Practical And Advanced Machine Learning Methods For Model Risk Management
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Practical And Advanced Machine Learning Methods For Model Risk Management
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Author : INDRA REDDY MALLELA NAGARJUNA PUTTA PROF.(DR.) AVNEESH KUMAR
language : en
Publisher: DeepMisti Publication
Release Date : 2024-12-22
Practical And Advanced Machine Learning Methods For Model Risk Management written by INDRA REDDY MALLELA NAGARJUNA PUTTA PROF.(DR.) AVNEESH KUMAR and has been published by DeepMisti Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-22 with Computers categories.
In today’s fast-evolving landscape of artificial intelligence (AI) and machine learning (ML), organizations are increasingly relying on advanced models to drive decision-making and innovation across various sectors. As machine learning technologies grow in complexity and scale, managing the risks associated with these models becomes a critical concern. From biases in algorithms to the interpretability of predictions, the potential for errors and unintended consequences demands rigorous frameworks for assessing and mitigating risks. "Practical and Advanced Machine Learning Methods for Model Risk Management" explores these challenges in depth. It is designed to provide both foundational knowledge and advanced techniques for effectively managing model risks throughout their lifecycle—from development and deployment to monitoring and updating. This book caters to professionals working in data science, machine learning engineering, risk management, and governance, offering a comprehensive understanding of how to balance model performance with robust risk management practices. The book begins with a strong foundation in the principles of model risk management (MRM), exploring the core concepts of risk identification, assessment, and mitigation. From there, it dives into more advanced techniques for managing risks in complex ML models, including methods for ensuring model fairness, transparency, and interpretability, as well as strategies for dealing with adversarial attacks, data security concerns, and ethical considerations. Throughout, we emphasize the importance of collaboration between data scientists, risk professionals, and organizational leaders in creating a culture of responsible AI. This collaborative approach is crucial for building models that not only perform at the highest levels but also adhere to ethical standards and regulatory requirements. By the end of this book, readers will have a deep understanding of the critical role that risk management plays in AI and machine learning, as well as the practical tools and methods needed to implement a comprehensive MRM strategy. Whether you are just beginning your journey in model risk management or are seeking to refine your existing processes, this book serves as an essential resource for navigating the complexities of machine learning in today’s rapidly changing technological landscape. We hope this book equips you with the knowledge to effectively address the risks of ML models and apply these insights to improve both the performance and trustworthiness of your AI systems. Thank you for embarking on this journey with us. Authors
Disrupting Finance
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Author : Theo Lynn
language : en
Publisher: Palgrave Pivot
Release Date : 2018-12-19
Disrupting Finance written by Theo Lynn and has been published by Palgrave Pivot this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-19 with Business & Economics categories.
This open access Pivot demonstrates how a variety of technologies act as innovation catalysts within the banking and financial services sector. Traditional banks and financial services are under increasing competition from global IT companies such as Google, Apple, Amazon and PayPal whilst facing pressure from investors to reduce costs, increase agility and improve customer retention. Technologies such as blockchain, cloud computing, mobile technologies, big data analytics and social media therefore have perhaps more potential in this industry and area of business than any other. This book defines a fintech ecosystem for the 21st century, providing a state-of-the art review of current literature, suggesting avenues for new research and offering perspectives from business, technology and industry.
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.
Machine Learning For Financial Risk Management With Python
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Author : Abdullah Karasan
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-12-07
Machine Learning For Financial Risk Management With Python written by Abdullah Karasan and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-07 with Computers categories.
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
Practical Methods Of Financial Engineering And Risk Management
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Author : Rupak Chatterjee
language : en
Publisher: Apress
Release Date : 2014-09-26
Practical Methods Of Financial Engineering And Risk Management written by Rupak Chatterjee and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-26 with Business & Economics categories.
Risk control, capital allocation, and realistic derivative pricing and hedging are critical concerns for major financial institutions and individual traders alike. Events from the collapse of Lehman Brothers to the Greek sovereign debt crisis demonstrate the urgent and abiding need for statistical tools adequate to measure and anticipate the amplitude of potential swings in the financial markets—from ordinary stock price and interest rate moves, to defaults, to those increasingly frequent "rare events" fashionably called black swan events. Yet many on Wall Street continue to rely on standard models based on artificially simplified assumptions that can lead to systematic (and sometimes catastrophic) underestimation of real risks. In Practical Methods of Financial Engineering and Risk Management, Dr. Rupak Chatterjee— former director of the multi-asset quantitative research group at Citi—introduces finance professionals and advanced students to the latest concepts, tools, valuation techniques, and analytic measures being deployed by the more discerning and responsive Wall Street practitioners, on all operational scales from day trading to institutional strategy, to model and analyze more faithfully the real behavior and risk exposure of financial markets in the cold light of the post-2008 realities. Until one masters this modern skill set, one cannot allocate risk capital properly, price and hedge derivative securities realistically, or risk-manage positions from the multiple perspectives of market risk, credit risk, counterparty risk, and systemic risk. The book assumes a working knowledge of calculus, statistics, and Excel, but it teaches techniques from statistical analysis, probability, and stochastic processes sufficient to enable the reader to calibrate probability distributions and create the simulations that are used on Wall Street to valuate various financial instruments correctly, model the risk dimensions of trading strategies, and perform the numerically intensive analysis of risk measures required by various regulatory agencies.
Liquidity Dynamics And Risk Modeling
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Author : Mazin A. M. Al Janabi
language : en
Publisher: Springer Nature
Release Date : 2024-12-09
Liquidity Dynamics And Risk Modeling written by Mazin A. M. Al Janabi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-09 with Business & Economics categories.
This book presents a high-quality contribution to the applications of modern financial algorithms for liquidity risk management and its practical uses and applications to investable portfolios and mutual funds. It brings together the latest thinking on the emerging topic of contemporary liquidity risk estimations and management and includes principles, reviews, examples, and concrete financial markets applications to trading and investment portfolios. Furthermore, it explores research directions of liquidity risk management using modified Liquidity-Adjusted Value-at-Risk (L-VaR) models with the application of machine learning optimization algorithms. The book presents specific self-contained use-cases throughout, showing practical applications of the concepts discussed and providing further directions for researchers and financial markets participants. The book draws practical insights from personal experiences and applies specific examples (with the use of real-world case studies and analysis) about how the modeling techniques and machine learning optimization algorithms could address specific theoretical and practical issues of liquidity risk management and coherent asset allocation in trading and investment portfolios. It will be of interest to researchers, students, and practitioners of risk management, portfolio management, and machine learning.
Application Of Big Data Deep Learning Machine Learning And Other Advanced Analytical Techniques In Environmental Economics And Policy
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Author : Tsun Se Cheong
language : en
Publisher: Frontiers Media SA
Release Date : 2022-07-25
Application Of Big Data Deep Learning Machine Learning And Other Advanced Analytical Techniques In Environmental Economics And Policy written by Tsun Se Cheong and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-25 with Technology & Engineering categories.
Machine Learning For Risk Calculations
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Author : Ignacio Ruiz
language : en
Publisher: John Wiley & Sons
Release Date : 2021-12-28
Machine Learning For Risk Calculations written by Ignacio Ruiz 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 2021-12-28 with Business & Economics categories.
State-of-the-art algorithmic deep learning and tensoring techniques for financial institutions The computational demand of risk calculations in financial institutions has ballooned and shows no sign of stopping. It is no longer viable to simply add more computing power to deal with this increased demand. The solution? Algorithmic solutions based on deep learning and Chebyshev tensors represent a practical way to reduce costs while simultaneously increasing risk calculation capabilities. Machine Learning for Risk Calculations: A Practitioner’s View provides an in-depth review of a number of algorithmic solutions and demonstrates how they can be used to overcome the massive computational burden of risk calculations in financial institutions. This book will get you started by reviewing fundamental techniques, including deep learning and Chebyshev tensors. You’ll then discover algorithmic tools that, in combination with the fundamentals, deliver actual solutions to the real problems financial institutions encounter on a regular basis. Numerical tests and examples demonstrate how these solutions can be applied to practical problems, including XVA and Counterparty Credit Risk, IMM capital, PFE, VaR, FRTB, Dynamic Initial Margin, pricing function calibration, volatility surface parametrisation, portfolio optimisation and others. Finally, you’ll uncover the benefits these techniques provide, the practicalities of implementing them, and the software which can be used. Review the fundamentals of deep learning and Chebyshev tensors Discover pioneering algorithmic techniques that can create new opportunities in complex risk calculation Learn how to apply the solutions to a wide range of real-life risk calculations. Download sample code used in the book, so you can follow along and experiment with your own calculations Realize improved risk management whilst overcoming the burden of limited computational power Quants, IT professionals, and financial risk managers will benefit from this practitioner-oriented approach to state-of-the-art risk calculation.
Deep Learning Foundations And Advancements
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Author : Dr. Gali Nageswara Rao
language : en
Publisher: RK Publication
Release Date : 2024-10-01
Deep Learning Foundations And Advancements written by Dr. Gali Nageswara Rao and has been published by RK Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-01 with Computers categories.
Deep Learning: Foundations and Advancements a comprehensive exploration of the core principles and cutting-edge developments in deep learning. This foundational topics such as neural networks, optimization techniques, and learning algorithms, while also delving into advanced applications and research, including reinforcement learning, generative models, and deep neural architectures. With a focus on both theory and practical implementation, it offers readers a solid understanding of how deep learning is transforming industries like computer vision, natural language processing, and autonomous systems.
Progressive Computational Intelligence Information Technology And Networking
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Author : Poonam Nandal
language : en
Publisher: CRC Press
Release Date : 2025-07-22
Progressive Computational Intelligence Information Technology And Networking written by Poonam Nandal and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-22 with Computers categories.
Progressive Computational Intelligence, Information Technology and Networking presents a rich and diverse collection of cutting-edge research, real-world applications, and innovative methodologies spanning across multiple domains of computer science, artificial intelligence, and emerging technologies. This comprehensive volume brings together different scholarly chapters contributed by researchers, practitioners, and thought leaders from around the globe. The book explores a wide array of topics including—but not limited to—machine learning, deep learning, cloud computing, cybersecurity, Internet of Things (IoT), blockchain, natural language processing, image processing, and data analytics. It addresses the practical implementation of technologies in sectors such as healthcare, agriculture, education, smart cities, environmental monitoring, finance, and more. Each chapter delves into specific challenges, frameworks, and experimental outcomes, making this book an essential reference for academicians, researchers, industry professionals, and students who aim to stay ahead in the rapidly evolving digital world.