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Author : Piero Lorenzini
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)
Download or read book “An” Application of Deep Generative Models in Credit Risk Modelling written by Piero Lorenzini and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Terisa Roberts
Publisher : John Wiley & Sons
ISBN 13 : 1119824931
Total Pages : 214 pages
Book Rating : 4.1/5 (198 download)
Download or read book Risk Modeling written by Terisa Roberts and published by John Wiley & Sons. This book was released on 2022-09-27 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: A wide-ranging overview of the use of machine learning and AI techniques in financial risk management, including practical advice for implementation Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning introduces readers to the use of innovative AI technologies for forecasting and evaluating financial risks. Providing up-to-date coverage of the practical application of current modelling techniques in risk management, this real-world guide also explores new opportunities and challenges associated with implementing machine learning and artificial intelligence (AI) into the risk management process. Authors Terisa Roberts and Stephen Tonna provide readers with a clear understanding about the strengths and weaknesses of machine learning and AI while explaining how they can be applied to both everyday risk management problems and to evaluate the financial impact of extreme events such as global pandemics and changes in climate. Throughout the text, the authors clarify misconceptions about the use of machine learning and AI techniques using clear explanations while offering step-by-step advice for implementing the technologies into an organization’s risk management model governance framework. This authoritative volume: Highlights the use of machine learning and AI in identifying procedures for avoiding or minimizing financial risk Discusses practical tools for assessing bias and interpretability of resultant models developed with machine learning algorithms and techniques Covers the basic principles and nuances of feature engineering and common machine learning algorithms Illustrates how risk modeling is incorporating machine learning and AI techniques to rapidly consume complex data and address current gaps in the end-to-end modelling lifecycle Explains how proprietary software and open-source languages can be combined to deliver the best of both worlds: for risk models and risk practitioners Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning is an invaluable guide for CEOs, CROs, CFOs, risk managers, business managers, and other professionals working in risk management.
Author : Elizabeth Mays
Publisher : Global Professional Publishi
ISBN 13 : 9781888998382
Total Pages : 280 pages
Book Rating : 4.9/5 (983 download)
Download or read book Credit Risk Modeling written by Elizabeth Mays and published by Global Professional Publishi. This book was released on 1998-12-10 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: Covers: � Implementing an application scoring system � Behavior modeling to manage your portfolio � Incorporating economic factors � Statistical techniques for choosing the optimal credit risk model � How to set cutoffs and override rules � Modeling for the sub-prime market � How to evaluate and monitor credit risk models This is an indispensable guide for credit professionals and risk managers who want to understand and implement modeling techniques for increased profitability. In this one-of-a-kind text, experts in credit risk provide a step-by-step guide to building and implementing models both for evaluating applications and managing existing portfolios.
Author : Harald Scheule
Publisher : Deep Credit Risk
ISBN 13 : 9780645245202
Total Pages : 456 pages
Book Rating : 4.2/5 (452 download)
Download or read book Deep Credit Risk (Chinese) written by Harald Scheule and published by Deep Credit Risk. This book was released on 2021-07-22 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: - 了解流动性，房屋净值和许多其他关键银行业特征变量的作用； - 选择并处理变量； - 预测违约、偿付、损失率和风险敞口； - 利用危机前特征预测经济衰退和危机后果； - 理解COVID-19对信用风险带来的影响； - 将创新的抽样技术应用于模型训练和验证； - 从Logit分类器到随机森林和神经网络的深入学习; - 进行无监督聚类、主成分和贝叶斯技术的应用； - 为CECL、IFRS 9和CCAR建立多周期模型； - 建立用于在险价值和期望损失的信贷组合相关模型； - 使用更多真实的信用风险数据并运行超过1500行的代码... - Understand the role of liquidity, equity and many other key banking features - Engineer and select features - Predict defaults, payoffs, loss rates and exposures - Predict downturn and crisis outcomes using pre-crisis features - Understand the implications of COVID-19 - Apply innovative sampling techniques for model training and validation - Deep-learn from Logit Classifiers to Random Forests and Neural Networks - Do unsupervised Clustering, Principal Components and Bayesian Techniques - Build multi-period models for CECL, IFRS 9 and CCAR - Build credit portfolio correlation models for VaR and Expected Shortfal - Run over 1,500 lines of pandas, statsmodels and scikit-learn Python code - Access real credit data and much more ...
Author : Abdullah Karasan
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492085200
Total Pages : 334 pages
Book Rating : 4.4/5 (92 download)
Download or read book Machine Learning for Financial Risk Management with Python written by Abdullah Karasan and published by "O'Reilly Media, Inc.". This book was released on 2021-12-07 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, risk analysts, and quantitative and algorithmic analysts will examine Python-based machine learning and deep learning models for assessing financial risk. Building hands-on AI-based financial modeling skills, you'll learn how to replace traditional financial risk models with ML models. Author Abdullah Karasan helps you explore the theory behind financial risk modeling before diving into practical ways of employing ML models in modeling financial risk using Python. With this book, you will: Review classical time series applications and compare them with deep learning models Explore volatility modeling to measure degrees of risk, using support vector regression, neural networks, and deep learning Improve market risk models (VaR and ES) using ML techniques and including liquidity dimension Develop a credit risk analysis using clustering and Bayesian approaches Capture different aspects of liquidity risk with a Gaussian mixture model and Copula model Use machine learning models for fraud detection Predict stock price crash and identify its determinants using machine learning models
Author : Stefan Trueck
Publisher : Academic Press
ISBN 13 : 0080920306
Total Pages : 279 pages
Book Rating : 4.0/5 (89 download)
Download or read book Rating Based Modeling of Credit Risk written by Stefan Trueck and published by Academic Press. This book was released on 2009-01-15 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the last decade rating-based models have become very popular in credit risk management. These systems use the rating of a company as the decisive variable to evaluate the default risk of a bond or loan. The popularity is due to the straightforwardness of the approach, and to the upcoming new capital accord (Basel II), which allows banks to base their capital requirements on internal as well as external rating systems. Because of this, sophisticated credit risk models are being developed or demanded by banks to assess the risk of their credit portfolio better by recognizing the different underlying sources of risk. As a consequence, not only default probabilities for certain rating categories but also the probabilities of moving from one rating state to another are important issues in such models for risk management and pricing. It is widely accepted that rating migrations and default probabilities show significant variations through time due to macroeconomics conditions or the business cycle. These changes in migration behavior may have a substantial impact on the value-at-risk (VAR) of a credit portfolio or the prices of credit derivatives such as collateralized debt obligations (D+CDOs). In Rating Based Modeling of Credit Risk the authors develop a much more sophisticated analysis of migration behavior. Their contribution of more sophisticated techniques to measure and forecast changes in migration behavior as well as determining adequate estimators for transition matrices is a major contribution to rating based credit modeling. Internal ratings-based systems are widely used in banks to calculate their value-at-risk (VAR) in order to determine their capital requirements for loan and bond portfolios under Basel II One aspect of these ratings systems is credit migrations, addressed in a systematic and comprehensive way for the first time in this book The book is based on in-depth work by Trueck and Rachev
Author : Yasmine Bensultana
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (114 download)
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.
Author : Bohdan Popovych
Publisher : Springer Nature
ISBN 13 : 365840180X
Total Pages : 93 pages
Book Rating : 4.6/5 (584 download)
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.
Author : David Lando
Publisher : Princeton University Press
ISBN 13 : 1400829194
Total Pages : 328 pages
Book Rating : 4.4/5 (8 download)
Download or read book Credit Risk Modeling written by David Lando and published by Princeton University Press. This book was released on 2009-12-13 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Credit risk is today one of the most intensely studied topics in quantitative finance. This book provides an introduction and overview for readers who seek an up-to-date reference to the central problems of the field and to the tools currently used to analyze them. The book is aimed at researchers and students in finance, at quantitative analysts in banks and other financial institutions, and at regulators interested in the modeling aspects of credit risk. David Lando considers the two broad approaches to credit risk analysis: that based on classical option pricing models on the one hand, and on a direct modeling of the default probability of issuers on the other. He offers insights that can be drawn from each approach and demonstrates that the distinction between the two approaches is not at all clear-cut. The book strikes a fruitful balance between quickly presenting the basic ideas of the models and offering enough detail so readers can derive and implement the models themselves. The discussion of the models and their limitations and five technical appendixes help readers expand and generalize the models themselves or to understand existing generalizations. The book emphasizes models for pricing as well as statistical techniques for estimating their parameters. Applications include rating-based modeling, modeling of dependent defaults, swap- and corporate-yield curve dynamics, credit default swaps, and collateralized debt obligations.
Author : Christian Bluhm
Publisher : CRC Press
ISBN 13 : 1584889934
Total Pages : 386 pages
Book Rating : 4.5/5 (848 download)
Download or read book Introduction to Credit Risk Modeling written by Christian Bluhm and published by CRC Press. This book was released on 2016-04-19 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: Contains Nearly 100 Pages of New MaterialThe recent financial crisis has shown that credit risk in particular and finance in general remain important fields for the application of mathematical concepts to real-life situations. While continuing to focus on common mathematical approaches to model credit portfolios, Introduction to Credit Risk Modelin
Author : Rossella Locatelli
Publisher : Springer Nature
ISBN 13 : 3031102363
Total Pages : 115 pages
Book Rating : 4.0/5 (311 download)
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.
Author : Zhiyong Zheng
Publisher : Springer Nature
ISBN 13 : 9811583730
Total Pages : 238 pages
Book Rating : 4.8/5 (115 download)
Download or read book Proceedings of the First International Forum on Financial Mathematics and Financial Technology written by Zhiyong Zheng and published by Springer Nature. This book was released on 2021-02-08 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains high-quality papers presented at the First International Forum on Financial Mathematics and Financial Technology. With the rapid development of FinTech, the in-depth integration between mathematics, finance and advanced technology is the general trend. This book focuses on selected aspects of the current and upcoming trends in FinTech. In detail, the included scientific papers focus on financial mathematics and FinTech, presenting the innovative mathematical models and state-of-the-art technologies such as deep learning, with the aim to improve our financial analysis and decision-making and enhance the quality of financial services and risk control. The variety of the papers delivers added value for both scholars and practitioners where they will find perfect integration of elegant mathematical models and up-to-date data mining technologies in financial market analysis.
Author : Raymond A. Anderson
Publisher : Oxford University Press
ISBN 13 : 0192844199
Total Pages : 934 pages
Book Rating : 4.1/5 (928 download)
Download or read book Credit Intelligence & Modelling written by Raymond A. Anderson and published by Oxford University Press. This book was released on 2022 with total page 934 pages. Available in PDF, EPUB and Kindle. Book excerpt: Credit Intelligence and Modelling provides an indispensable explanation of the statistical models and methods used when assessing credit risk and automating decisions. Over eight modules, the book covers consumer and business lending in both the developed and developing worlds, providing the frameworks for both theory and practice. It first explores an introduction to credit risk assessment and predictive modelling, micro-histories of credit and credit scoring, as well as the processes used throughout the credit risk management cycle. Mathematical and statistical tools used to develop and assess predictive models are then considered, in addition to project management and data assembly, data preparation from sampling to reject inference, and finally model training through to implementation. Although the focus is credit risk, especially in the retail consumer and small-business segments, many concepts are common across disciplines, whether for academic research or practical use. The book assumes little prior knowledge, thus making it an indispensable desktop reference for students and practitioners alike. Credit Intelligence and Modelling expands on the success of The Credit Scoring Toolkit to cover credit rating and intelligence agencies, and the data and tools used as part of the process.
Author : Colin Chen
Publisher : Springer Nature
ISBN 13 : 3031525426
Total Pages : 404 pages
Book Rating : 4.0/5 (315 download)
Download or read book Practical Credit Risk and Capital Modeling, and Validation written by Colin Chen and published by Springer Nature. This book was released on with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : David Jamieson Bolder
Publisher : Springer
ISBN 13 : 3319946889
Total Pages : 704 pages
Book Rating : 4.3/5 (199 download)
Download or read book Credit-Risk Modelling written by David Jamieson Bolder and published by Springer. This book was released on 2018-10-31 with total page 704 pages. Available in PDF, EPUB and Kindle. Book excerpt: The risk of counterparty default in banking, insurance, institutional, and pension-fund portfolios is an area of ongoing and increasing importance for finance practitioners. It is, unfortunately, a topic with a high degree of technical complexity. Addressing this challenge, this book provides a comprehensive and attainable mathematical and statistical discussion of a broad range of existing default-risk models. Model description and derivation, however, is only part of the story. Through use of exhaustive practical examples and extensive code illustrations in the Python programming language, this work also explicitly shows the reader how these models are implemented. Bringing these complex approaches to life by combining the technical details with actual real-life Python code reduces the burden of model complexity and enhances accessibility to this decidedly specialized field of study. The entire work is also liberally supplemented with model-diagnostic, calibration, and parameter-estimation techniques to assist the quantitative analyst in day-to-day implementation as well as in mitigating model risk. Written by an active and experienced practitioner, it is an invaluable learning resource and reference text for financial-risk practitioners and an excellent source for advanced undergraduate and graduate students seeking to acquire knowledge of the key elements of this discipline.
Author : Leonard Barolli
Publisher : Springer Nature
ISBN 13 : 3030577961
Total Pages : 517 pages
Book Rating : 4.0/5 (35 download)
Download or read book Advances in Intelligent Networking and Collaborative Systems written by Leonard Barolli and published by Springer Nature. This book was released on 2020-08-20 with total page 517 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to provide the latest research findings, innovative research results, methods and development techniques from both theoretical and practical perspectives related to intelligent social networks and collaborative systems, intelligent networking systems, mobile collaborative systems, secure intelligent cloud systems, etc., as well as to reveal synergies among various paradigms in such a multi-disciplinary field intelligent collaborative systems. With the fast development of the Internet, we are experiencing a shift from the traditional sharing of information and applications as the main purpose of the Web to an emergent paradigm, which locates people at the very centre of networks and exploits the value of people's connections, relations and collaboration. Social networks are also playing a major role in the dynamics and structure of intelligent Web-based networking and collaborative systems. Virtual campuses, virtual communities and organizations strongly leverage intelligent networking and collaborative systems by a great variety of formal and informal electronic relations, such as business-to-business, peer-to-peer and many types of online collaborative learning interactions, including the emerging e-learning systems. This has resulted in entangled systems that need to be managed efficiently and in an autonomous way. In addition, latest and powerful technologies based on grid and wireless infrastructure as well as cloud computing are currently enhancing collaborative and networking applications as a great deal but also facing new issues and challenges. The principal purpose of the research and development community is to stimulate research that will lead to the creation of responsive environments for networking and, at longer-term, the development of adaptive, secure, mobile and intuitive intelligent systems for collaborative work and learning.
Author : Tomasz R. Bielecki
Publisher : Springer Science & Business Media
ISBN 13 : 3662048213
Total Pages : 517 pages
Book Rating : 4.6/5 (62 download)
Download or read book Credit Risk: Modeling, Valuation and Hedging written by Tomasz R. Bielecki and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 517 pages. Available in PDF, EPUB and Kindle. Book excerpt: The motivation for the mathematical modeling studied in this text on developments in credit risk research is the bridging of the gap between mathematical theory of credit risk and the financial practice. Mathematical developments are covered thoroughly and give the structural and reduced-form approaches to credit risk modeling. Included is a detailed study of various arbitrage-free models of default term structures with several rating grades.
