Some studies containing instructions in white text or small font — visible only to machines — will be withdrawn from preprint ...

Detecting writing via artificial intelligence is a tricky dance: Doing it right means being effective at identifying it while ...

How AI is transforming fraud detection AI-powered fraud detection systems leverage machine learning, big data analytics, and real-time processing to detect and prevent fraudulent activities. Unlike ...

This article explores the transformative potential of machine learning algorithms in combating supply chain fraud, focusing on techniques such as supervised and unsupervised learning, anomaly ...

This study explores the application of machine learning in financial anomaly detection. Using comparative analysis, the research contrasts traditional statistical methods with various types of machine ...

Artificial intelligence (AI) and machine learning have created or improved upon various fraud detection algorithms, constituting a step up from traditional rules-based approaches that were more time ...

Credit card companies must be able to identify fraudulent credit card transactions so that clients are not charged for items they did not purchase. Previously, many machine learning approaches and ...

Point-of-Sale (POS) fraud: ML analyzes data segments to identify anomalies related to employee theft. Email phishing: ML-based malware scanners can detect and block malicious emails, safeguarding ...

Bespoke fraud ML models are powered by algorithms that learn from historical data, picking up on behaviors and characteristics commonly associated with fraud.