Anomaly Detection on Fraud
0.925AUC-PRiForest++
Evaluation results
| Method | Links | ||
|---|---|---|---|
| iForest++2026.02 | 0.925 | — | |
| VAE++ESDD2026.02 | 0.922 | — | |
| LOF++2026.02 | 0.913 | — | |
| ARCUS2026.02 | 0.907 | — | |
| METER2026.02 | 0.905 | — | |
| CPOCEDS2026.02 | 0.904 | — | |
| VAE++ES2026.02 | 0.901 | — | |
| StrAEm++DD2026.02 | 0.899 | — | |
| Baseline2026.02 | 0.895 | — | |
| Memstream2026.02 | 0.891 | — | |
| DisentAD2026.03 | 0.6142 | — | |
| MCM2026.03 | 0.5306 | — | |
| eDSVDDType=Deep Ensemble-based2022.06 | 0.48 | 0.948 | |
| SEAD2026.02 | 0.476 | — | |
| LeSiNN2022.06 | 0.401 | 0.952 | |
| LUNAR2026.03 | 0.3981 | — | |
| DIFType=Deep Ensemble-based2022.06 | 0.387 | 0.953 | |
| DIF2022.06 | 0.387 | 0.953 | |
| OFA-TAD2026.03 | 0.387 | — | |
| EIF2022.06 | 0.378 | 0.95 | |
| eRECONType=Deep Ensemble-based2022.06 | 0.372 | 0.946 | |
| eREPENType=Deep Ensemble-based2022.06 | 0.345 | 0.954 | |
| eRDPType=Deep Ensemble-based2022.06 | 0.329 | 0.947 | |
| AE2026.03 | 0.2777 | — | |
| KNN2026.03 | 0.2535 | — | |
| iForest2026.03 | 0.2373 | — | |
| DRL2026.03 | 0.2309 | — | |
| PID2022.06 | 0.186 | 0.95 | |
| DSVDD2026.03 | 0.1819 | — | |
| IF2022.06 | 0.155 | 0.95 | |
| LOF2026.03 | 0.0027 | — | |
| ACRMethod Category=Universal2025.12 | — | 0.6575 | |
| DAGMMMethod Category=Task-Specific2025.12 | — | 0.8147 | |
| DBN=10,000, d=302026.05 | — | 0.494 | |
| DSN=10,000, d=302026.05 | — | 0.551 | |
| G-ShapleyN=10,000, d=302026.05 | — | 0.508 | |
| GOADMethod Category=Task-Specific2025.12 | — | 0.7809 | |
| ICAD-LLMMethod Category=Task-Specific2025.12 | — | 0.9264 | |
| ICAD-LLMMethod Category=Universal2025.12 | — | 0.8526 | |
| IForestMethod Category=Task-Specific2025.12 | — | 0.7363 | |
| KNNN=10,000, d=302026.05 | — | 0.826 | |
| LSH-ShapleyN=10,000, d=302026.05 | — | 0.795 | |
| MCMMethod Category=Task-Specific2025.12 | — | 0.9323 | |
| NeuTraL ADMethod Category=Universal2025.12 | — | 0.6707 | |
| UniADMethod Category=Universal2025.12 | — | 0.7438 |