Digital Repository

A Systematic Investigation on the Effectiveness of the Tabbert Model for Credit Card Fraud Detection

Show simple item record

dc.contributor.author Hewapathirana, Isuru Udayangani
dc.contributor.author Kekayan, Nanthakumar
dc.contributor.author Diyasena, Deshanjali
dc.date.accessioned 2024-10-24T07:03:49Z
dc.date.available 2024-10-24T07:03:49Z
dc.date.issued 2022
dc.identifier.citation I. Hewapathirana, N. Kekayan and D. Diyasena, "A Systematic Investigation on the Effectiveness of the Tabbert Model for Credit Card Fraud Detection," 2022 International Research Conference on Smart Computing and Systems Engineering (SCSE), Colombo, Sri Lanka, 2022, pp. 96-101, doi: 10.1109/SCSE56529.2022.9905208 en_US
dc.identifier.uri http://repository.kln.ac.lk/handle/123456789/28585
dc.description.abstract As a result of rapid digitisation, online transactions using credit cards have become popular. With this, fraudulent activities have also increased considerably. Although many supervised and unsupervised machine learning techniques were proposed in past research for identifying fraudulent transactions, they do not fully utilize the tabular and hierarchical structure present in transaction datasets. Recently, the TabBERT neural network model was proposed to calculate row-wise embeddings that capture both inter and intra dependencies between transactions in tabular time series data. In this research, we present a systematic experimental framework to assess the effectiveness of applying the embeddings calculated using the TabBERT model for credit card fraud detection. We employ the calculated row embeddings for fraud detection using three unsupervised machine learning algorithms and two supervised machine learning algorithms. We perform our experiments on a synthetic dataset that has been generated using the TabGPT model. Overall, TabBERT-based embeddings increase the performance of the supervised learning models with the extreme gradient boosting model achieving a precision of 99% and an F1 score of 98%, and the multilayer neural network model achieving a precision of 97% and an F1 score of 95%. For unsupervised learning, the use of TabBERT embeddings increases the recall rate of K-means clustering algorithm by 0.19%. en_US
dc.subject fraud detection, TabBERT, tabular time series en_US
dc.title A Systematic Investigation on the Effectiveness of the Tabbert Model for Credit Card Fraud Detection en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search Digital Repository


Browse

My Account