Awesome Fraud Detection Research Papers
This project, titled "Awesome Fraud Detection Research Papers," is an impressive curated collection of research papers dedicated to the emerging field of fraud detection. It compiles an extensive list of scholarly works from a variety of prestigious conferences across multiple disciplines including Network Science, Data Science, Natural Language Processing, Data Mining, Artificial Intelligence, and Databases.
Conferences and Disciplines
The repository aggregates research papers from renowned conferences such as:
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Network Science: ASONAM and Complex Networks focus on the structural properties and dynamics of networks, which are crucial in detecting fraudulent activities in networked systems.
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Data Science: DSAA emphasizes the significance of data-driven methodologies to enhance fraud detection techniques.
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Natural Language Processing (NLP): ACL includes works that leverage textual data for identifying fraudulent patterns.
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Data Mining: Conferences like KDD, ICDM, SIGIR, SDM, WWW, and CIKM provide cutting-edge methods for mining large datasets for fraud detection insights.
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Artificial Intelligence (AI): AAAI, AISTATS, IJCAI, and UAI showcase AI techniques, such as machine learning and deep learning, tailored to combating fraud.
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Databases: VLDB explores database technologies that support the storage and analysis of complex data relationships pertinent to fraud detection.
Highlight of Recent Studies
For 2023, some notable papers include:
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Anti-Money Laundering by Group-Aware Deep Graph Learning (TKDE 2023) explores graph learning techniques tailored for money laundering detection.
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Semi-supervised Credit Card Fraud Detection via Attribute-driven Graph Representation (AAAI 2023) introduces a novel approach that combines semi-supervision with graph representation for enhanced credit card fraud detection.
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BERT4ETH: A Pre-trained Transformer for Ethereum Fraud Detection (WWW 2023) leverages the power of transformers, showing promise in detecting fraudulent transactions within the Ethereum network.
Historical Context and Evolution
Previous years have seen a range of innovative papers exploring various aspects of fraud detection, from graph neural networks (GNNs) for fraud detection in real-time (CIKM 2022) to the use of neural meta-graph search for explainability (CIKM 2022). The collection captures the evolving nature of fraud detection methodologies, highlighting the shift from traditional statistical approaches to more complex AI-driven solutions.
Collaborative and Open-Source Nature
The project encourages open collaboration, inviting contributions from researchers and practitioners around the globe. This open-source initiative signifies a collective effort in advancing the field of fraud detection, making the repository not only a valuable resource for academic research but also a practical toolkit for industry professionals.
Conclusion
"Awesome Fraud Detection Research Papers" stands as an invaluable asset for anyone interested in the multifaceted research landscape of fraud detection. Through its comprehensive collection of high-quality papers, the repository provides deep insights into the challenges of identifying fraudulent activities and the innovative solutions emerging to address these issues. Whether for academic interest or practical application, this collection is essential for staying abreast with the latest trends and techniques in fraud detection.