Construction of Financial data Prediction Risk Model in the Era of Big Data

Traditional financial data analysis relies on limited historical data and cannot reflect complex market dynamics and risk factors. In the context of big data, this article adopts the Long Short Term Memory (LSTM) model to construct a financial data prediction risk model, which can accurately and real-time predict the risks of the financial market. It collects a large amount of financial data, preprocesses the data, and converts it into a data format suitable for LSTM model analysis. This article uses a bidirectional LSTM model for risk prediction and updates model parameters through a cross entropy loss function. The experimental results in the test set indicate that the average accuracy of the bidirectional LSTM model for financial risk (FR) prediction is 97.7%. Therefore, building a financial data prediction risk model through bidirectional LSTM can accurately perform FR prediction analysis.