AIMC Topic: Fraud

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BalancerGNN: Balancer Graph Neural Networks for imbalanced datasets: A case study on fraud detection.

Neural networks : the official journal of the International Neural Network Society
Fraud detection for imbalanced datasets is challenging due to machine learning models inclination to learn the majority class. Imbalance in fraud detection datasets affects how graphs are built, an important step in many Graph Neural Networks (GNNs)....

A new fusion neural network model and credit card fraud identification.

PloS one
Credit card fraud identification is an important issue in risk prevention and control for banks and financial institutions. In order to establish an efficient credit card fraud identification model, this article studied the relevant factors that affe...

Collaborative artificial intelligence system for investigation of healthcare claims compliance.

Scientific reports
Healthcare fraud, waste and abuse are costly problems that have huge impact on society. Traditional approaches to identify non-compliant claims rely on auditing strategies requiring trained professionals, or on machine learning methods requiring labe...

Detection of Ponzi scheme on Ethereum using machine learning algorithms.

Scientific reports
Security threats posed by Ponzi schemes present a considerably higher risk compared to many other online crimes. These fraudulent online businesses, including Ponzi schemes, have witnessed rapid growth and emerged as major threats in societies like N...

Procedure code overutilization detection from healthcare claims using unsupervised deep learning methods.

BMC medical informatics and decision making
BACKGROUND: Fraud, Waste, and Abuse (FWA) in medical claims have a negative impact on the quality and cost of healthcare. A major component of FWA in claims is procedure code overutilization, where one or more prescribed procedures may not be relevan...

Applications of Bayesian Neural Networks in Outlier Detection.

Big data
Anomaly detection is crucial in a variety of domains, such as fraud detection, disease diagnosis, and equipment defect detection. With the development of deep learning, anomaly detection with Bayesian neural networks (BNNs) becomes a novel research t...

New Software Interface for Registering Rapid Antigen Test Results to Prevent Fraud.

Disaster medicine and public health preparedness
Donald O. Besong has already documented that the online registration of unsupervised lateral flow test results poses concerns in the case of a serious pandemic where there are not enough medics to read scans or watch videos of candidates' results (Be...

Monitoring and Analysis of Venture Capital and Corporate Fraud Based on Deep Learning.

Computational intelligence and neuroscience
With the continuous expansion of global investment institutions, the development of the investment industry is gradually accelerating, but the risks behind the investment are also constantly increasing. Using the data of A-share companies in China's ...

E-Commerce Fraud Detection Model by Computer Artificial Intelligence Data Mining.

Computational intelligence and neuroscience
This study aims to identify e-commerce fraud, solve the financial risks of e-commerce enterprises through big data mining (BDM), further explore more effective solutions through Information fusion technology (IFT), and create an e-commerce fraud dete...

Feature engineering solution with structured query language analytic functions in detecting electricity frauds using machine learning.

Scientific reports
Detecting fraud related to electricity consumption is usually a difficult challenge as the input datasets are sometimes unreliable due to missing and inconsistent records, faults, misinterpretation of meter reading remarks, status, etc. In this paper...