# benedekrozemberczki/awesome-fraud-detection-papers

A curated list of data mining papers about fraud detection.

Repository: https://github.com/benedekrozemberczki/awesome-fraud-detection-papers
Canonical: https://ross.abutalabs.com/products/awesome-fraud-detection-papers
Language: Python
License: CC0-1.0
License Family: permissive
Topics: fraud-detection, fraud-prevention, fraud-checker, fraud-management, fraud-explorer, classification, data-science, churn, credit-scoring, deep-learning, random-forest, gradient-boosting, data-mining, link-prediction, graph-classification, classifier, logistic-regression, credit-card-fraud, credit-card-validation, credit-card-fraud-detection
Last push: 2026-01-05T12:28:59+00:00

## Health v2 (maintenance only)
Score: 58/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 60, release rhythm 32, longevity 100
- inputs: {"age_days": 2655, "days_push": 240, "days_rel": 240, "gap_med": null, "n_releases_24m": 1}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1827, forks 329 (observed 2026-08-28T04:05:41.555796+00:00)

## What it is
A curated awesome-list of academic research papers on fraud detection, spanning data mining, graph learning, and machine learning venues like KDD, AAAI, and WWW. It organizes papers by year and conference with links to publications.

## Use cases
- find research papers on fraud detection
- survey graph-based fraud detection methods
- learn about credit card fraud detection models
- keep up with latest fraud detection research
- find papers on anomaly detection in transactions
- research fraud detection for a fintech project

## When to choose
- you need a curated reading list of fraud detection literature
- you are a researcher or student surveying the field
- you want paper links organized by venue and year

## When to avoid
- you need production-ready fraud detection software
- you want runnable code rather than papers
- you need tutorials or beginner learning material

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, data-science, nlp
- domain: machine-learning, data-science, fintech, awesome-lists
- platform: cross-platform
- tags: awesome-list, fraud-detection, research-papers, curated-list, graph-neural-networks, anomaly-detection, credit-card-fraud

## Member repositories
- benedekrozemberczki/awesome-fraud-detection-papers (main) score 58

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:41.555796+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:19:25.224965+00:00, confidence not recorded.
  - readme: https://github.com/benedekrozemberczki/awesome-fraud-detection-papers (fetched 2026-08-28T04:05:41.555796+00:00, sha 6680d1438c62)
- Data as of 2026-08-30T08:39:29.467469+00:00.