Evaluating Transfer Learning Methods on Real-World Data Streams: A Case Study in Financial Fraud Detection

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2025

Autores

Pereira, RR (Autor) Outra; Bono, J (Autor) Outra; Ferreira, HM (Autor) Outra; Pedro Ribeiro (Autor) FCUP; Carlos Soares (Autor) FEUP; Bizarro, P (Autor) Outra

Ata de Conferência Internacional

Título: Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track - European Conference, ECML PKDD 2025, Porto, Portugal, September 15-19, 2025, Proceedings, Part IX

Páginas: 230-246

Indexação

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-01A-B64

DOI: 10.1007/978-3-032-06118-8_14

Abstract (EN): When the available data for a target domain is limited, transfer learning (TL) methods leverage related data-rich source domains to train and evaluate models, before deploying them on the target domain. However, most TL methods assume fixed levels of labeled and unlabeled target data, which contrasts with real-world scenarios where both data and labels arrive progressively over time. As a result, evaluations based on these static assumptions may not reflect how methods perform in practice. To support a more realistic assessment of TL methods in dynamic settings, we propose an evaluation framework that (1) simulates varying data availability over time, (2) creates multiple domains via resampling of a given dataset and (3) introduces inter-domain variability through controlled transformations, e.g., including time-dependent covariate and concept shifts. These capabilities enable the systematic simulation of a large number of variants of the experiments, providing deeper insights into how algorithms may behave when deployed. We demonstrate the usefulness of the proposed framework by performing a case study on a proprietary real-world suite of card payment datasets. To support reproducibility, we also apply the framework on the publicly available Bank Account Fraud (BAF) dataset. By providing a methodology for evaluating TL methods over time and in different data availability conditions, our framework supports a better understanding of model behavior in real-world environments, which enables more informed decisions when deploying models in new domains.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 16