Science

A new review examines how critical sectors are struggling to balance the promise of artificial intelligence with growing demands for responsible governance and compliance.

A new analysis dissects whether the current surge in artificial intelligence investment represents a sustainable buildout or a precarious financial bubble.

New research reveals that the way financial assets influence each other isn’t fixed, but dynamically changes during periods of market turbulence, amplifying systemic risk.

A new framework leverages neural networks to model evolving time series data by directly parameterizing the underlying Markov transition probabilities.

As artificial intelligence reshapes financial markets, understanding and mitigating its inherent vulnerabilities is paramount for maintaining stability and trust.

New research introduces a system for rigorously testing whether large language models comply with complex financial regulations during user interactions.

New research demonstrates that advanced machine learning models can accurately predict small and medium-sized enterprise defaults, offering insights into underlying economic factors.

A new model reveals how interactions between diverse investor strategies and limited capital can trigger instability and contagion across multiple asset classes.

New research reveals that careful data preparation and aligning model assumptions are more critical for accurate U.S. bond market predictions than employing sophisticated deep learning architectures.

Researchers have developed a new AI framework capable of generating highly realistic synthetic financial time-series data, offering a powerful tool for testing and improving trading strategies.