| Yazarlar (2) |
Ahsen Tetik
T.C. Maltepe Universitesi, Türkiye |
Prof. Dr. Ensar GÜL
Beykoz Üniversitesi, Türkiye |
| Özet |
| This study aims to develop an effective fraud detection system for financial transactions by proposing a hybrid approach that combines the Local Outlier Factor (LOF) algorithm with the gradient boosting method LightGBM. To address the common challenge of data imbalance in fraud detection, the Synthetic Minority Over-sampling Technique (SMOTE) is applied. The model's performance is rigorously evaluated on the PaySim dataset, focusing on key metrics such as accuracy, precision, recall, and F1-score. The results demonstrate that this hybrid approach provides an efficient and reliable solution for identifying fraudulent activities in simulated banking transaction data. |
| Anahtar Kelimeler |
| Anomaly Detection | Financial Transactions | Fraud Detection | LightGBM | Local Outlier Factor (LOF) | SMOTE |
| Bildiri Türü | Tebliğ/Bildiri |
| Bildiri Alt Türü | Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum) |
| Bildiri Niteliği | Alanında Hakemli Uluslararası Kongre/Sempozyum |
| DOI Numarası | 10.1109/ASYU67174.2025.11208285 |
| Bildiri Dili | İngilizce |
| Kongre Adı | Innovations in Intelligence Systems and Applications (ASYU 2025) |
| Kongre Tarihi | 10-09-2025 / |
| Basıldığı Ülke | Türkiye |
| Basıldığı Şehir | Bursa |
| Atıf Sayıları |
