With the macroeconomy entering a new normal, many new problems are exposed in all walks of life, and the risk of default in the financial sector is also being exposed at an accelerated pace. In the context of big data, internet finance, as an important part of the financial market, also faces many risks in the process of its rapid development. Reasonable, scientific, and effective prediction and prevention of financial default risk have become a key link in the process of risk management practice in the financial industry. Based on the powerful prediction function of the neural network, this paper combined neural network and chaos theory to construct a chaotic RBF neural network. It was applied to financial default risk prediction, which made the prediction accuracy and efficiency higher. The chaotic neural network solves the shortcomings of unstable prediction in the basic neural network and can comprehensively and accurately predict the financial default risk, so as to take measures to prevent risks. The experimental results of this paper show that the accuracy rate of the chaotic RBF neural network reaches 95%, while the accuracy rates of the BP neural network and the RBF neural network are 67% and 78%, respectively. Although the prediction accuracy of these two methods is also high, it is still not as high as the chaotic RBF neural network. Therefore, it is very meaningful to choose the chaotic RBF neural network to predict financial default risk in this paper.

中文翻译：

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大数据背景下基于神经网络的金融违约风险预测算法

随着宏观经济进入新常态，各行各业都暴露出许多新问题，金融领域的违约风险也在加速暴露。在大数据背景下，互联网金融作为金融市场的重要组成部分，在其快速发展的过程中也面临诸多风险。对金融违约风险进行合理、科学、有效的预测和防范，已成为金融业风险管理实践过程中的关键环节。基于神经网络强大的预测功能，本文将神经网络与混沌理论相结合，构建了混沌RBF神经网络。应用于金融违约风险预测，提高了预测的准确性和效率。混沌神经网络解决了基础神经网络预测不稳定的缺点，能够全面准确地预测金融违约风险，从而采取措施防范风险。本文的实验结果表明，混沌RBF神经网络的准确率达到95%，而BP神经网络和RBF神经网络的准确率分别为67%和78%。虽然这两种方法的预测精度也很高，但仍然不如混沌 RBF 神经网络。因此，本文选择混沌 RBF 神经网络来预测金融违约风险是非常有意义的。本文的实验结果表明，混沌RBF神经网络的准确率达到95%，而BP神经网络和RBF神经网络的准确率分别为67%和78%。虽然这两种方法的预测精度也很高，但仍然不如混沌 RBF 神经网络。因此，本文选择混沌 RBF 神经网络来预测金融违约风险是非常有意义的。本文的实验结果表明，混沌RBF神经网络的准确率达到95%，而BP神经网络和RBF神经网络的准确率分别为67%和78%。虽然这两种方法的预测精度也很高，但仍然不如混沌 RBF 神经网络。因此，本文选择混沌 RBF 神经网络来预测金融违约风险是非常有意义的。