An updated browser-based demo shows how AI decision logs can be cryptographically verified without relying on trust-based internal records. What matters in AI governance is not whether a system ...

The SEC's 2026 examination priorities emphasize existing regulatory expectations extended to technologies that didn't exist ...

Why 90% of enterprise AI projects fail to scale, and how Turinton is compressing adoption cycles by aligning AI with business ...

Pharma faces regulations and stringent rules for transparency, but the payoff is drugs that can get to market faster and ...

Integrating predictive fraud data is crucial for automating complex workflows, particularly in the claims space,' says COO ...

This article explores the potential of large language models (LLMs) in reliability systems engineering, highlighting their ...

Modern mission requirements dictate analyzing data in place and deploying governed analytics and artificial intelligence (AI) at the edge. This enables officers to obtain a complete, risk-informed ...

Technology will continue to evolve, but trust is what will shape markets, guide regulation, attract investment and determine ...

Explainable AI plays a central role in validating model behavior. Using established explainability techniques, the study examines which financial variables drive fraud predictions. The results show a ...

Traditional rule-based systems, once sufficient for detecting simple patterns of fraud, have been overwhelmed by the scale, ...

With Autopilot Lending, AI can analyze digital behavior, spending patterns, financial intent, and demographic signals to ...

Williams, A. and Louis, L. (2026) Cumulative Link Modeling of Ordinal Outcomes in the National Health Interview Survey Data: Application to Depressive Symptom Severity. Journal of Data Analysis and ...