A Financial Performance Optimization Model Based on Group Intelligence Algorithm and Its Effectiveness Evaluation

Financial performance optimization is an important embodiment of enterprises to improve operational efficiency and optimize management level.The article proposes a method of financial performance optimization and evaluation using group intelligence algorithm in order to optimize the financial performance of enterprises.EVA is introduced to establish the evaluation index of enterprise financial performance.The financial performance prediction model is constructed according to the propagation process of BP neural network, and the IPSO-BP algorithm is utilized to avoid BP from falling into local optimum and improve the prediction accuracy.In the learning ability test, the relative errors of the EVA value, EVA payoff and EVA rate of the IPSO-BP algorithm are controlled within 6%, 8% and 10% respectively, and the average relative error of the model application results is 3.87%.The model in this paper can achieve more accurate financial performance assessment and prediction, which is conducive to the optimization of financial performance management of enterprises.