Machine Learning applied to Projected Financial Statements (PFS) (#1420)
Read ArticleDate of Conference
July 19-21, 2023
Published In
"Leadership in Education and Innovation in Engineering in the Framework of Global Transformations: Integration and Alliances for Integral Development"
Location of Conference
Buenos Aires
Authors
Bellido-Zea, Coster
Villalobos-Meneses, Bertha
Alfaro Rodriguez, Carlos
Grados-Espinoza, Anna
Gomero-Ostos, Nestor
Hoyos-Rivas, Fernando
Ramirez-Veliz, Francisco
Abstract
Projected financial statements represent one of the most reliable sources when it comes to making decisions involving the company's long-term performance. Therefore, finding methods to optimize their preparation and accuracy is the holy grail of financial accounting. The objective of this research is to use machine learning in projected financial statements, in order to obtain more accurate data through training in a tetradimensional space or also called Euclidean space of n dimensions.