Optimization Model of Financial Decision Support System Integrating Big Data and Artificial Intelligence

In this paper, an optimization model of financial decision support system combining big data and artificial intelligence technology is proposed, which aims to accurately warn the financial risks of enterprises through efficient data processing and intelligent analysis. The model constructs a deep learning framework. Combining with Convolutional Neural Network (CNN) and Back Propagation Neural Network (BPNN), CNN is used to extract and process the preliminary features of multi-source data, and the potential feature patterns in the data are mined, and the processed feature data are transmitted to BPNN. BPNN further analyzes and integrates the characteristics, and outputs the final financial risk assessment results and decision-making suggestions. In addition, the weight optimization strategy of CNN based on Cuckoo Algorithm (CA) is introduced. By simulating the parasitic behavior of Cuckoo and Levy’s flight behavior, the weight allocation of CNN is optimized, and the performance and accuracy of the model are improved. The results show that the model is superior to the traditional model based on recurrent neural network (RNN) in terms of risk prediction accuracy, recall rate, $\mathbf{F 1}$ score, stability and user satisfaction, and can provide reliable financial decision support for enterprises.