New research reveals that artificial intelligence consistently outperforms people in identifying fraudulent claims and resists being swayed by biased investor pressure.
New research uses computational modeling to reveal how flawed governance structures can drive economic systems toward predictable breakdowns, while decentralized designs foster lasting growth.
A novel framework adapts public health surveillance methods to move beyond simple incident counts and understand the underlying dynamics of AI-related risks.
![The simulation, parameterized with [latex]\theta = 0.3[/latex], demonstrates that a shock to a specific Brazilian asset ([latex]VIVT3.SA[/latex]) triggers a localized cascade of defaults within the Brazilian financial subnetwork, while developed-market assets remain insulated from contagion, underscoring the inherent vulnerabilities of emerging economies.](https://arxiv.org/html/2604.19796v1/figure_7.png)
New research reveals that the way global equity markets are connected, combined with the potential for extreme losses, is a primary driver of systemic risk, rather than a broad, uniform threat.
New research reveals how machine learning systems used in higher education can inadvertently reinforce existing inequities throughout their entire lifecycle.
A new analysis of Bitcoin transaction data reveals that complex graph neural networks don’t always outperform simpler models in detecting fraudulent activity.
![Financial markets, when subjected to external shocks-such as the U.S. tariff announcement of April 2025-exhibit a predictable three-phase response characterized by an initial complexity gap [latex]\Delta(t) > 0[/latex], an immediate convergence towards [latex]\Delta(t) \approx 0[/latex] following the event, and a subsequent recovery pattern involving gap re-widening, secondary convergence, and eventual structural readjustment, as measured by the evolution of normalized largest eigenvalue [latex]\lambda_{\max}^{\text{norm}}(t)[/latex] and average correlation [latex]\rho(t)[/latex].](https://arxiv.org/html/2604.19107v1/Plots_for_paper_All/US_Complexity_Analysis_RawCorr.png)
New research reveals a predictable pattern in how major stock markets respond to unexpected shocks, offering insights into systemic risk and portfolio management.
New research demonstrates how connecting large language models to real-time financial data and quantitative tools dramatically improves accuracy and reliability in answering complex questions.
A new approach integrates anomaly detection with agentic AI systems to move beyond reactive fall response and enable continuous, adaptive risk management in human activity.
New research explores how artificial intelligence, specifically large language models, can introduce unpredictable and sometimes destabilizing behaviors into financial markets.