Artificial intelligence in visually rich document digitization for efficient asset management

This work addresses the use of digital document processing as well as its application within the financial market, with an emphasis on the automated extraction of data from balance sheet documents, data that is common in the day-to-day life of this sector and that needs to be precise due to its large number of fields and recurrence. To perform this function, optical recognition technologies such as OCR, semantic entity recognition (SER) and named entity recognition (NER) were chosen to be used. The present work is an applied research with the main objective of developing a platform with the focus of optimizing the process of extracting and digitizing data from balance sheet documents, thus reducing errors and increasing operational efficiency in the asset management process. The methodology used involves the study of financial projection layouts, the development of an updated automated data extraction model, and validation mechanisms to guarantee result consistency.