-time analytics and adaptability. This paper assesses how AI powered NLP models modify Traditional MIS systems by translating data from both structured and unstructured forms into applicable business intelligence. Out of all the advanced NLP methods, few-shot learning, low-resource NLP model, and hybrid deep learning architecture were selected to be tested against traditional AI-based data processing frameworks to provide conclusive proof of the greater acceleration of data-driven decision making achieved with AI-based NLP. Using experimental research and practical case studies, examine the use of AI-NLP in five key areas of financial risk management: data interpretation, customer sentiment analysis, supply chain predictive analytics, and decision-making optimization. The findings reveal that operational NLP models led to a 65% increase in the speed of data interpretation, and a 30% increase in predictive accuracy, in addition to a significant 50% decrease in operational decision latency when compared to traditional frameworks AI powered NLP MIS. Further, estimate the cost impact of AI-NLP-driven business information systems in terms of cost-efficiency, scalability and automation, which AI-NLP powered systems greatly advanced trend forecasting, risk mitigation, and decision-making subsequently staggering less other business units. This research also considers the challenges of model interpretability, data privacy, computation burden, and cost efficiency while providing adaptable AI solutions for the next generation of business information systems. The research results support a strategic framework for organizations that aim to replace rule-based management information systems with AI-powered automated decision support systems which use deep learning, reducing operational expenditures, increasing efficiency, and providing real-time agility on strategic decisions.