Construction and Exploration of a Financial Risk Control Model Based on Machine Learning

With the development and innovation of the financial industry, effective risk management has become an important task in the financial industry. Therefore, more and more financial institutions are adopting machine learning based methods to construct risk control models. This article discussed the construction and analysis methods of financial risk control models based on machine learning. Machine learning models can learn latent patterns and patterns from historical data, more accurately assess risks and detect fraudulent behavior. The construction process includes steps such as data preprocessing, feature engineering, model selection and training, model evaluation and optimization. Empirical analysis showed that machine learning models have advantages in tasks such as credit evaluation, fraud detection, and loan rejection. However, challenges such as data quality, feature selection, and model interpretability still need to be addressed. Further research can improve model performance and interpretability, enabling financial institutions to better manage risks and provide customers with safe and reliable financial services.