This paper introduces RiskLabs, a novel framework that leverages large language models (LLMs) to analyze and predict financial risks by integrating diverse data sources, including textual and vocal information from earnings conference calls (ECCs), market-related time series data, and contextual news data. The framework employs a multi-stage process: extracting and analyzing ECC data using LLMs, gathering and processing time-series data before ECC dates, and using multimodal fusion techniques to combine these data features for comprehensive financial risk prediction. Empirical results demonstrate RiskLabs' effectiveness in forecasting financial market volatility and variance. Comparative experiments highlight the contribution of different data sources to financial risk assessment and the critical role of LLMs in this context. The framework includes four key modules: Earnings Conference Call Encoder, News-Market Reactions Encoder, Time-Series Encoder, and Multi-Task Prediction Block. The Earnings Conference Call Encoder processes audio and textual data from ECCs, while the News-Market Reactions Encoder analyzes news data to gauge market reactions. The Time-Series Encoder processes historical and real-time market data, and the Multi-Task Prediction Block integrates these features for multifaceted risk prediction. The framework also incorporates retrieval augmentation generation (RAG) to enhance the accuracy of information retrieval and analysis. The study addresses challenges in financial risk prediction, including the integration of diverse data types and the limitations of traditional models. The proposed approach uses a Bayes-VaR method to capture complex relationships between response variables, improving the accuracy of risk predictions. The framework's contributions include the development of RiskLabs, the integration of diverse financial data sources, and the demonstration of the framework's effectiveness in forecasting financial risks. The study also highlights the potential of LLMs in financial risk assessment and the importance of addressing challenges such as data preprocessing and model adaptability.This paper introduces RiskLabs, a novel framework that leverages large language models (LLMs) to analyze and predict financial risks by integrating diverse data sources, including textual and vocal information from earnings conference calls (ECCs), market-related time series data, and contextual news data. The framework employs a multi-stage process: extracting and analyzing ECC data using LLMs, gathering and processing time-series data before ECC dates, and using multimodal fusion techniques to combine these data features for comprehensive financial risk prediction. Empirical results demonstrate RiskLabs' effectiveness in forecasting financial market volatility and variance. Comparative experiments highlight the contribution of different data sources to financial risk assessment and the critical role of LLMs in this context. The framework includes four key modules: Earnings Conference Call Encoder, News-Market Reactions Encoder, Time-Series Encoder, and Multi-Task Prediction Block. The Earnings Conference Call Encoder processes audio and textual data from ECCs, while the News-Market Reactions Encoder analyzes news data to gauge market reactions. The Time-Series Encoder processes historical and real-time market data, and the Multi-Task Prediction Block integrates these features for multifaceted risk prediction. The framework also incorporates retrieval augmentation generation (RAG) to enhance the accuracy of information retrieval and analysis. The study addresses challenges in financial risk prediction, including the integration of diverse data types and the limitations of traditional models. The proposed approach uses a Bayes-VaR method to capture complex relationships between response variables, improving the accuracy of risk predictions. The framework's contributions include the development of RiskLabs, the integration of diverse financial data sources, and the demonstration of the framework's effectiveness in forecasting financial risks. The study also highlights the potential of LLMs in financial risk assessment and the importance of addressing challenges such as data preprocessing and model adaptability.