The paper introduces RiskLabs, a novel framework that leverages large language models (LLMs) to predict financial risks. RiskLabs integrates various types of financial data, including textual and vocal information from earnings conference calls (ECCs), market-related time series data, and contextual news data surrounding ECC release dates. The framework employs a multi-stage process: extracting and analyzing ECC data using LLMs, gathering and processing time-series data, and using multimodal fusion techniques to amalgamate these data features for comprehensive financial risk prediction. Empirical results demonstrate the effectiveness of RiskLabs in forecasting both volatility and variance in financial markets. The paper also discusses the contributions of LLMs in financial risk assessment and addresses challenges such as hallucination and the dynamic nature of financial markets. The framework's structure and functionality are detailed, including the Earnings Conference Call Encoder, Time-Series Encoder, Relevant News Encoder, and Multi-Task Prediction Block. The paper concludes with a comprehensive analysis of the role of LLMs in financial risk prediction and proposes practical solutions to enhance the framework's performance.The paper introduces RiskLabs, a novel framework that leverages large language models (LLMs) to predict financial risks. RiskLabs integrates various types of financial data, including textual and vocal information from earnings conference calls (ECCs), market-related time series data, and contextual news data surrounding ECC release dates. The framework employs a multi-stage process: extracting and analyzing ECC data using LLMs, gathering and processing time-series data, and using multimodal fusion techniques to amalgamate these data features for comprehensive financial risk prediction. Empirical results demonstrate the effectiveness of RiskLabs in forecasting both volatility and variance in financial markets. The paper also discusses the contributions of LLMs in financial risk assessment and addresses challenges such as hallucination and the dynamic nature of financial markets. The framework's structure and functionality are detailed, including the Earnings Conference Call Encoder, Time-Series Encoder, Relevant News Encoder, and Multi-Task Prediction Block. The paper concludes with a comprehensive analysis of the role of LLMs in financial risk prediction and proposes practical solutions to enhance the framework's performance.