Fuzzy decision support system for financial planning and management

Abstract

With the increasing complexity of the business environment, corporate financial planning and management face many challenges of ambiguity and uncertainty. Traditional financial decision support systems have obvious shortcomings in dealing with such problems. This paper constructs a fuzzy decision support system for financial planning and management, which realizes efficient processing of financial data by quantifying fuzzy information, reasoning based on fuzzy rules, and defuzzifying output. The experiment uses real financial data sets from multiple industries to compare the system in this paper with classic models such as the autoregressive moving average model (ARIMA) and the support vector machine model (SVM). The results show that the system in this paper performs well in multiple dimensions such as prediction accuracy and decision risk control. For example, in the financial crisis warning scenario, the comprehensive warning accuracy rate reaches 88.2%, and the false alarm rate is only 5.6%, which is significantly better than the control model. This study not only enriches the theory of financial decision-making, but also provides an efficient and practical tool for corporate financial decision-making, helping enterprises make more scientific decisions in a complex economic environment.