This paper introduces a deep learning model for predicting high-risk behaviors in financial traders by analyzing large volumes of transaction data. The model uses unsupervised pre-training to learn distributed representations of data, capturing complex relationships. It then employs a deep neural network, enhanced through supervised learning, to classify and predict traders' risk levels. The model focuses on financial spread trading related to Contracts For Difference (CFD), identifying potential misuse of insider information and assessing risks to market makers. By distinguishing between high-risk (A-book) and lower-risk (B-book) clients, the model supports strategic hedging decisions, crucial for market stability. The model's robustness and accuracy are validated through extensive evaluations, highlighting its potential for practical implementation in dynamic financial markets. The study demonstrates the effectiveness of deep neural networks in financial risk management, showing that they outperform traditional machine learning models in classification tasks. The model's ability to adapt to the dynamic nature of trader behaviors and capture shifts in risk profiles is critical in speculative markets where past performance does not reliably predict future risk. The integration of deep learning into financial risk management promises enhanced accuracy in risk assessment and a more strategic approach to hedging, thereby improving the resilience and stability of financial markets. The study also highlights the importance of using deep learning strategies in financial risk management, potentially transforming the landscape of financial analysis and decision-making.This paper introduces a deep learning model for predicting high-risk behaviors in financial traders by analyzing large volumes of transaction data. The model uses unsupervised pre-training to learn distributed representations of data, capturing complex relationships. It then employs a deep neural network, enhanced through supervised learning, to classify and predict traders' risk levels. The model focuses on financial spread trading related to Contracts For Difference (CFD), identifying potential misuse of insider information and assessing risks to market makers. By distinguishing between high-risk (A-book) and lower-risk (B-book) clients, the model supports strategic hedging decisions, crucial for market stability. The model's robustness and accuracy are validated through extensive evaluations, highlighting its potential for practical implementation in dynamic financial markets. The study demonstrates the effectiveness of deep neural networks in financial risk management, showing that they outperform traditional machine learning models in classification tasks. The model's ability to adapt to the dynamic nature of trader behaviors and capture shifts in risk profiles is critical in speculative markets where past performance does not reliably predict future risk. The integration of deep learning into financial risk management promises enhanced accuracy in risk assessment and a more strategic approach to hedging, thereby improving the resilience and stability of financial markets. The study also highlights the importance of using deep learning strategies in financial risk management, potentially transforming the landscape of financial analysis and decision-making.