Statistical And Machine Learning For Credit Risk Parameter Modeling
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Statistical And Machine Learning For Credit Risk Parameter Modeling
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Author : Marvin Zöllner
language : en
Publisher: Cuvillier Verlag
Release Date : 2023-10-19
Statistical And Machine Learning For Credit Risk Parameter Modeling written by Marvin Zöllner and has been published by Cuvillier Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-19 with categories.
Die Dissertation befasst sich mit der Anwendung von statistischem und maschinellem Lernen zur Modellierung der Verlustquote bei Ausfall (LGD). Im Forschungsgebiet der LGD-Modellierung gibt es eine Reihe von Fragen und Problemen, die bisher in der Literatur nicht berücksichtigt wurden. Erstens ist unklar, welche Merkmale einer LGD-Verteilung für die Prognosefähigkeit von Schätzmethoden entscheidend sind und welche Schätzmethode für die LGD-Modellierung am besten geeignet ist. Zweitens besteht ein Zielkonflikt zwischen der Transparenz und der Prognosegenauigkeit bei LGD-Schätzmethoden. Komplexe maschinelle Lernalgorithmen weisen eine bessere Vorhersageleistung auf, allerdings auf Kosten einer geringeren Erklärbarkeit. Umgekehrt bietet die lineare Regression eine hohe Interpretierbarkeit, scheint aber eine geringere Prognosegenauigkeit aufzuweisen. Um diesen Zielkonflikt zu lösen, besteht ein geeigneter Ansatz darin, die Vorhersagegenauigkeit der interpretierbaren linearen Regression durch maschinelles Lernen zu verbessern. Drittens stellt die Selektion optimaler Clustervariablen in der gruppierten Modellierung eine zu lösende Herausforderung dar. Die offenen Forschungsfragen werden in der Dissertation anhand von Kreditausfalldaten der Global Credit Data empirisch beantwortet.
Statistical And Machine Learning For Credit Risk Parameter Modeling
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Author : Marvin Zöllner
language : en
Publisher:
Release Date : 2023
Statistical And Machine Learning For Credit Risk Parameter Modeling written by Marvin Zöllner and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
Interpretable Machine Learning
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Author : Christoph Molnar
language : en
Publisher: Lulu.com
Release Date : 2020
Interpretable Machine Learning written by Christoph Molnar and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Computers categories.
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Credit Risk Analytics
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Author : Bart Baesens
language : en
Publisher: John Wiley & Sons
Release Date : 2016-10-03
Credit Risk Analytics written by Bart Baesens and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-03 with Business & Economics categories.
The long-awaited, comprehensive guide to practical credit risk modeling Credit Risk Analytics provides a targeted training guide for risk managers looking to efficiently build or validate in-house models for credit risk management. Combining theory with practice, this book walks you through the fundamentals of credit risk management and shows you how to implement these concepts using the SAS credit risk management program, with helpful code provided. Coverage includes data analysis and preprocessing, credit scoring; PD and LGD estimation and forecasting, low default portfolios, correlation modeling and estimation, validation, implementation of prudential regulation, stress testing of existing modeling concepts, and more, to provide a one-stop tutorial and reference for credit risk analytics. The companion website offers examples of both real and simulated credit portfolio data to help you more easily implement the concepts discussed, and the expert author team provides practical insight on this real-world intersection of finance, statistics, and analytics. SAS is the preferred software for credit risk modeling due to its functionality and ability to process large amounts of data. This book shows you how to exploit the capabilities of this high-powered package to create clean, accurate credit risk management models. Understand the general concepts of credit risk management Validate and stress-test existing models Access working examples based on both real and simulated data Learn useful code for implementing and validating models in SAS Despite the high demand for in-house models, there is little comprehensive training available; practitioners are left to comb through piece-meal resources, executive training courses, and consultancies to cobble together the information they need. This book ends the search by providing a comprehensive, focused resource backed by expert guidance. Credit Risk Analytics is the reference every risk manager needs to streamline the modeling process.
Handbook Of Financial Econometrics Mathematics Statistics And Machine Learning In 4 Volumes
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Author : Cheng Few Lee
language : en
Publisher: World Scientific
Release Date : 2020-07-30
Handbook Of Financial Econometrics Mathematics Statistics And Machine Learning In 4 Volumes written by Cheng Few Lee and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-30 with Business & Economics categories.
This four-volume handbook covers important concepts and tools used in the fields of financial econometrics, mathematics, statistics, and machine learning. Econometric methods have been applied in asset pricing, corporate finance, international finance, options and futures, risk management, and in stress testing for financial institutions. This handbook discusses a variety of econometric methods, including single equation multiple regression, simultaneous equation regression, and panel data analysis, among others. It also covers statistical distributions, such as the binomial and log normal distributions, in light of their applications to portfolio theory and asset management in addition to their use in research regarding options and futures contracts.In both theory and methodology, we need to rely upon mathematics, which includes linear algebra, geometry, differential equations, Stochastic differential equation (Ito calculus), optimization, constrained optimization, and others. These forms of mathematics have been used to derive capital market line, security market line (capital asset pricing model), option pricing model, portfolio analysis, and others.In recent times, an increased importance has been given to computer technology in financial research. Different computer languages and programming techniques are important tools for empirical research in finance. Hence, simulation, machine learning, big data, and financial payments are explored in this handbook.Led by Distinguished Professor Cheng Few Lee from Rutgers University, this multi-volume work integrates theoretical, methodological, and practical issues based on his years of academic and industry experience.
Credit Scoring And Its Applications
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Author : Lyn C. Thomas
language : en
Publisher: SIAM
Release Date : 2002-01-01
Credit Scoring And Its Applications written by Lyn C. Thomas and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-01-01 with Mathematics categories.
The only book that details the mathematical models that help creditors make intelligent credit risk decisions.
Credit Risk Modelling
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Author : David Jamieson Bolder
language : en
Publisher: Springer
Release Date : 2018-10-31
Credit Risk Modelling written by David Jamieson Bolder and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-31 with Business & Economics categories.
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.
An Introduction To Kolmogorov Complexity And Its Applications
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Author : Ming Li
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-09
An Introduction To Kolmogorov Complexity And Its Applications written by Ming Li and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-09 with Mathematics categories.
Briefly, we review the basic elements of computability theory and prob ability theory that are required. Finally, in order to place the subject in the appropriate historical and conceptual context we trace the main roots of Kolmogorov complexity. This way the stage is set for Chapters 2 and 3, where we introduce the notion of optimal effective descriptions of objects. The length of such a description (or the number of bits of information in it) is its Kolmogorov complexity. We treat all aspects of the elementary mathematical theory of Kolmogorov complexity. This body of knowledge may be called algo rithmic complexity theory. The theory of Martin-Lof tests for random ness of finite objects and infinite sequences is inextricably intertwined with the theory of Kolmogorov complexity and is completely treated. We also investigate the statistical properties of finite strings with high Kolmogorov complexity. Both of these topics are eminently useful in the applications part of the book. We also investigate the recursion theoretic properties of Kolmogorov complexity (relations with Godel's incompleteness result), and the Kolmogorov complexity version of infor mation theory, which we may call "algorithmic information theory" or "absolute information theory. " The treatment of algorithmic probability theory in Chapter 4 presup poses Sections 1. 6, 1. 11. 2, and Chapter 3 (at least Sections 3. 1 through 3. 4).
Advances In Information Communication And Cybersecurity
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Author : Yassine Maleh
language : en
Publisher: Springer Nature
Release Date : 2022-01-12
Advances In Information Communication And Cybersecurity written by Yassine Maleh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-12 with Technology & Engineering categories.
This book gathers the proceedings of the International Conference on Information, Communication and Cybersecurity, held on November 10–11, 2021, in Khouribga, Morocco. The conference was jointly coorganized by The National School of Applied Sciences of Sultan Moulay Slimane University, Morocco, and Charles Darwin University, Australia. This book provides an opportunity to account for state-of-the-art works, future trends impacting information technology, communications, and cybersecurity, focusing on elucidating the challenges, opportunities, and inter-dependencies that are just around the corner. This book is helpful for students and researchers as well as practitioners. ICI2C 2021 was devoted to advances in smart information technologies, communication, and cybersecurity. It was considered a meeting point for researchers and practitioners to implement advanced information technologies into various industries. There were 159 paper submissions from 24 countries. Each submission was reviewed by at least three chairs or PC members. We accepted 54 regular papers (34\%). Unfortunately, due to limitations of conference topics and edited volumes, the Program Committee was forced to reject some interesting papers, which did not satisfy these topics or publisher requirements. We would like to thank all authors and reviewers for their work and valuable contributions. The friendly and welcoming attitude of conference supporters and contributors made this event a success!
Revolutionizing Finance Leveraging Artificial Intelligence Machine Learning And Big Data For Smarter Credit Risk And Fraud Protection
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Author : Harish Kumar Sriram
language : en
Publisher: Deep Science Publishing
Release Date : 2025-04-26
Revolutionizing Finance Leveraging Artificial Intelligence Machine Learning And Big Data For Smarter Credit Risk And Fraud Protection written by Harish Kumar Sriram and has been published by Deep Science Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-26 with Business & Economics categories.
In today’s fast-paced digital economy, financial institutions are facing increasing pressure to make smarter, faster, and more secure decisions. As global markets grow more interconnected and cyber threats more sophisticated, traditional approaches to credit risk assessment and fraud prevention are no longer sufficient. Revolutionizing Finance: Leveraging AI, ML, and Big Data for Smarter Credit Risk and Fraud Protection presents a forward-looking perspective on how intelligent technologies are transforming the foundations of financial security and trust. This book is the product of years of research, industry observation, and a deep belief that innovation is the key to sustainable financial health. Artificial intelligence (AI), machine learning (ML), and big data analytics have evolved from buzzwords into essential tools for financial resilience. They offer the ability to detect patterns, predict risk, and prevent fraud in ways that were unimaginable just a decade ago. Our goal is to demystify these technologies and demonstrate how they can be applied to create more dynamic and accurate credit models, reduce false positives in fraud detection, and increase operational efficiency. By blending theory with real-world applications, we provide readers with both the foundational knowledge and practical insights needed to embrace and implement these transformative tools. This book is designed for financial professionals, data scientists, policymakers, and anyone with a vested interest in the future of finance. We aim to empower readers with the confidence to lead change, harness data intelligently, and build systems that are not only reactive but predictive and proactive. As we stand at the intersection of finance and technology, we invite you to explore the possibilities and challenges that lie ahead. The journey to revolutionized finance starts here — and it's powered by intelligence, innovation, and data.