An Effective Classification Model of Credit Rating And Default of Medium, Small and Micro Enterprises Based on The Genetic Back Propagation Neural Network PDF Download
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Author: wei jin Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
In China, medium, small, and micro enterprises (MSMEs) play an important role in economic development, but they are difficult to obtain a substantial loan due to their unquantifiable credit rating and default. To address this issue, this paper applies machine learning and intelligent optimization algorithms to establish a classification model of default and credit rating of MSMEs based on their daily invoice data. More precisely, 12 indicators related to default and credit rating are extracted, and then the principal component analysis is conducted to reduce the dimension and synthesize all information. Subsequently, the genetic back propagation neural network (GA-BPNN) is adopted to characterize the relationship between indicators and default and credit rating, respectively. The results indicate that the prediction accuracy of default risk and credit rating is 0.92 and 0.86, respectively. This demonstrates that GA-BPNN can classify the underlying default and credit rating of MSMEs effectively and provides a potential decision-making approach.
Author: wei jin Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
In China, medium, small, and micro enterprises (MSMEs) play an important role in economic development, but they are difficult to obtain a substantial loan due to their unquantifiable credit rating and default. To address this issue, this paper applies machine learning and intelligent optimization algorithms to establish a classification model of default and credit rating of MSMEs based on their daily invoice data. More precisely, 12 indicators related to default and credit rating are extracted, and then the principal component analysis is conducted to reduce the dimension and synthesize all information. Subsequently, the genetic back propagation neural network (GA-BPNN) is adopted to characterize the relationship between indicators and default and credit rating, respectively. The results indicate that the prediction accuracy of default risk and credit rating is 0.92 and 0.86, respectively. This demonstrates that GA-BPNN can classify the underlying default and credit rating of MSMEs effectively and provides a potential decision-making approach.
Author: Petr Hájek Publisher: ISBN: 9781616686796 Category : Credit analysis Languages : en Pages : 0
Book Description
This book presents the modelling possibilities of neural networks on a complex real-world problem, i.e. credit rating process modelling. Current approaches in credit rating modelling are introduced, as well as the incorporation of previous findings on corporate and municipal credit rating modelling. Based on this analysis, the model is designed to classify US companies and municipalities into credit rating classes. The model includes data pre-processing, the selection process of input variables, and the design of various neural networks' structures for classification.
Author: Bohdan Popovych Publisher: Springer Nature ISBN: 365840180X Category : Business & Economics Languages : en Pages : 93
Book Description
The scope of this study is to investigate the capability of AI methods to accurately detect and predict credit risks based on retail borrowers' features. The comparison of logistic regression, decision tree, and random forest showed that machine learning methods are able to predict credit defaults of individuals more accurately than the logit model. Furthermore, it was demonstrated how random forest and decision tree models were more sensitive in detecting default borrowers.
Author: Francesco Ciampi Publisher: ISBN: Category : Languages : en Pages :
Book Description
A large number of empirical studies have used univariate and multivariate statistical methods when examining the effectiveness of appropriately selected corporation data in constructing company default prediction models. Having accurate evaluation methods has become increasingly important since the New Basel Capital Accord linked the banks' capital requirements to the banks' models for company default prediction. Solutions are now urgently needed in view of the current global financial crisis which is having serious effects on the overall word economic system and is making it extremely difficult for banks to grant credit, and for firms to obtain it.The empirical studies mentioned mostly rely on Multivariate Discriminant Analysis (MDA) and Logistic Regression Analysis (LRA); and they mainly focus on large and medium-sized enterprises.Our study applies Artificial Neural Network Analysis (ANNA) to a sample of over 6,000 small Italian firms, with a view to developing and testing default prediction models based on an appropriately selected set of financial-economic ratios.Our results show that: i) when compared to traditional statistical methods (MDA and LRA), ANNA can make a better contribution to decision support systems for Small Enterprise (SE) credit-risk evaluation; and ii) when the decisional function is separately calculated according to size, geographical area and business sector, ANNA prediction accuracy is markedly higher for the smallest-sized firms and for firms operating in Central Italy.
Author: Lean Yu Publisher: Springer Science & Business Media ISBN: 3540778039 Category : Business & Economics Languages : en Pages : 248
Book Description
Credit risk analysis is one of the most important topics in the field of financial risk management. Due to recent financial crises and regulatory concern of Basel II, credit risk analysis has been the major focus of financial and banking industry. Especially for some credit-granting institutions such as commercial banks and credit companies, the ability to discriminate good customers from bad ones is crucial. The need for reliable quantitative models that predict defaults accurately is imperative so that the interested parties can take either preventive or corrective action. Hence credit risk analysis becomes very important for sustainability and profit of enterprises. In such backgrounds, this book tries to integrate recent emerging support vector machines and other computational intelligence techniques that replicate the principles of bio-inspired information processing to create some innovative methodologies for credit risk analysis and to provide decision support information for interested parties.
Author: S. Finlay Publisher: Springer ISBN: 1137031697 Category : Business & Economics Languages : en Pages : 183
Book Description
A guide on how Predictive Analytics is applied and widely used by organizations such as banks, insurance providers, supermarkets and governments to drive the decisions they make about their customers, demonstrating who to target with a promotional offer, who to give a credit card to and the premium someone should pay for home insurance.
Author: S. Finlay Publisher: Springer ISBN: 0230298982 Category : Business & Economics Languages : en Pages : 295
Book Description
Every year, financial services organizations make billions of dollars worth of decisions using automated systems. For example, who to give a credit card to and the premium someone should pay for their home insurance. This book explains how the forecasting models, that lie at the heart of these systems, are developed and deployed.
Author: Söhnke M. Bartram Publisher: CFA Institute Research Foundation ISBN: 195292703X Category : Business & Economics Languages : en Pages : 95
Book Description
Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity.
Author: wei jin
Author: wei jin
Author: Petr Hájek
Author: Bohdan Popovych
Author: Francesco Ciampi
Author: Lean Yu
Author: S. Finlay
Author: S. Finlay
Author: Söhnke M. Bartram
Author: Xiaoyi Chen
Author: Paul Markus Konrad