Science

New research reveals how interconnected bank leverage can amplify systemic risk and contribute to financial crises.

New research reveals that equipping smaller language models with the ability to self-correct factual errors dramatically boosts their performance in complex financial classification tasks.

A new framework leverages the power of autonomous AI agents to deliver faster, more transparent, and accurate credit risk assessments.

New research reveals that a select group of U.S. banks act as critical conduits for systemic risk, amplifying the impact of economic and policy changes.

A new machine learning approach leveraging routine health data can forecast the development of liver cirrhosis up to three years in advance, offering opportunities for early intervention.

Researchers are leveraging artificial intelligence to better understand and profile the severity of Parkinson’s Disease using a combination of diverse patient data.

A new deep learning approach effectively combines satellite radar and optical imagery to accurately map flood extent, even with significant gaps in visual data.

A novel application of quantized tensor trains offers a scalable solution for accurately pricing options on multiple underlying assets, a longstanding challenge in computational finance.

A new stochastic model captures the erratic nature of trust, moving beyond traditional approaches to account for unpredictable jumps in decision-making.

Researchers have introduced a comprehensive dataset and evaluation framework to better assess the ability of artificial intelligence to understand and interpret financial credit information from both visual and textual sources.