Construction of Mobile Internet Financial Risk Cautioning Framework Based on BP Neural Network

Abstract

With the emergence of the 21st-century global economy, the international financial system faces economic risks. A competitive cautioning model for financial management is required to mitigate risks and losses in the financial sector. The financial losses of the banking industry have been categorized and analyzed using the Internet of Things (IoT) and big data technologies to minimize the economic risk of commercial banks in mobile internet finance (MIF). This article proposes a new financial risk cautioning framework (FRCF) based on the IoT, big data, and back propagation-neural network (BP-NN) to ensure steady growth of MIF in the long term. In this article, a big data technology-based approach for data recognition and mining has been suggested. A BP-NN-based method for risk identification and assessment in MIF is also presented. The BP-NN technique calculates each neural network (NN) layer’s node count, transfer functions, learning rate, and other characteristics. The proposed FRCF has been developed through the proper construction, analysis, and testing of many information samples. A conceptual understanding of the use of IoT, big data, and artificial intelligence (AI) technologies through NN models in the financial industry has been described in the article. The proposed FRCF can predict the MIF risks associated with the MIF lending infrastructure with a 98.2% accuracy.