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Fraud-R1 : A Multi-Round Benchmark for Assessing the Robustness of LLM Against Augmented Fraud and Phishing Inducements

Annual Meeting of the Association for Computational Linguistics
We introduce Fraud-R1, a benchmark designed to evaluate LLMs' ability to defend against internet fraud and phishing in dynamic, real-world scenarios. Fraud-R1 comprises 8,564 fraud cases sourced from phishing scams, fake job postings, social media, and ...
Shu Yang   +9 more
semanticscholar   +1 more source

A Taxonomy of Frauds and Fraud Detection Techniques

2009
Fraud is growing noticeably with the expansion of modern technology and the universal superhighways of communication, resulting in the loss of billions of dollars worldwide each year. Several recent techniques in detecting fraud are constantly evolved and applied to many commerce areas.
Naeimeh Laleh, Mohammad Abdollahi Azgomi
openaire   +1 more source

Internet Financial Fraud Detection Based on Graph Learning

IEEE Transactions on Computational Social Systems, 2023
The rapid development of information technology such as the Internet of Things, Big Data, artificial intelligence, and blockchain has changed the transaction mode of the financial industry and greatly improved the convenience of financial transactions ...
Ranran Li   +4 more
semanticscholar   +1 more source

Fraud Is Bad, Studying Fraud Is Hard

Controlled Clinical Trials, 2000
Recently reported in Science: a German hematologist with a bibliography of 347 papers has been shown to have included fraudulent data in 52 of these papers, with another 42 raising strong suspicions of fraud [1]. It appears this gentleman will be joining the list of notorious researchers who, motivated presumably by unrestrained ambition, falsified and
openaire   +2 more sources

Revisiting Graph-based Fraud Detection in Sight of Heterophily and Spectrum

AAAI Conference on Artificial Intelligence, 2023
Graph-based fraud detection (GFD) can be regarded as a challenging semi-supervised node binary classification task. In recent years, Graph Neural Networks (GNN) have been widely applied to GFD, characterizing the anomalous possibility of a node by ...
Fan Xu   +5 more
semanticscholar   +1 more source

Responding to Fraud

Science, 2006
![Figure][1] Our journal—as well as science with a small “s”—went through a disappointing and troubling experience with the two stem cell papers from the South Korean research group led by Dr. Woo Suk Hwang.
openaire   +2 more sources

Optimizing fraud detection in financial transactions with machine learning and imbalance mitigation

Expert Syst. J. Knowl. Eng.
The rapid advancement of the Internet and digital payments has transformed the landscape of financial transactions, leading to both technological progress and an alarming rise in cybercrime.
E. AL-Dahasi   +3 more
semanticscholar   +1 more source

Fraud in the NHS

British Journal of Nursing, 2019
Richard Griffith, Senior Lecturer in Health Law at Swansea University, outlines what constitutes fraud and the measures taken by the NHS to counter fraudulent ...
openaire   +3 more sources

Banking and fraud

Computer Law & Security Review, 2017
Abstract The authors wrote a memorandum to the UK Treasury Committee, House of Commons in January 2011 on the topic of banking and fraud. The methods used by thieves to steal from the customers of banks have increased, and in September 2016, the UK consumer magazine Which?
Stephen Mason, Nicholas Bohm
openaire   +1 more source

Research Fraud

British Journal of Perioperative Nursing (United Kingdom), 2002
Readers will be well aware of the importance attached to research or other convincing evidence to underpin nursing practice. Much excellent research has been and is still being done by nurses, with the intention of improving practice and establishing a knowledge base for nursing. The medical profession holds its powerful position primarily by virtue of
Stephen Timmons, Marilyn Williams
openaire   +2 more sources

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