The United States Department of Justice (DOJ) recently announced the Fraud Oversight through Careful Use of Statistics (FOCUS) initiative – a ...

The financial industry in 2026 is defined by a tension between rapid AI adoption and regulatory caution. With 81% of financial firms now using AI and regulators like the Federal Reserve calling for ...

The financial landscape of 2026 is defined by a paradox: machine learning systems are now more powerful and autonomous than ever, yet they operate under the strictest regulatory scrutiny in history.

As manual verification methods continue to show their limitations in modern risk environments, TrustLayer is expanding its AI ...

Proactive monitoring is no longer optional for leaders—it’s essential to safeguarding a vulnerable and growing population.

Digital identity verification has entered a new phase. What used to rely on static rules, document uploads, and manual review ...

Ford engineers are studying whether AI can play a role in detecting faulty run-downs. To do that, they first had to determine ...

Machine learning often feels difficult at the beginning, especially when everything stays theoretical. That changes once you start working on real projects and see how models are actually used.

Abstract: Auto insurance claim fraud has become increasingly prevalent in recent years posing a significant challenge for the auto insurance industry. To address this issue there is a growing need for ...

Abstract: Credit card fraud detection presents a significant challenge due to the extreme class imbalance in transaction datasets. Traditional machine learning models struggle to achieve high recall ...

Every so often, we receive an email that stops us cold. Not because it is dramatic. Not because it is careless. Because it feels impossible. Sheri M. from Georgia recently wrote to us with this ...