Science

As autonomous AI systems multiply, organizations are grappling with how to understand, govern, and mitigate the risks of increasingly complex agent networks.

New research highlights the critical need to validate explainable AI techniques across different model architectures before deploying them in high-stakes financial environments.

A new AI framework uses predictive modeling to shield borrowers from liquidation in decentralized lending platforms.

A new approach using graph neural networks and temporal analysis offers regulators a powerful tool for monitoring systemic risk and proactively identifying vulnerable institutions.

Researchers are leveraging the power of large language models to identify misleading information in the financial world, achieving top results in a recent challenge.

New research reveals that acoustic features in corporate earnings calls are surprisingly unreliable for gauging financial health, and can even hinder predictive accuracy.

A new deep learning framework improves the accuracy of building load forecasting by intelligently fusing historical data and adapting to fluctuating energy usage.

A novel deep learning model leverages the power of CNNs, LSTMs, and attention to accurately forecast when industrial equipment will fail, prioritizing safety and minimizing downtime.

Reusable skills for AI agents are streamlining automation, but public skill registries are creating new security vulnerabilities.

New research reveals that the geometry of global production networks is creating a permanently fragile system increasingly susceptible to cascading failures.