Um modelo estratégico para a análise de crédito utilizando redes neurais artificiais

• Autor(a) principal: Pires, Íris Rosane Netto
• Data de Publicação: 2008
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UFU
• Texto Completo: https://repositorio.ufu.br/handle/123456789/14390
• topic: Crédito; Análise de crédito; Análise subjetiva de crédito; Ativos intangíveis; Redes neurais artificiais; Administração de crédito; Redes neurais (Computação); Credit; Credit analysis; Subjective credit analysis; Intangible assets; Artificial neural networks; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA

The present work reflects a case study done at a factoring company, acting in the area of market fomenting. This work focuses on the negotiations related to financial credit done with its client businesses, notably micro and small companies. It examines the credit analysis system utilized, highlighting that, after a selected bibliographical review and a deep study of its method and analysis process, some gaps were found which give its negotiations credit concession actions of high risk. With the objective of giving support to the analysts of the aforementioned company in the analysis process, as well as conferring a more efficient directive for decision making, this work proposes a strategic credit analysis model which uses contemporary approaches with the objective of aggregating value and giving new emphasis to the model used. The proposed model is based on the utilization of two conceptual tools: subjective credit analysis, applied in a standardized way and concepts of intangible assets to qualify and quantify the portfolio management risks; and a computational tool, artificial neural network techniques specifically, to process, learn and generalize the proposed model, and from there, form a more accurate diagnosis for future clients. Therefore, after the elaboration of the method, several tests were made, the results of which were considered promising and with a good proficiency level. Not only was the contemporary approach utilized considered prominent, but also the application of neural networks demonstrated high performance handling the multivariate data given to it.