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Credit scoring and behavioral scoring have become very important credit risk management tasks during the past few years due to the impact of several financial crises. The objective of the proposed study is to explore the performance of behavioral scoring using three commonly discussed data mining techniques-linear discriminant analysis (LDA), backpropagation neural networks (BPN), and support vector...
In this paper, the current forecast of storm surge based on BP is adapted to deal with the characteristics of storm surge. One main kind of fuzzy information in geology calamity predicting system is solved by information diffusion method. The whole process is as follows: Firstly, influential information is collected by single step predicting model and neural network predicting model separately to...
Risk Management improvement and credit risk evaluation are turning core areas of concern within the financial and banking industries. Specifically credit scoring, as one of the key analytical techniques in credit risk evaluation is envisioned as an arena in which the application of Artificial Intelligence (IA) and Neural systems has high potential for development. This paper contributes by presenting...
Flood disaster management is an important part of flood risk assessment. A regional flood disaster risk assessment index system is established in this paper. Then principal component analysis (PCA) method and BP neural network are combined, and a regional flood disaster risk assessment of PCA-BP neural network model is established. PCA-BP neural network model analyze the loss of flood disaster about...
The paper combines theory with practice and applies neural network technology to establish a credit risk assessment model based on BP neural network technology. The assessment model, to some extent, improves the traditional credit risk analytical approaches in our country, overcoming the defects that subjectivity exists in credit risk measurement, expanding developing route for credit risk measurement...
With financial globalization, the rapid development of financial derivatives and the complexity of banks management, operational risk measurement and management in commercial bank management is becoming increasingly important. How to effectively predict, control and prevent operational risk in commercial banks have become an important issue. Using BP neural network model to predict the risk has its...
Product innovation is the main way for the economic development of enterprise, but product innovation project is high ricky and uncertainty, so the risk assessment and management of product innovation project has become concerned about academic and business community.This paper intends to do a systemic analysis of product innovation project risk evaluation, researching the product innovation project...
The traditional financial risk warning model are all based on probability theory and statistical analysis, but the precisions of the results are usually not satisfied in practice. In this study, rough set theory is first used to evaluate factors and then the key factors are selected as inputs to construct a neural network model combined with fuzzy rules. Furthermore, the fuzzy neural network (FNN)...
Information security risk assessment is essential to government for making an efficient and effective security management plan. This paper firstly established a hierarchy structure index system for E-government information systems security risk assessment based on the operationally critical threat, assets and vulnerability evaluation (OCTAVE). Considering that the previous weights selection methods...
According to the complexity of financial system, the model of credit risk assessment based on PSO algorithm and BP neural network integrated is proposed, which in order to improve the accuracy and reliability of risk assessment. First the neural network model of a credit risk evaluation is created, and then PSO algorithm is introduced to optimize the weight and threshold of the neural network, at...
Power system load was effected by many factors such as weather conditions, holidays, day types, so that the build of short-term load forecasting model is very important. The author analyzed the theory of support vector machine, studied the learning discipline of minimize the structural risk, solved the problem of insufficient training samples better. At the base of support vector machine, The author...
As known, real estate investment has many distinguish features: heavy invest, long recovery and high yield etc. So when facing the temptation of high yield, real estate enterprise has to assess risks scientifically. Especially since American subprime crisis broke out, trading volumes of real estate keep shrinking and house prices keep dropping. In this situation, risk management is particularly important...
In this paper, we studied the two most commonly used artificial intelligence methods (Multilayer Perceptron and Radial Basis Function network) to build the credit scoring model of applications, and analyzed the most important restraining factors of the applications of neural network which is the exponential increase in the variables bringing the model over-complex. On this basis, the author combines...
The construction industry is plagued by risk and often suffers poor performance as a result. Therefore, risk management is very important for construction project to achieve its goal. The risk evaluation is the basic work of risk management. There are a number of risk evaluation techniques, but each has its own faults. In this paper, a new model, which integrates Artificial Neural Networks and Rough...
Traditional risk assessment methods of construction project are often affected by the subjective factors. In order to reduce or avoid the effect of subjective factors, This paper firstly established the project risk evaluation index system, based on the detailed analysis of the project's internal and external environment, then built up the risk evaluation model with BP neural network, learned and...
Developed a novel model named dynamic neural network model for the risk evaluation of an investment project. The fundamental identification of weight was given based on Triangle Fuzzy Number and Analytical Hierarchy Process (TFN-AHP), through combining the weight with dynamic decision group, a formula of dynamic normalized weight vector was established by Clustering, and the final weight vector of...
Credit is the cornerstone of modern market economy. Credit risk is one of the most important risks which the banks are facing to. Credit risk Evaluation virtually is a non-linear classification matter. The banks evaluate and classify the clients according to their information data, then according to the results of classification to decide whether to authorize the loans. This article established the...
An assessment method for water shortage risk based on neural network classificatory of fuzzy sets is presented in paper. Risk rate, weakness, possibility of recovery, period for reoccurrence and risk level are defined as the indexes for water shortage risk assessment of regional resources. The suggested model is used to evaluate water shortage risk of Zhanghe irrigation region in Hubei Province in...
With rapid development of information technology, the information system has been widely applying in government, national defense and economic sphere. The security problem of information system is more and more related to the economic development. Therefore, evaluating risk effectively, selecting effective defense measures and defending information threats actively are the key points of resolving...
Based on analyzing the current way of evaluating the credit risk, BP neural network (BPNN) is used in the paper. BPNN is powerful for the problem with non-linear and high dimension. A multi-class BPNN model is constructed to measure the credit sales risk. In this model, dominant factors affecting the credit sales risk are the input vectors and the credit risk is divided into five grades. Through a...
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