Risk prediction of financial management in agricultural companies based on RBF neural network and Markov

Modern agricultural companies face numerous challenges, including financial management risks. In order to help agricultural companies better manage financial risks, this study proposes a method based on RBF neural networks and Markov models. Firstly, we use RBF neural networks to establish a financial prediction model. By inputting historical financial data such sales, costs, profits, etc., RBF neural networks can learn potential nonlinear relationships and predict future financial indicators. This model can help agricultural companies predict potential financial risks, such as losses, cash flow issues, etc; Secondly, we introduce a Markov model to consider the dynamic changes in financial risk, defining financial risk as different states. By observing past financial data, we can estimate the probability of state transition and predict future financial risk states; Finally, we combine the RBF neural network with the Markov model to establish a comprehensive financial management risk prediction model. This model can predict the future financial risks of agricultural companies based on past historical data and current market environment, and provide corresponding response strategies. This will help agricultural companies develop reasonable financial planning, reduce financial risks, and improve operational efficiency. Research has shown that financial management risk prediction based on RBF neural networks and Markov tends to be conservative and can accurately predict sudden changes when the data is complete. This method provides an effective tool for agricultural companies to better manage financial risks, improve operational level and efficiency.