Bank Statements to Network Features: Extracting Features Out of Time Series Using Visibility Graph

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2022

Autores

Shaji, N (Autor) Outra; João Gama (Autor) FEP; Rita Ribeiro (Autor) FCUP; Gomes, P (Autor) Outra

Ata de Conferência Internacional

Título: Advances in Intelligent Data Analysis XX - 20th International Symposium on Intelligent Data Analysis, IDA 2022, Rennes, France, April 20-22, 2022, Proceedings

Páginas: 278-289

Indexação

ISI Web of Knowledge - 1 Citação

Scopus - 1 Citação

Outras Informações

ID Authenticus: P-00W-C11

DOI: 10.1007/978-3-031-01333-1_22

Abstract (EN): Non-traditional data like the applicant¿s bank statement is a significant source for decision-making when granting loans. We find that we can use methods from network science on the applicant¿s bank statements to convert inherent cash flow characteristics to predictors for default prediction in a credit scoring or credit risk assessment model. First, the credit cash flow is extracted from a bank statement and later converted into a visibility graph or network. Afterwards, we use this visibility network to find features that predict the borrowers¿ repayment behaviour. We see that feature selection methods select all the five extracted features. Finally, SMOTE is used to balance the training data. The model using the features from the network and the standard features together is shown having superior performance compared to the model that uses only the standard features, indicating the network features¿ predictive power. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Idioma: Inglês

Tipo (Avaliação Docente): Científica