Abstract
In order to deal with the problems of traditional e-banking risk measurement and early warning methods, such as low accuracy of e-banking risk measurement and longer early warning time, an e-banking risk measurement and early warning method based on the GMDH algorithm is proposed. This scheme mines the e-banking risk measurement and early warning indicators by the GMDH algorithm, and it will input the influencing factors and risk factors as independent variables into the GMDH modeling network and then input the e-banking business growth rate as the dependent variable into the GMDH modeling network which is standardized by the normative method of processing the e-banking business risk measurement and early warning index data. According to the processing results, it calculates the weight of the measurement and early warning index by the entropy method, and it constructs the e-banking risk measurement model with the genetic algorithm which can help to calculate the optimal solution of the parameters, formulate the risk measurement interval, and determine the risk in order to realize the risk warning of electronic banking business. The simulation results show that the proposed method has a higher accuracy of e-banking risk measurement and a shorter warning time.
Highlights
With the continuous internationalization of the financial industry and the continuous innovation of financial products, the banking business environment is undergoing profound changes, and its complexity and uncertainty are increasing day by day
(2) We use this new scheme to mine the e-banking risk measurement and early warning indicators by the GMDH algorithm, and it will input the influencing factors and risk factors as independent variables into the GMDH modeling network and input the e-banking business growth rate as the dependent variable into the GMDH modeling network which is standardized by the normative method of processing the e-banking business risk measurement and early warning index data
Is paper proposes a self-organizing data mining algorithm (GMDH)-based e-banking risk measurement and early warning method. It can mine the e-banking risk measurement and early warning indicators by the GMDH algorithm, and it will input the influencing factors and risk factors as independent variables into the GMDH modeling network and input the e-banking business growth rate as the dependent variable into the GMDH modeling network which is standardized by the normative method of processing the e-banking business risk measurement and early warning index data
Summary
With the continuous internationalization of the financial industry and the continuous innovation of financial products, the banking business environment is undergoing profound changes, and its complexity and uncertainty are increasing day by day. Literature [3] proposes a commercial bank credit risk measurement method based on BP neural network. It uses the on-balance sheet and off-balance sheet business of commercial banks as the entry point and uses the financial data of 500 credit enterprises from the local corporate bank in Guizhou Province to improve the BP neural network model. According to the preprocessing results, the Bayesian network model measures the operational risks of listed commercial banks in my country. (1) the abovementioned literature have put forward some suggestions for dealing with e-banking risk measurement and early warning, the accuracy of e-banking risk measurement is low and the early warning time is longer For this reason, this paper proposes a self-organizing data mining algorithm (GMDH)-based e-banking risk measurement and early warning method.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
