AI-Driven Policy Simulations for Global Economic Stability

Abstract

 The rising intricacy of worldwide financial frameworks requires creative ways to deal with strategy assessment and navigation. This examination paper presents an original structure utilizing computerized reasoning (man-made intelligence) to recreate strategy effects and upgrade worldwide monetary solidness. The review utilizes a thorough system, beginning with data normalization to normalize different financial pointers, guaranteeing similarity across datasets. Correlation analysis is used for highlight determination, confining key financial factors that altogether impact results while diminishing overt repetitiveness. For grouping, the Temporal Combination Transformer (TFT), a state of the art profound learning model, is applied to catch transient conditions and complex collaborations inborn in monetary information. The proposed structure exhibits its viability in anticipating the results of monetary arrangements, giving significant bits of knowledge into likely dangers and advantages. Results feature the model's capacity to oblige dynamic monetary circumstances and upgrade interpretability through its vigorous consideration instrument. This research highlights the groundbreaking capability of artificial intelligence driven reproductions in cultivating informed policymaking and guaranteeing financial versatility.