Results 51 to 60 of about 132,324 (298)
Australian Journal of Social Issues, EarlyView.ABSTRACT Australia's Robodebt scheme, an automated debt recovery program introduced in 2016, was exposed by the Robodebt Royal Commission (RC) as a serious failure of public administration and source of significant harm for thousands of Australians. Through a critical discourse analysis (CDA) of Australian news media, this study explores whether the RC'
Rebecca Coleman‐Hicks +1 more
wiley +1 more source
Complex Systems Informatics and Modeling Quarterly, 2019 Financial industries are undergoing a digital transformation of their products, services, overall business models. Part of this digitalization in banking aims at automating most of the manual work in payment handling and integrating the workflows of ...
Alexander Diadiushkin +2 more
doaj +1 more source
, 2017 Most past work on social network link fraud detection tries to separate genuine users from fraudsters, implicitly assuming that there is only one type of fraudulent behavior. But is this assumption true?
Beutel, Alex +3 more
core +1 more source
Statistical Fraud Detection: A Review
Statistical Science, 2002 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bolton, Richard J., Hand, David J.
openaire +3 more sources
Opposing consensus science through scholarly practices: The role of claims maintenance
Journal of the Association for Information Science and Technology, EarlyView.Abstract This study examines how three US‐based communities who oppose consensus science produce and disseminate scholarly‐like artifacts: pro‐life activists, Young Earth Creationists, and Anthropogenic Climate Crisis skeptics. Prior research shows that industry‐ or church‐backed advocacy campaigns often generate claims supported by these communities ...
Irene V. Pasquetto +3 more
wiley +1 more source
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENTDetection of financial fraud is now a cause of major concern in the financial and banking industry because fraud techniques are becoming highly sophisticated. Classical rule- based systems are generally ineffective in detecting complex patterns of fraud, which call for more complex machine learning and artificial intelligence processes.
Sai Chandana Y +3 more
openaire +2 more sources
Multi-view graph neural network for fraud detection algorithm
Tongxin xuebao, 2022 Aiming at the problem that in the field of fraud detection, imbalance labels and lack of necessary connections between fraud nodes, resulting in fraud detection tasks not conforming to the hypothesis of homogeneity of graph neural networks, multi-view ...
Zhuo CHEN, Miao ZHU, Junwei DU
doaj +2 more sources
British Journal of Clinical Pharmacology, EarlyView.Abstract Health care is shifting towards a digital‐guided system, integrating digital diagnostics, biomarkers and therapeutics in many care pathways. However, despite rapid technological advancement and preliminary adoption accelerated by the COVID‐19 pandemic, a significant implementation gap persists. This narrative review explores the causes of this
Mees H. P. Stoop +3 more
wiley +1 more source
A Review of Artificial Intelligence for Financial Fraud Detection
Applied SciencesFinancial fraud has expanded rapidly with the growth of the digital economy, evolving from conventional transactional misconduct to more complex and data-intensive forms. Traditional rule-based detection methods are increasingly inadequate for addressing
Haiquan Yang +2 more
doaj +1 more source
A survey of outlier detection methodologies [PDF]
, 2004 Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply ...
Austin, J., Hodge, V.J.
core +5 more sources