Awesome Fraud Detection Research Papers
A curated collection of academic papers on data mining and machine learning techniques for fraud detection across various domains.
What is Awesome Fraud Detection Research Papers?
Awesome Fraud Detection Papers is a curated list of academic research papers on data mining and machine learning techniques for detecting fraud. It compiles publications from major conferences like KDD, AAAI, and WWW, covering domains such as financial transactions, insurance claims, online reviews, and social networks. The repository helps researchers and engineers stay updated on advanced methods like graph neural networks, anomaly detection, and federated learning applied to fraud prevention.
Data scientists, machine learning researchers, and engineers working on fraud detection systems in industries like finance, e-commerce, cybersecurity, and insurance. It is also valuable for academics and students studying anomaly detection, graph mining, or applied AI.
It saves significant time in literature review by providing a centralized, structured, and up-to-date collection of papers with direct links. Unlike generic academic search engines, it is domain-focused, includes code implementations when available, and connects to related curated lists for broader learning.
Overview
A curated list of data mining papers about fraud detection.
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