With the widespread application of cloud computing technology, the security of financial accounting information systems has increasingly come under scrutiny. This study explores the construction of an accounting information management cloud platform based on cloud computing. By analyzing the basic operational mechanisms of online accounting cloud platforms, it designs a DaaS process for the financial early warning module, providing a technical foundation for the operation of the financial crisis early warning module. Subsequently, a financial early warning model for enterprises based on a hybrid LSTM-GRU structure is proposed. Using M Company’s financial data from 2019 to 2024 as the research sample, a set of 24 potential financial indicators is established, covering five aspects: profitability, solvency, operational efficiency, cash flow capability, and growth potential. The research results show that incorporating Benford factors plays a certain role in improving the overall performance of the financial crisis early warning model. Additionally, the model enables more accurate and stable prediction results, with prediction accuracies of 95.74%, 94.83%, and 94.31% for T-1, T-2, and T-3 years, respectively, enabling accurate judgment of future corporate financial trends.