AIMC Topic: Fraud

Clear Filters Showing 1 to 10 of 40 articles

Multimodal anti fraud education improves cognitive emotional and behavioral engagement in older adults.

Scientific reports
This study examines the differential effectiveness of video-based versus text-based anti-fraud educational interventions in improving cognitive comprehension, emotional engagement, and behavioral intentions among older adults. Using a stratified samp...

Hybrid feature selection framework for enhanced credit card fraud detection using machine learning models.

PloS one
Electronic payment methods are increasingly prevalent worldwide, facilitating both in-person and online transactions. As credit card usage for online payments grows, fraud and payment defaults have also risen, resulting in significant financial losse...

Multifunctional Hydrogen-Bonded Organic Frameworks for Intelligent Anti-Counterfeiting and Food Safety Monitoring.

ACS applied materials & interfaces
In an era of increasing digital threats and product counterfeiting, this study introduces MA-IPA@NPA, a groundbreaking hydrogen-bonded organic framework (HOF) material designed for advanced anticounterfeiting applications. This innovative material sh...

A benchmarking framework and dataset for learning to defer in human-AI decision-making.

Scientific data
Learning to Defer (L2D) algorithms improve human-AI collaboration by deferring decisions to human experts when they are likely to be more accurate than the AI model. These can be crucial in high-stakes tasks like fraud detection, where false negative...

Revisiting low-homophily for graph-based fraud detection.

Neural networks : the official journal of the International Neural Network Society
The openness of Internet stimulates a large number of fraud behaviors which have become a huge threat. Graph-based fraud detectors have attracted extensive interest since the abundant structure information of graph data has proved effective. Conventi...

AI, doping and ethics: On why increasing the effectiveness of detecting doping fraud in sport may be morally wrong.

Journal of medical ethics
In this article, our aim is to show why increasing the effectiveness of detecting doping fraud in sport by the use of artificial intelligence (AI) may be morally wrong. The first argument in favour of this conclusion is that using AI to make a non-id...

A robust and interpretable ensemble machine learning model for predicting healthcare insurance fraud.

Scientific reports
Healthcare insurance fraud imposes a significant financial burden on healthcare systems worldwide, with annual losses reaching billions of dollars. This study aims to improve fraud detection accuracy using machine learning techniques. Our approach co...

Fraud detection in healthcare claims using machine learning: A systematic review.

Artificial intelligence in medicine
OBJECTIVE: Identifying fraud in healthcare programs is crucial, as an estimated 3%-10% of the total healthcare expenditures are lost to fraudulent activities. This study presents a systematic literature review of machine learning techniques applied t...

Local interpretable spammer detection model with multi-head graph channel attention network.

Neural networks : the official journal of the International Neural Network Society
Fraudulent reviews posted by spammers on the online shopping websites mislead consumers' purchasing decisions. To curb fraudulent reviews, many methods have been proposed for detecting spammers. However, the existing spammer detection methods operate...

Recent advances on artificial intelligence-based approaches for food adulteration and fraud detection in the food industry: Challenges and opportunities.

Food chemistry
Food adulteration is the deceitful practice of misleading consumers about food to profit from it. The threat to public health and food quality or nutritional valuable make it a major issue. Food origin and adulteration should be considered to safegua...