The Data Analysis of Enterprise Operational Risk Prediction Under Machine Learning

In the digital age, the financial sector faces increasingly severe risk management challenges. Traditional methods often rely on historical data and statistical models, which struggle to cope with the high volatility of the market. These methods exhibit poor adaptability in rapidly changing financial markets and often fail to meet demands in terms of accuracy and reliability. To address these issues, this study proposes a law risk prediction model based on deep learning—the WBIF model. This model integrates Bidirectional Long Short-Term Memory (BiLSTM) and Fully Convolutional Networks (FCN) and employs the Whale Optimization Algorithm (WOA) for parameter optimization. Experimental results show that compared to traditional models, the WBIF model reduces the Mean Absolute Error (MAE) by 51.73% on the UCI machine learning library dataset and improves accuracy by 12% on the Kaggle credit card fraud detection dataset.