Credit-Risk Decision Process Using Neural Networks in Industrial Sectors

2018 ◽  
pp. 71-81
Author(s):  
Aleksandra Wójcicka
Keyword(s):  
Neural Networks ◽  
Credit Risk ◽  
Decision Process ◽  
Industrial Sectors ◽  
Risk Decision

Credit Risk Evaluation Using Neural Networks

2008 ◽  
pp. 163-179 ◽  
Author(s):  
Z. Yang ◽  
D. Wu ◽  
G. Fu ◽  
C. Luo
Keyword(s):  
Neural Networks ◽  
Credit Risk ◽  
Risk Evaluation

scholarly journals Applying Artificial Neural Networks to Forecast European Union Allowance Prices: The Effect of Information from Pollutant-Related Sectors

Energies ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4439
Author(s):  
Miguel A. Jaramillo-Morán ◽  
Agustín García-García
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Co2 Emission ◽  
Forecasting Accuracy ◽  
Iron And Steel ◽  
Industrial Sectors ◽  
Past Data ◽  
Original Time ◽  
Model Structures ◽  
Political Decisions

In this paper, we forecast the price of CO2 emission allowances using an artificial intelligence tool: neural networks. We were able to provide confident predictions of several future prices by processing a set of past data. Different model structures were tested. The influence of subjective economic and political decisions on price evolution leads to complex behavior that is hard to forecast. We analyzed correlations with different economic variables related to the price of CO2 emission allowances and found the behavior of two to be similar: electricity prices and iron and steel prices. They, along with CO2 emission allowance prices, were included in the forecasting model in order to verify whether or not this improved forecasting accuracy. Only slight improvements were observed, which proved to be more significant when their respective time series trends or fluctuations were used instead of the original time series. These results show that there is some sort of link between the three variables, suggesting that the price of CO2 emission allowances is closely related to the time evolution of the price of electricity and that of iron and steel, which are very pollutant industrial sectors. This can be regarded as evidence that the CO2 market is working properly.

Making Accurate Credit Risk Predictions with Cost-Sensitive MLP Neural Networks

2013 ◽  
pp. 1-8 ◽  
Author(s):  
R. Alejo ◽  
V. García ◽  
A. I. Marqués ◽  
J. S. Sánchez ◽  
J. A. Antonio-Velázquez
Keyword(s):  
Neural Networks ◽  
Credit Risk ◽  
Mlp Neural Networks

Evaluating the risk decision process

Toxicology ◽  
2006 ◽  
Vol 222 (1-2) ◽  
pp. 154-155
Author(s):  
Thomas Höfer
Keyword(s):  
Decision Process ◽  
Risk Decision

A NEURAL NETWORK MODEL FOR CREDIT RISK EVALUATION

2009 ◽  
Vol 19 (04) ◽  
pp. 285-294 ◽  
Author(s):  
ADNAN KHASHMAN
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Credit Risk ◽  
Network Model ◽  
Neural Network Model ◽  
Risk Evaluation ◽  
Evaluation System ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Learning Schemes

Credit scoring is one of the key analytical techniques in credit risk evaluation which has been an active research area in financial risk management. This paper presents a credit risk evaluation system that uses a neural network model based on the back propagation learning algorithm. We train and implement the neural network to decide whether to approve or reject a credit application, using seven learning schemes and real world credit applications from the Australian credit approval datasets. A comparison of the system performance under the different learning schemes is provided, furthermore, we compare the performance of two neural networks; with one and two hidden layers following the ideal learning scheme. Experimental results suggest that neural networks can be effectively used in automatic processing of credit applications.

Application of Chaos Particle Swarm Optimized Neural Networks for Evaluating the Credit Risk

2011 ◽  
Vol 460-461 ◽  
pp. 687-691
Author(s):  
Zhi Bin Xiong
Keyword(s):  
Neural Networks ◽  
Credit Risk ◽  
Simple Structure ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Test Results ◽  
Chaos Optimization ◽  
Proposed Model ◽  
The Neural Networks ◽  
Better Than

This paper proposes a hybrid algorithm based on chaos optimization and particle swarm optimization (PSO) to improve the performance of the neural networks (NN) on evaluating credit risk. The hybrid algorthm not only maintains the advantage of simple structure, but also improves the convergence of the traditional PSO algorithm, and enhances the global optimization capability and accuracy of the algorithm. The test results indicate that the performance of the proposed model is better than the ones of NN model using the BP algorithm and traditional PSO algorithm.

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