Results 31 to 40 of about 22,315 (263)
AI Driven Fraud Detection Models in Financial Networks: A Comprehensive Systematic Review
IEEE AccessRapid advancements in digital innovation and globalization has significantly increased the complexity of financial networks, making them more vulnerable to fraud.
Nusrat Jahan Sarna +6 more
doaj +1 more source
From Traditional Methods to Machine Learning: The Evolution of Fraud Detection in Auditing with New Technologies [PDF]
پژوهشهای تجربی حسابداریFraudulent financial reporting is a global issue with significant negative consequences for all stakeholders. However, detecting fraud remains challenging due to its subjective nature.
Zahra Taheri +2 more
doaj +1 more source
Agribusiness, EarlyView.ABSTRACT Using survey and discrete choice experiment data, we examined US specialty crop growers' preferences for marketing contract attributes in the context of emerging blockchain‐based technologies and expanding traceability initiatives. Results show that farmers preferred traditional written contracts but might be willing to accept digital ...
Elizabeth Canales +3 more
wiley +1 more source
GraphRAG for engineering diagrams: ChatP&ID enables LLM interaction with P&IDs
AIChE Journal, EarlyView.Abstract Piping and Instrumentation Diagrams (P&IDs) are central to process engineering workflows, yet extracting information from them remains a tedious and time‐consuming task. This work introduces ChatP&ID, a framework enabling natural‐language interaction with smart P&IDs through Graph Retrieval‐Augmented Generation (GraphRAG), to our knowledge ...
Achmad Anggawirya Alimin +1 more
wiley +1 more source
The Evaluation of Distributed Topic Modeling Paradigms for Detection Of Fraudulent Insurance Claims In Healthcare Forum [PDF]
MATEC Web of ConferencesHealthcare fraud is the deliberate misrepresentation of the healthcare industry for the purpose of obtaining unjustified financial gain. There are many different types of healthcare fraud, which can influence patients, healthcare professionals, insurers,
Subbarayudu Yerragudipadu +5 more
doaj +1 more source
Engineering Proceedings, 2023 The proliferation of internet services in various industries, especially the financial sector, has increased financial fraud. Fraud detection and prevention are critical to protecting both individuals and organizations from significant financial loss ...
Vikas R. Shetty +2 more
doaj +1 more source
Enhancing credit card fraud detection with a stacking-based hybrid machine learning approach. [PDF]
PeerJ Comput SciThe swift progression of technology has increased the complexity of cyber fraud, posing an escalating challenge for the banking sector to reliably and efficiently identify fraudulent credit card transactions.
Marazqah Btoush EAL +4 more
europepmc +2 more sources
Advanced Intelligent Systems, EarlyView.An instance‐level, model‐agnostic explanation of class differentiation is introduced through SHAP‐LCD, linking probability shifts to feature‐wise Shapley contributions. The method operates on tabular and image data and is released in a fully reproducible implementation, offering a transparent way to examine, at each instance, why predictive models ...
Roxana M. Romero Luna +2 more
wiley +1 more source
From Conventional Methods to Contemporary Neural Network Approaches: Financial Fraud Detection [PDF]
, 2021 This chapter provides insights on the underlying reasons to replace the conventional methods with contemporary approaches—the neural network-based machine learning methods—in financial fraud detection.
Zengin-Karaibrahimoglu, Y. +2 more
core +1 more source
Advanced Intelligent Systems, EarlyView.Visual features, numerical descriptors, and controlled textual attributes extracted from smartphone images of Chenpi are integrated by VALIANT, a tailored multimodal framework for simultaneous storage‐age classification and authenticity verification. The workflow distinguishes genuine products from suspicious standard operating procedure mimics while ...
Simon C. K. Chan +5 more
wiley +1 more source