34 stories tagged Finance · page 2 of 2
Google is launching its AI-powered Finance tool across Europe. This update brings local language support and powerful features to help users manage their money. The tool is designed to make financial planning more accessible and intuitive for everyday people.
Researchers have developed a specialized AI system for fraud detection and anti-money laundering. It's designed to handle complex financial data more efficiently than generic chat systems. This could make financial transactions safer for everyone.
Large language models (LLMs) often fail to adjust their responses based on how certain the information they retrieve is. This could have serious implications in fields like medicine and finance where accuracy is critical.
Researchers developed FinAgent-RAG, an AI tool that helps answer complex questions about financial documents. It can handle tables, text, and footnotes to provide accurate insights.
OpenAI and PwC are collaborating to help companies use AI agents to automate finance tasks, improve forecasting, and modernize the CFO role. This partnership aims to make financial operations more efficient and data-driven for businesses.
Kepler used Claude to create AI systems that can explain their decisions, which is crucial for financial services. This helps build trust in AI-driven financial tools. In plain English, it means AI can now show its work, making it more reliable for everyday banking and investments.
A new study found that better stop-loss and take-profit settings can significantly improve the performance of autonomous crypto trading bots. The research highlights the importance of systematic testing for exit strategies, not just entry points.
Researchers deployed 3,505 AI agents to trade real ETH, processing 7.5M inferences and $20M in volume. The study highlights the reliability of language-model agents in real-world financial markets.
Researchers introduced Deep FinResearch Bench to assess AI's financial research capabilities. The benchmark evaluates qualitative rigor, forecasting accuracy, and claim verifiability in investment reports.
Researchers introduce ReSS, a hybrid framework that merges symbolic and neural models to improve tabular data prediction. The approach aims to enhance both accuracy and human-understandable reasoning in high-stakes domains like healthcare and finance.