// the find
benedekrozemberczki/awesome-fraud-detection-papers
A curated list of data mining papers about fraud detection.
A bibliography of academic fraud detection papers from major ML/DM conferences (KDD, WWW, AAAI, IJCAI, etc.), spanning 2015–2025. It's a reading list, not a library — no datasets, no implementations, no benchmarks. The target audience is researchers who want a starting point for the literature, not engineers trying to build something.
Coverage of graph-based fraud detection is genuinely good — the GNN-heavy 2020–2025 section reflects where the field actually moved. Conference sourcing is credible (KDD, WWW, ICDM are the right venues). Active maintenance through 2025 with AAAI/IJCAI/CIKM entries is a real differentiator versus similar lists that stalled in 2021. The breadth across fraud domains — credit card, insurance, e-commerce, customs, reviews — makes it useful beyond any single vertical.
No code links for the majority of papers; the ones that do link code often point to the same `safe-graph/DGFraud` repo, which covers maybe 15% of entries. Zero curation signal — every paper gets identical formatting regardless of whether it was influential or never cited. No datasets section, so a practitioner who wants to reproduce anything still has to hunt separately. The `awesome.py` file in the tree suggests some tooling exists but there's no documentation explaining what it does or why.