scholarly journals The Integration of Big Data and Artificial Neural Networks for Enhancing Credit Risk Scoring in Emerging Markets: Evidence from Egypt

2022 ◽  
Vol 14 (2) ◽  
pp. 32
Author(s):  
Osama Wagdi ◽  
Yasmeen Tarek
Keyword(s):  
Neural Networks ◽  
Big Data ◽  
Artificial Neural Networks ◽  
Emerging Markets ◽  
Credit Risk ◽  
Financial Stability ◽  
Proposed Model ◽  
Risk Scoring ◽  
Artificial Neural ◽  
The Impact

This study investigates the effectiveness of technology models in credit risk scoring modeling in emerging markets. the study proposes evaluation methods for credit risk scoring modeling for current and potential borrowers through an investigation into the Egyptian banking industry by offering and examining a framework for the integration of big data and artificial neural networks based on systematic and unsystematic risk for both the macroeconomic environment and characteristics of current and potential borrowers. The data for the borrowers under examination covers the period from 2015 to 2019 for 75 firms, excluding 2020 and 2021 data to isolate the impact of COVID-19 on the results of the inferred statistics. Artificial Neural Networks was training within 25 firms under NeuroXL program but examination for 50 firms. The study found the ability of artificial neural networks to rank the commitment of borrowers in Egyptian banks under big data about the firm and Egyptian economy. Additions to discrepancy between the proposed model against some traditional models. Finally; The Integration of Big Data and ANN can help banks to bring out the value of data within create a level of financial stability for banks. Especially in emerging markets characterized by information inefficiency.

scholarly journals Progress on Artificial Neural Networks for Big Data Analytics: A Survey

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 70535-70551 ◽  
Author(s):  
Haruna Chiroma ◽  
Usman Ali Abdullahi ◽  
Shafi'i Muhammad Abdulhamid ◽  
Ala Abdulsalam Alarood ◽  
Lubna A. Gabralla ◽  
...  
Keyword(s):  
Neural Networks ◽  
Big Data ◽  
Artificial Neural Networks ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Artificial Neural

scholarly journals Atmospheric Pollutant Flow and Precipitation: Modeling Effects on the Vegetation Ecosystem

2018 ◽  
Vol 7 (3) ◽  
pp. 157-161
Author(s):  
Allag Fateh ◽  
Saddek Bouharati ◽  
Lamri Tedjar ◽  
Mohamed Fenni
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Chemical Composition ◽  
Wide Distribution ◽  
Climatic Conditions ◽  
Mathematical Methods ◽  
Atmospheric Pollutant ◽  
Artificial Neural ◽  
Precipitation Modeling ◽  
The Impact

Because of their fixed life and wide distribution, plants are the first victims of air pollution. The atmosphere is considered polluted when the increase of the rate of certain components causes harmful effects on the different constituents of the ecosystems. The study of the flow of air near a polluting source (cement plant in our case), allows to predict its impact on the surrounding plant ecosystem. Different factors are to be considered. The chemical composition of the air, the climatic conditions, and the impacted plant species are complex parameters to be analyzed using conventional mathematical methods. In this study, we propose a system based on artificial neural networks. Since artificial neural networks have the capacity to treat different complex parameters, their application in this domain is adequate. The proposed system makes it possible to match the input and output spaces. The variables that constitute the input space are the chemical composition, the concentration of the latter in the rainwater, their duration of deposition on the leaves and stems, the climatic conditions characterizing the environment, as well as the species of plant studied. The output variable expresses the rate of degradation of this species under the effect of pollution. Learning the system makes it possible to establish the transfer function and thus predict the impact of pollutants on the vegetation.

scholarly journals PREDICTION OF FINANCIAL DISTRESS: CASE OF MINING ENTERPRISES IN CZECH REPUBLIC

2020 ◽  
Vol 15 (1) ◽  
pp. 1-14
Author(s):  
Zuzana Rowland ◽  
Alla Kasych ◽  
Petr Suler
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Czech Republic ◽  
Financial Stability ◽  
Grey Zone ◽  
Financial Health ◽  
Classification Problems ◽  
Mining Companies ◽  
Artificial Neural

The ability to predict a company's financial health is a challenge for many researchers and scientists. It is also a distracting topic, as many other new approaches to financial health predictions have emerged in recent years. In this paper, we focused on identifying the financial health of mining companies in the Czech Republic. We chose the neural network method because, based on various instances of related research, neural networks represent a more reliable financial forecast than mathematical-statistical methods such as discriminant analysis and logistic regression. The concept of a neural network emerged with the first artificial neural networks, inspired by biological systems. The existence of prediction and classification problems directly predetermines artificial neural networks with respect to a given issue. We used the Amadeus database for processing, including financial indicators, SPSS, and Visual Gene Developer software. In total, we analyzed sixty-four mining companies. Complete data on financial stability were available for fifty-three companies, which we explored, and based on these results, identified financial situations for the other thirteen. Based on the available information, we processed a neural network and regression analysis. We managed to classify thirteen companies as solvent, insolvent, and in the grey zone, with the help of prediction.

Artificial Neural Networks and Their Applications in Business

2018 ◽  
pp. 6642-6657
Author(s):  
Trevor J. Bihl ◽  
William A. Young II ◽  
Gary R. Weckman
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Big Data ◽  
Artificial Neural Networks ◽  
Model Complexity ◽  
Ann Model ◽  
Processing Methods ◽  
Starting Point ◽  
Clustering And Classification ◽  
Artificial Neural

Despite the natural advantage humans have for recognizing and interpreting patterns, large and complex datasets, as in Big Data, preclude efficient human analysis. Artificial neural networks (ANNs) provide a family of pattern recognition approaches for prediction, clustering and classification applicable to KDD with ANN model complexity ranging from simple (for small problems) highly complex (for large issues). To provide a starting point for readers, this chapter first describes foundational concepts that relate to ANNs. A listing of commonly used ANN methods, heuristics, and criteria for initializing ANNs is then discussed. Common pre- and post- data processing methods for dimensionality reduction and data quality issues are then described. The authors then provide a tutorial example of ANN analysis. Finally, the authors list and describe applications of ANNs to specific business related endeavors for further reading.

Artificial Neural Networks and Their Applications in Business

2019 ◽  
pp. 1009-1027
Author(s):  
Trevor J. Bihl ◽  
William A. Young II ◽  
Gary R. Weckman
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Big Data ◽  
Artificial Neural Networks ◽  
Model Complexity ◽  
Ann Model ◽  
Processing Methods ◽  
Starting Point ◽  
Clustering And Classification ◽  
Artificial Neural

Despite the natural advantage humans have for recognizing and interpreting patterns, large and complex datasets, as in big data, preclude efficient human analysis. Artificial neural networks (ANNs) provide a family of pattern recognition approaches for prediction, clustering, and classification applicable to KDD with ANN model complexity ranging from simple (for small problems) to highly complex (for large issues). To provide a starting point for readers, this chapter first describes foundational concepts that relate to ANNs. A listing of commonly used ANN methods, heuristics, and criteria for initializing ANNs are then discussed. Common pre- and post-data processing methods for dimensionality reduction and data quality issues are then described. The authors then provide a tutorial example of ANN analysis. Finally, the authors list and describe applications of ANNs to specific business-related endeavors for further reading.

Assessing the impact of PM2.5 on respiratory disease using artificial neural networks

2018 ◽  
Vol 235 ◽  
pp. 394-403 ◽  
Author(s):  
Gabriela Polezer ◽  
Yara S. Tadano ◽  
Hugo V. Siqueira ◽  
Ana F.L. Godoi ◽  
Carlos I. Yamamoto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Respiratory Disease ◽  
Artificial Neural ◽  
The Impact

Investigating of Correlation between MODIS Satellite Data and Ground Data Related to Aerosols and Modeling Aerosols Using Neural Networks over Shiraz

2012 ◽  
Vol 260-261 ◽  
pp. 926-929
Author(s):  
Ali Reza Dehghani ◽  
Ali Akbar Safavi ◽  
Mohammad Jafar Nazemossadat ◽  
Seyed Mohammad Hessam Mohammadi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Satellite Data ◽  
Satellite Image ◽  
Frequency Bands ◽  
Input And Output ◽  
Satellite Information ◽  
Proposed Model ◽  
Artificial Neural ◽  
Band Number

This paper presents an investigation of satellite data and ground data about aerosols and then modelsthe mentioned data over Shiraz using artificial neural networks. MODIS satellite data are available on 36 various frequency bands. In this study, a good correlation between ground data and the 10 first satellite image bands is being shown. Specially, the best correlation was found in band number 8. Therefore, using neural networks and ground data along with satellite information, a model of aerosols is constructed. In the mentioned model, satellite data of band 8 and ground data are used as network input and output, respectively. The results show the effectiveness of the proposed model.

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