Download or read book An Effective Classification Model of Credit Rating And Default of Medium Small and Micro Enterprises Based on The Genetic Back Propagation Neural Network written by wei jin and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Credit Rating Modelling by Neural Networks written by Petr Hájek and published by . This book was released on 2010 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Application of AI in Credit Scoring Modeling written by Bohdan Popovych and published by Springer Nature. This book was released on 2022-12-07 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Using Artificial Neural Networks Analysis for Small Enterprise Default Prediction Modeling written by Francesco Ciampi and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Artificial Intelligence in Asset Management written by Söhnke M. Bartram and published by CFA Institute Research Foundation. This book was released on 2020-08-28 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Credit Rating Migration Risks in Structure Models written by Jin Liang and published by Springer Nature. This book was released on with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Credit Scoring Response Modelling and Insurance Rating written by S. Finlay and published by Springer. This book was released on 2010-10-27 with total page 295 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Credit Scoring Response Modeling and Insurance Rating written by S. Finlay and published by Springer. This book was released on 2012-06-26 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book A Neural Network Model for Classification Problem Using Backpropagation Method written by Xiaoyi Chen and published by . This book was released on 1994 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Default Prediction Modeling for Small Enterprises written by Francesco Ciampi and published by . This book was released on 2013 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: Previous empirical research shows the effectiveness of using sets of economic-financial ratios for company default prediction statistical modeling. However, such research rarely focuses on small enterprises (SEs) as specific units of analysis. In Italy, SEs account for more than 98% of all firms and employ over 70% of the total workforce. The results of our statistical analyses, conducted on a sample of small manufacturing firms in Northern and Central Italy, show that both discriminant analysis and logistic regression are effective tools for designing SEs' default prediction models based on economic-financial ratios. In addition, the proposed models gain in prediction accuracy when they are specifically constructed for separate business sectors and separate company size groups.Without denying the value of jointly using quantitative and qualitative variables to measure a firm's rating, this paper confirms that: i) SEs' credit rating models must adequately weight sets of appropriately selected financial and economic ratios; ii) SEs' credit rating should be modeled separately from that of large and medium-sized firms; and iii) SEs' credit rating models need to be specifically designed so as to take into account the diverse economic and financial profiles of firms in different industries and at different stages of growth.
Download or read book Consumer Credit Scoring Models with Limited Data written by Maja Sustersic and published by . This book was released on 2007 with total page 23 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper we design the neural network consumer credit scoring models for financial institutions where data usually used in previous research are not available. We use extensive primarily accounting data set on transactions and account balances of clients available in each financial institution. As many of these numerous variables are correlated and have very questionable information content, we considered the issue of variable selection and the selection of training and testing sub-sets crucial in developing efficient scoring models. We used a genetic algorithm for variable selection. In dividing performing and nonperforming loans into training and testing sub-sets we replicated the distribution on Kohonen artificial neural network, however, when evaluating the efficiency of models, we used k-fold cross-validation. We developed consumer credit scoring models with error back propagation artificial neural networks and checked their efficiency against models developed with logistic regression. Considering the dataset of questionable information content, the results were surprisingly good and one of the error back propagation artificial neural network models has shown the best results. We showed that our variable selection method is well suited for the addressed problem.
Download or read book The Use of Genetic Algorithm Clustering and Feature Selection Techniques in Construction of Decision Tree Models for Credit Scoring written by Mohammad Khanbabaei and published by . This book was released on 2020 with total page 19 pages. Available in PDF, EPUB and Kindle. Book excerpt: Decision tree modelling, as one of data mining techniques, is used for credit scoring of bank customers. The main problem is the construction of decision trees that could classify customers optimally. This study presents a new hybrid mining approach in the design of an effective and appropriate credit scoring model. It is based on genetic algorithm for credit scoring of bank customers in order to offer credit facilities to each class of customers. Genetic algorithm can help banks in credit scoring of customers by selecting appropriate features and building optimum decision trees. The new proposed hybrid classification model is established based on a combination of clustering, feature selection, decision trees, and genetic algorithm techniques. We used clustering and feature selection techniques to pre-process the input samples to construct the decision trees in the credit scoring model. The proposed hybrid model choices and combines the best decision trees based on the optimality criteria. It constructs the final decision tree for credit scoring of customers. Using one credit data set, results confirm that the classification accuracy of the proposed hybrid classification model is more than almost the entire classification models that have been compared in this paper. Furthermore, the number of leaves and the size of the constructed decision tree (i.e. complexity) are less, compared with other decision tree models. In this work, one financial data set was chosen for experiments, including Bank Mellat credit data set.
Download or read book Interpretability of Neural Networks written by Ksenia Ponomareva and published by . This book was released on 2020 with total page 17 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks have risen in popularity for a number of applications, also in quantitative finance. However, the low interpretability of their 'black box' representation has always been a common criticism. Previous literature has attempted to provide a better understanding and visualisation of neural networks, focusing primarily on image classification. This paper shows the feasibility of applying the same methods to an example deep neural network model, concerned with the estimation of credit risk for a portfolio of credit cards. Results show that the analysis of relevance, sensitivity and neural activities can increase the interpretability of a neural network in a financial modelling context.
Download or read book Are Credit Scoring Models Able to Predict Small Enterprise Default Statistical Evidence from Italian Small Enterprises written by Francesco Ciampi and published by . This book was released on 2013 with total page 19 pages. Available in PDF, EPUB and Kindle. Book excerpt: From as early as the 1960s, there have been a large number of studies aimed at assessing the application of statistical models to corporation data with a view to predicting business failure. This issue has become increasingly important in recent years, as the New Basel Capital Accord (Basel II) linked capital requirements to banks' models for company default prediction. Empirical research shows that economic-financial ratios can help to predict company default through the implementation of statistical techniques. The literature focuses mainly on large and medium sized enterprises that systematically produce detailed financial documentation. However, the financial statements of small enterprises (SEs) tend to disclose less (and are therefore more difficult to interpret), and this prevents a widespread use of statistical models. The issue is of vital importance in countries like Italy with large numbers of SEs. This paper applies mainstream statistical techniques (linear discriminant analysis and logistic regression) to a sample of over 6,000 Italian firms in the attempt to develop two distress prediction models, specifically constructed for SEs and taking into account diversity of size, geographical location and business sector. For both models, prediction accuracy increases progressively with larger firms, and is higher in the North and in manufacturing firms. The success rate is lower in smaller firms and for those located in Southern Italy. Our results suggest that the limited information in SE accounts affects a model's prediction success rate, and also that SEs need to be assessed with specifically designed models.
Download or read book A Primer on Machine Learning Methods for Credit Rating Modeling written by Yixiao Jiang and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using machine learning methods, this chapter studies features that are important to predict corporate bond ratings. There is a growing literature of predicting credit ratings via machine learning methods. However, there have been less empirical studies using ensemble methods, which refer to the technique of combining the prediction of multiple classifiers. This chapter compares six machine learning models: ordered logit model (OL), neural network (NN), support vector machine (SVM), bagged decision trees (BDT), random forest (RF), and gradient boosted machines (GBMs). By providing an intuitive description for each employed method, this chapter may also serve as a primer for empirical researchers who want to learn machine learning methods. Moody,Äôs ratings were employed, with data collected from 2001 to 2017. Three broad categories of features, including financial ratios, equity risk, and bond issuer,Äôs cross-ownership relation with the credit rating agencies, were explored in the modeling phase, performed with the data prior to 2016. These models were tested on an evaluation phase, using the most recent data after 2016.
Download or read book Developing and Testing Models for Replicating Credit Ratings written by Michael Doumpos and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Credit ratings issued by international agencies are extensively used in practice to support investment and financing decisions. Furthermore, a considerable portion of the financial research has been devoted to the analysis of credit ratings, in terms of their effectiveness, and practical implications. This paper explores the development of appropriate models to replicate the credit ratings issued by a rating agency. The analysis is based on a multicriteria classification method used in the development of the model. Special focus is laid on testing the out-of-time and out-of-sample effectiveness of the models and a comparison is performed with other parametric and non-parametric classification methods. The results indicate that using publicly available financial data, it is possible to replicate the credit ratings of the firms with a satisfactory accuracy.
Download or read book Can Credit Scoring Models Effectively Predict Small Enterprise Default Statistical Evidence from Italian Firms written by Francesco Ciampi and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper applies mainstream statistical techniques (linear discriminant analysis and logistic regression) to a sample of over 6,000 Italian firms in the attempt to develop two distress prediction models, specifically constructed for SEs and taking into account diversity of size, geographical location and business sector. For both models, prediction accuracy increases progressively with larger firms, and is higher in the North and in manufacturing firms.
