In the context of increasing complexity and volatility in enterprise project operations and financial systems, this study proposes an integrated risk management framework combining Internet of Things (IoT) technologies, BP neural networks, and data-driven modeling approaches. The research addresses risk identification and control in multi-project management environments by constructing a resource conflict risk evaluation model based on neural networks, utilizing Garson sensitivity analysis to rank risk factor significance. For financial operations, an IoT-enabled inventory pledge financing model is developed to mitigate fraud and market fluctuation risks through real-time monitoring and intelligent data processing. Empirical analysis of operational risk loss data from banks and financial institutions is conducted using SPSS and the Peak Over Threshold (POT) model. Value at Risk (VaR) and Expected Shortfall (ES) are applied to quantify high-loss risks across categories such as internal fraud, external fraud, employee error, and IoT system failures. The study further implements a multi-layered embedded management system and proposes algorithmic enhancements for clustering and optimization in resource allocation. Results demonstrate that integrating IoT and neural networks significantly improves risk visibility, early warning capability, and systemic stability in both marketing and financial domains.