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Intelligent architecture to support second generation general accounting
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| 1.96 MB | Adobe PDF |
Orientador(es)
Resumo(s)
This study aimed to innovate the world of accounting software. After so many years, accountants are faced
with an unbelievable amount of work, which is not always productive, effective and efficient for both the
accountant and the company that provided him with the data required to carry out the accounting. There is
already accounting software with various automation processes, from ornamentation to profitability analysis
and management reporting. There is also software that is updated in accordance with the accounting laws,
i.e., the platform changes its mechanisms according to the changes in the law.
Despite the existence of this software, manual work remains, and the amount of information accountants are
faced with is still very large. It is difficult for accountants to do a 100% reliable job with so much information
and data they have. One of the most common situations in the accounting world is undoubtedly the
miscalculation or forgetting of some financial or non-financial data found in accounting operations (income
statements, balance sheets, etc.). To render accounting operations efficient, effective and productive, errorfree
and 100% reliable, an intelligent architecture has been developed to support second generation general
accounting. This architectural design was developed with a view to make the existing software smarter with
the help of artificial intelligence.
A study was carried out on accounting keys and concepts, on AI and main process automation techniques to
build the model. With these studies it was intended to acquire all possible requirements for the creation of the
architecture. Towards the end of the thesis the model was validated.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and Management
Palavras-chave
Intelligent architecture general accounting artificial intelligence RPA and OCR Machine Learning