Analysis on Fraudulent Bank Account Data Detector Using Ensemble Learning

Marvel Lemuel Junaidi, Farrell Marcello Lienardi, Nathan Setiawan, Hidayaturrahman

2025

Abstract

As the modern age has expanded in recent years, complications such as fraudulent data in the banks follow as well. As one of the efforts for countermeasure, a study is conducted to analyze an ensemble learning method, namely RFNet and compare it to two other models, namely XBNet and DevNet, using Logistic Regression to stack the models. The study is conducted on a publicly available bank account dataset to evaluate the performance of these models based on their respective accuracy, precision, recall, and ROC-AUC scores, as well as their execution times. As for the statistical approach, we also measured the confidence interval with 95% confidence, along with the standard deviation to show model reliability, stability, and consistency. The results of this study show that XBNet still outperforms the other methods in terms of overall performance, consistency, and reliability. Even so, the RFNet model can be an applicable alternative for fraud detection in certain scenarios and can compete in consistency in some metrics, while also outdoing a well-known DevNet across several metrics. These results highlight the importance of model choice and tuning when conducting tasks involving large amounts of data, especially when dealing with imbalanced data.

Paper Citation

in Harvard Style

Lemuel Junaidi M., Marcello Lienardi F., Setiawan N. and Hidayaturrahman. (2025). Analysis on Fraudulent Bank Account Data Detector Using Ensemble Learning. In Proceedings of the 1st International Conference on Research and Innovations in Information and Engineering Technology - Volume 1: RITECH; ISBN 978-989-758-784-9, SciTePress, pages 196-202. DOI: 10.5220/0014268100004928

in Bibtex Style

@conference{ritech25,
author={Marvel Lemuel Junaidi and Farrell Marcello Lienardi and Nathan Setiawan and Hidayaturrahman},
title={Analysis on Fraudulent Bank Account Data Detector Using Ensemble Learning},
booktitle={Proceedings of the 1st International Conference on Research and Innovations in Information and Engineering Technology - Volume 1: RITECH},
year={2025},
pages={196-202},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0014268100004928},
isbn={978-989-758-784-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Innovations in Information and Engineering Technology - Volume 1: RITECH
TI - Analysis on Fraudulent Bank Account Data Detector Using Ensemble Learning
SN - 978-989-758-784-9
AU - Lemuel Junaidi M.
AU - Marcello Lienardi F.
AU - Setiawan N.
AU - Hidayaturrahman.
PY - 2025
SP - 196
EP - 202
DO - 10.5220/0014268100004928
PB - SciTePress