Rudolf: Interactive Rule Refinement System for Fraud Detection
Summary: RUDOLF is an interactive rule-refinement system for fraud detection, enabling experts to adapt rules as fraud and legitimate patterns evolve. It proposes best-rule adaptations to cover frauds and exclude legitimate activity, demonstrated on credit-card and network data. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Tova Milo
- 2. Slava Novgorodov
- 3. Wang-Chiew Tan
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,766 | ICARUS: Minimizing Human Effort in Iterative Data Completion | 2018 | VLDB | 4.6564959e-05 |
| 11,266 | MINT: Detecting Fraudulent Behaviors from Time-series Relational Data | 2023 | VLDB | 4.1945683e-05 |
| 11,293 | Fanglue: An Interactive System for Decision Rule Crafting | 2023 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 487 | Why Not? | 2009 | SIGMOD | 0.00022050218 |
| 3,197 | A Probabilistic Optimization Framework for the Empty-Answer Problem | 2013 | VLDB | 7.3955829e-05 |
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| 12,562 | Using Association Rules for Fraud Detection in Web Advertising Networks | 2005 | VLDB | 4.1945683e-05 |
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| 3,600 | xFraud: Explainable Fraud Transaction Detection | 2022 | VLDB | 6.9315684e-05 |
| 9,055 | GOLDRUSH: Rule Sharing System for Fraud Detection | 2018 | VLDB | 4.4039656e-05 |