The rapid development of information technology promotes the digital transformation of the financial industry, and the deep integration of big data technology and the financial market forms a big data financial model. Traditional financial risk assessment methods have limitations such as insufficient accuracy and slow convergence speed when dealing with massive multidimensional data. Big data algorithms show great potential in the fields of transaction fraud identification, credit risk assessment, customer marketing and stock market prediction, etc. However, the existing assessment models still face challenges such as local extreme value traps and poor generalization ability, and the construction of efficient and accurate financial risk assessment models has become a current research focus. METHODS: A regional financial risk assessment model based on Improved Cuckoo Optimization BPNN Neural Network (ICS-BPNN) is constructed, and a risk assessment index system is established by selecting five first-level indexes and 26 second-level indexes for the local macroeconomy, the government sector, the financial sector, the real estate sector and the real economy. The principal component analysis method of downscaling and entropy value method are used to determine the weights, and the traditional cuckoo algorithm is improved by dynamic step size and abandonment probability to optimize the weights and thresholds of BP neural network. Results: the ICS-BPNN algorithm reaches the global optimal solution of 0.046 after 20 iterations, while the traditional BP algorithm needs 42 iterations to find the optimal solution of 0.105. The absolute errors of the ICS-BPNN algorithm are all under 3.85, the risk prediction accuracies are all over 0.90, and the average value of R² of the fit of the test set is 0.848. The average value of the financial risk prediction of the eastern part of the country is 0.848 for the years 2026 and 2027, respectively. The predicted values of financial risk of the region in 2026 and 2027 are 0.482 and 0.527 respectively, which are both in risky status. Conclusion: The improved cuckoo algorithm effectively improves the convergence speed and prediction accuracy of BP neural network, and the ICS-BPNN model shows excellent performance in regional financial risk assessment, which provides reliable technical support for financial risk management.
