RiskLabs: Predicting Financial Risk Using Large Language Model Based on Multi-Sources Data

arXiv:2404.07452v1 Announce Type: cross
Abstract: The integration of Artificial Intelligence (AI) techniques, particularly large language models (LLMs), in finance has garnered increasing academic attention. Despite progress, existing studies predominantly focus on tasks like financial text summarization, question-answering (Q$\&$A), and stock movement prediction (binary classification), with a notable gap in the application of LLMs for financial risk prediction. Addressing this gap, in this paper, we introduce \textbf{RiskLabs}, a novel framework that leverages LLMs to analyze and predict financial risks. RiskLabs uniquely …

abstract academic artificial artificial intelligence arxiv attention binary classification cs.ai cs.ce cs.lg data finance financial focus gap integration intelligence language language model language models large language large language model large language models llms prediction progress q-fin.pm q-fin.rm question risk stock studies summarization tasks text text summarization type