AIMC Topic: Fraud

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Using an Optimized Learning Vector Quantization- (LVQ-) Based Neural Network in Accounting Fraud Recognition.

Computational intelligence and neuroscience
With the continuous development and wide application of artificial intelligence technology, artificial neural network technology has begun to be used in the field of fraud identification. Among them, learning vector quantization (LVQ) neural network ...

A field trials-based authentication study of conventionally and organically grown Chinese yams using light stable isotopes and multi-elemental analysis combined with machine learning algorithms.

Food chemistry
In this study, stable isotopes and multi-element signatures combined with chemometrics were used to distinguish conventional and organic Chinese yams based on field trials. Four light stable isotopes δD, δC, δN, δO, and 20 elements (e.g. Li, Na, Mn) ...

Medical Fraud and Abuse Detection System Based on Machine Learning.

International journal of environmental research and public health
It is estimated that approximately 10% of healthcare system expenditures are wasted due to medical fraud and abuse. In the medical area, the combination of thousands of drugs and diseases make the supervision of health care more difficult. To quantif...

Exploration of total synchronous fluorescence spectroscopy combined with pre-trained convolutional neural network in the identification and quantification of vegetable oil.

Food chemistry
In order to distinguish different vegetable oils, adulterated vegetable oils, and to identify and quantify counterfeit vegetable oils, a method based on a small sample size of total synchronous fluorescence (TSyF) spectra combined with convolutional ...

Using Harmony Search Algorithm in Neural Networks to Improve Fraud Detection in Banking System.

Computational intelligence and neuroscience
Financial fraud is among the main problems undermining the confidence of customers in addition to incurring economic losses to banks and financial institutions. In recent years, along with the proliferation of fraud, financial institutions began look...

Artificial Intelligence Crime: An Interdisciplinary Analysis of Foreseeable Threats and Solutions.

Science and engineering ethics
Artificial intelligence (AI) research and regulation seek to balance the benefits of innovation against any potential harms and disruption. However, one unintended consequence of the recent surge in AI research is the potential re-orientation of AI t...

Generative adversarial network based telecom fraud detection at the receiving bank.

Neural networks : the official journal of the International Neural Network Society
Recently telecom fraud has become a serious problem especially in developing countries such as China. At present, it can be very difficult to coordinate different agencies to prevent fraud completely. In this paper we study how to detect large transf...

GDFGAT: Graph attention network based on feature difference weight assignment for telecom fraud detection.

PloS one
In recent years, the number of telecom frauds has increased significantly, causing substantial losses to people's daily lives. With technological advancements, telecom fraud methods have also become more sophisticated, making fraudsters harder to det...

Innovative novel regularized memory graph attention capsule network for financial fraud detection.

PloS one
Financial fraud detection (FFD) is crucial for ensuring the safety and efficiency of financial transactions. This article presents the Regularised Memory Graph Attention Capsule Network (RMGACNet), an original architecture aiming at improving fraud d...