Fighting Crime Using AI & Machine Learning Fraud Detection uses AI and machine learning algorithms to monitor monetary and non-monetary events and look for patterns that indicate possible risks.

Fraud is no longer merely a nuisance. It’s become a significant, evolving threat that can cost businesses more than money. It ...

Q2 Holdings, Inc. (NYSE: QTWO), a leading provider of digital transformation solutions for financial services, today ...

Despite all of the safeguards and fraud detection systems in place, Capital One failed to monitor or detect the unauthorized activity.

Insurance fraud detection using machine learning can help providers distinguish the fraudulent claims with higher efficiency and as a result, offer reduced premiums for the honest consumers.

Machine learning techniques, such as those using XGBoost algorithms, have been effectively employed to detect and prevent technological fraud by recognizing patterns in large datasets and ...

Combining the innate strengths of unsupervised and supervised machine learning to provide a Transaction Safety Rating is the second step in using machine learning to thwart bot-based fraud.

The global shift toward digital banking has been dramatic, with the volume of cashless transactions increasing year over year. While this growth signals progress in financial technology, it has also ...

Machine learning leading the fight Machine learning is leading the fight against fraud, helping businesses and organisations be more efficient and accurate in their hunt for fraudulent application.

For more information on this research see: Fraud Detection In Healthcare Claims Using Machine Learning: a Systematic Review. Artificial Intelligence In Medicine, 2025;160.