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SME credit application, a text classification approach
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Resumo(s)
During the SME credit application process a credit expert will give a specific recommendation to the
credit commercial advisor. This recommendation can be classified as positive, negative or partial. This
project aims to construct a text classifier model in order to give the recommendation text one of the
categories mentioned before. To achieve this, two models are tested using state-of-the-art
architecture called BERT proposed by Google in 2019.
The first model will use single sentence BERT classification model as proposed by Google. The second
model will use SBERT architecture, where BERT embedding model will be fine-tuned for the specific
task, a max-pooling layer is added to extract a fixed size vector for all the document and work under
fully connected network architecture. Results show that the second approach got better results
regarding accuracy, precision and recall. Despite of the bunch of limitations of computational capacity,
limited number of tagged examples and BERT maximum sequence length the model show a good first
approach to solve the current problem.
Descrição
Project Work presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Marketing Research e CRM
Palavras-chave
Natural Language Processing (NLP) Banking Credit application Small and medium enterprise (SME) Neural Networks (NN) Bi-directional Encoder Representations for Transformers (BERT)