If a transaction is initiated using a device or behaviour ... While risk management and fraud detection are critical, data science extends far beyond these areas, creating new opportunities ...

His latest research, featured in the International Journal of Enhanced Research in Science, Technology & Engineering, is titled "Investigating Fraud Detection in Insurance Claims using Data Science." ...

The implemented system reduced fraud liability for issuing banks significantly while decreasing false positive alerts. This ...

The platform, which was developed in conjunction with data-science company Feedzai ... “One of the many advantages of using machine-learning models for fraud detection is the amount of data that a ...

An influential paper on amyloid protein and Alzheimer’s disease potentially fabricated data. Why did it take 16 years ... particular reveals the rippling consequences of fraud. It was, according to ...

Fraud detection requires leveraging new tools and models ... Criminals commit financial fraud in a variety of novel ways, such as hacking data from the dark web, using generative AI to create more ...

Advancements in fraud detection using payment enrichment data have significantly strengthened the ability of businesses to combat fraudulent activities. By harnessing the power of payment ...

Unsupervised Learning: AI identifies fraud patterns without labeled data, using anomaly detection techniques like autoencoders and clustering algorithms. • Deep Learning: Recurrent Neural ...

March 26, 2025 /PRNewswire/ -- A new report from Conning, 2025 Workers' Comp Study: Using Data & AI ... integration of AI and advanced data analytics into fraud detection processes is a game ...