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 Rule Extraction from Support Vector Machines written by Joachim Diederich and published by Springer. This book was released on 2007-12-27 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: Support vector machines (SVMs) are one of the most active research areas in machine learning. SVMs have shown good performance in a number of applications, including text and image classification. However, the learning capability of SVMs comes at a cost – an inherent inability to explain in a comprehensible form, the process by which a learning result was reached. Hence, the situation is similar to neural networks, where the apparent lack of an explanation capability has led to various approaches aiming at extracting symbolic rules from neural networks. For SVMs to gain a wider degree of acceptance in fields such as medical diagnosis and security sensitive areas, it is desirable to offer an explanation capability. User explanation is often a legal requirement, because it is necessary to explain how a decision was reached or why it was made. This book provides an overview of the field and introduces a number of different approaches to extracting rules from support vector machines developed by key researchers. In addition, successful applications are outlined and future research opportunities are discussed. The book is an important reference for researchers and graduate students, and since it provides an introduction to the topic, it will be important in the classroom as well. Because of the significance of both SVMs and user explanation, the book is of relevance to data mining practitioners and data analysts.
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 Managerial Perspectives on Intelligent Big Data Analytics written by Sun, Zhaohao and published by IGI Global. This book was released on 2019-02-22 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big data, analytics, and artificial intelligence are revolutionizing work, management, and lifestyles and are becoming disruptive technologies for healthcare, e-commerce, and web services. However, many fundamental, technological, and managerial issues for developing and applying intelligent big data analytics in these fields have yet to be addressed. Managerial Perspectives on Intelligent Big Data Analytics is a collection of innovative research that discusses the integration and application of artificial intelligence, business intelligence, digital transformation, and intelligent big data analytics from a perspective of computing, service, and management. While highlighting topics including e-commerce, machine learning, and fuzzy logic, this book is ideally designed for students, government officials, data scientists, managers, consultants, analysts, IT specialists, academicians, researchers, and industry professionals in fields that include big data, artificial intelligence, computing, and commerce.
Download or read book Predicting Corporate Credit Ratings Using Neural Network Models written by Simon James Frank and published by . This book was released on 2009 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Artificial Intelligence and Credit Risk written by Rossella Locatelli and published by Springer Nature. This book was released on 2022-09-13 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the alternative techniques and data leveraged for credit risk, describing and analysing the array of methodological approaches for the usage of techniques and/or alternative data for regulatory and managerial rating models. During the last decade the increase in computational capacity, the consolidation of new methodologies to elaborate data and the availability of new information related to individuals and organizations, aided by the widespread usage of internet, set the stage for the development and application of artificial intelligence techniques in enterprises in general and financial institutions in particular. In the banking world, its application is even more relevant, thanks to the use of larger and larger data sets for credit risk modelling. The evaluation of credit risk has largely been based on client data modelling; such techniques (linear regression, logistic regression, decision trees, etc.) and data sets (financial, behavioural, sociologic, geographic, sectoral, etc.) are referred to as “traditional” and have been the de facto standards in the banking industry. The incoming challenge for credit risk managers is now to find ways to leverage the new AI toolbox on new (unconventional) data to enhance the models’ predictive power, without neglecting problems due to results’ interpretability while recognizing ethical dilemmas. Contributors are university researchers, risk managers operating in banks and other financial intermediaries and consultants. The topic is a major one for the financial industry, and this is one of the first works offering relevant case studies alongside practical problems and solutions.
Download or read book Modelling Sovereign Credit Ratings written by and published by . This book was released on 2003 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Econometrics for Financial Applications written by Ly H. Anh and published by Springer. This book was released on 2017-12-18 with total page 1089 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses both theoretical developments in and practical applications of econometric techniques to finance-related problems. It includes selected edited outcomes of the International Econometric Conference of Vietnam (ECONVN2018), held at Banking University, Ho Chi Minh City, Vietnam on January 15-16, 2018. Econometrics is a branch of economics that uses mathematical (especially statistical) methods to analyze economic systems, to forecast economic and financial dynamics, and to develop strategies for achieving desirable economic performance. An extremely important part of economics is finances: a financial crisis can bring the whole economy to a standstill and, vice versa, a smart financial policy can dramatically boost economic development. It is therefore crucial to be able to apply mathematical techniques of econometrics to financial problems. Such applications are a growing field, with many interesting results – and an even larger number of challenges and open problems.
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 Risk Modeling written by Marriappan Vasudevan and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Credit risk assessment plays a major role in the banks and financial institutions to prevent counterparty risk failure. One of the primary capabilities of a robust risk management system must be detecting the risks earlier, though many of the bank systems today lack this key capability which leads to further losses (MGI, 2017). In searching for an improved methodology to detect such credit risk and increasing the lacking capabilities earlier, a comparative analysis between Deep Neural Network (DNN) and machine learning techniques such as Support Vector Machines (SVM), K-Nearest Neighbours (KNN) and Artificial Neural Network (ANN) were conducted. The Deep Neural Network used in this study consists of six layers of neurons. Further, sampling techniques such as SMOTE, SVM-SMOTE, RUS, and All-KNN to make the imbalanced dataset a balanced one were also applied. Using supervised learning techniques, the proposed DNN model was able to achieve an accuracy of 82.18% with a ROC score of 0.706 using the RUS sampling technique. The All KNN sampling technique was capable of achieving the maximum true positives in two different models. Using the proposed approach, banks and credit check institutions can help prevent major losses occurring due to counterparty risk failure.
Download or read book Credit Scoring and Its Applications Second Edition written by Lyn Thomas and published by SIAM. This book was released on 2017-08-16 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: Credit Scoring and Its Applications?is recognized as the bible of credit scoring. It contains a comprehensive review of the objectives, methods, and practical implementation of credit and behavioral scoring. The authors review principles of the statistical and operations research methods used in building scorecards, as well as the advantages and disadvantages of each approach. The book contains a description of practical problems encountered in building, using, and monitoring scorecards and examines some of the country-specific issues in bankruptcy, equal opportunities, and privacy legislation. It contains a discussion of economic theories of consumers' use of credit, and readers will gain an understanding of what lending institutions seek to achieve by using credit scoring and the changes in their objectives.? New to the second edition are lessons that can be learned for operations research model building from the global financial crisis, current applications of scoring, discussions on the Basel Accords and their requirements for scoring, new methods for scorecard building and new expanded sections on ways of measuring scorecard performance. And survival analysis for credit scoring. Other unique features include methods of monitoring scorecards and deciding when to update them, as well as different applications of scoring, including direct marketing, profit scoring, tax inspection, prisoner release, and payment of fines.?
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 Computer Systems that Learn written by Sholom M. Weiss and published by Morgan Kaufmann Publishers. This book was released on 1991 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text is a practical guide to classification learning systems and their applications, which learn from sample data and make predictions for new cases. The authors examine prominent methods from each area, using an engineering approach and taking the practitioner's point of view.
Download or read book Credit Risk Management written by Jiří Witzany and published by Springer. This book was released on 2017-02-24 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces to basic and advanced methods for credit risk management. It covers classical debt instruments and modern financial markets products. The author describes not only standard rating and scoring methods like Classification Trees or Logistic Regression, but also less known models that are subject of ongoing research, like e.g. Support Vector Machines, Neural Networks, or Fuzzy Inference Systems. The book also illustrates financial and commodity markets and analyzes the principles of advanced credit risk modeling techniques and credit derivatives pricing methods. Particular attention is given to the challenges of counterparty risk management, Credit Valuation Adjustment (CVA) and the related regulatory Basel III requirements. As a conclusion, the book provides the reader with all the essential aspects of classical and modern credit risk management and modeling.
Download or read book Artificial Intelligence and Credit Risk written by Yasmine Bensultana and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper contributes to literature in credit risk by reviewing standard firm-value-based models of contingent claims in a cross-sectional and time series setup and by comparing them to neural-network based machine learning (ML) models. First, we examine the Merton (1974) model with exogenous default and the Leland (1994) model with endogenous default and evaluate the extent to which these models can match observed credit default swap (CDS) spreads. We implement the models using a sample of 190 listed U.S. non-financial firms during a 10-year period from 2009 until 2018. We find that our empirical tests strongly reject the Merton (1974) and Leland (1994) model. These results are consistent with previous studies that find that structural credit risk models suffer from a spread underprediction problem, particularly with investment grade bonds. Second, we develop a neural network-based machine learning (ML) model, which applies the same input variables as the structural models. We find that the ML models strongly outperform both structural credit risk models and prove to match the observed CDS spreads remarkably well. Third, we extend our analysis and develop more sophisticated ML models by adding novel input parameters. Therefore, we add traditional financial ratios as quantitative input and a sentiment analysis consisting of 840 analyst reports as qualitative input data. While we find that the novel indicators do not significantly increase the predictive power of the ML models, they do increase their forecasting power over a three-year time horizon. We conclude that particularly over longer time-horizons, the neural networks seem to extract relevant information from additional input parameters, whose effect on credit risk is often neglected and not well understood in current credit risk applications.
Download or read book Modeling and Simulation in Engineering Economics and Management written by Kurt J. Engemann and published by Springer. This book was released on 2012-06-02 with total page 291 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains the refereed proceedings of the International Conference on Modeling and Simulation in Engineering, Economics, and Management, MS 2012, held in New Rochelle, NY, USA, in May/June 2012. The event was co-organized by the AMSE Association and Iona College. The 27 full papers in this book were carefully reviewed and selected from 78 submissions. In addition to these papers a summary of the plenary presentation given by Ronald R. Yager is also included. The book mainly focuses on the field of intelligent systems and its application to economics and business administration. Some papers have a stronger orientation towards modeling and simulation in these fields.
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.
