Risk Analytics
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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.
Risk Analytics
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Author : Eduardo Rodriguez
language : en
Publisher: CRC Press
Release Date : 2023-08-08
Risk Analytics written by Eduardo Rodriguez and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-08 with Business & Economics categories.
The 2022 World Economic Forum surveyed 1,000 experts and leaders who indicated their risk perception that the earth’s conditions for humans are a main concern in the next 10 years. This means environmental risks are a priority to study in a formal way. At the same time, innovation risks are present in theminds of leaders, newknowledge brings new risk, and the adaptation and adoption of risk knowledge is required to better understand the causes and effects can have on technological risks. These opportunities require not only adopting new ways of managing and controlling emerging processes for society and business, but also adapting organizations to changes and managing new risks. Risk Analytics: Data-Driven Decisions Under Uncertainty introduces a way to analyze and design a risk analytics system (RAS) that integrates multiple approaches to risk analytics to deal with diverse types of data and problems. A risk analytics system is a hybrid system where human and artificial intelligence interact with a data gathering and selection process that uses multiple sources to the delivery of guidelines to make decisions that include humans and machines. The RAS system is an integration of components, such as data architecture with diverse data, and a risk analytics process and modeling process to obtain knowledge and then determine actions through the new knowledge that was obtained. The use of data analytics is not only connected to risk modeling and its implementation, but also to the development of the actionable knowledge that can be represented by text in documents to define and share explicit knowledge and guidelines in the organization for strategy implementation. This book moves from a review of data to the concepts of a RAS. It reviews RAS system components required to support the creation of competitive advantage in organizations through risk analytics. Written for executives, analytics professionals, risk management professionals, strategy professionals, and postgraduate students, this book shows a way to implement the analytics process to develop a risk management practice that creates an adaptive competitive advantage under uncertainty.
Risk Analysis
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Author : David Vose
language : en
Publisher: John Wiley & Sons
Release Date : 2008-04-28
Risk Analysis written by David Vose 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 2008-04-28 with Business & Economics categories.
This book concerns itself with the quantification of risk, the modeling of identified risks and how to make decisions from those models. Quantitative risk analysis (QRA) using Monte Carlo simulation offers a powerful and precise method for dealing with the uncertainty and variability of a problem. By providing the building blocks the author guides the reader through the necessary steps to produce an accurate risk analysis model and offers general and specific techniques to cope with most modeling problems. A wide range of solved problems is used to illustrate these techniques and how they can be used together to solve otherwise complex problems.
Causal Analytics For Applied Risk Analysis
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Author : Louis Anthony Cox Jr.
language : en
Publisher: Springer
Release Date : 2018-06-19
Causal Analytics For Applied Risk Analysis written by Louis Anthony Cox Jr. and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-19 with Business & Economics categories.
Causal analytics methods can revolutionize the use of data to make effective decisions by revealing how different choices affect probabilities of various outcomes. This book presents and illustrates models, algorithms, principles, and software for deriving causal models from data and for using them to optimize decisions with uncertain outcomes. It discusses how to describe and summarize situations; detect changes; evaluate effects of policies or interventions; learn what works best under different conditions; predict values of as-yet unobserved quantities from available data; and identify the most likely explanations for observed outcomes, including surprises and anomalies. The book resents practical techniques for causal modeling and analytics that practitioners can apply to improve understanding of how choices affect probabilities of consequences and, based on this understanding, to recommend choices that are more likely to accomplish their intended objectives.The book begins with a survey of modern analytics methods, focusing mainly on techniques useful for decision, risk, and policy analysis. Chapter 2 introduces free in-browser software, including the Causal Analytics Toolkit (CAT) software, to enable readers to perform the analyses described and to apply modern analytics methods easily to their own data sets. Chapters 3 through 11 show how to apply causal analytics and risk analytics to practical risk analysis challenges, mainly related to public and occupational health risks from pathogens in food or from pollutants in air. Chapters 12 through 15 turn to broader questions of how to improve risk management decision-making by individuals, groups, organizations, institutions, and multi-generation societies with different cultures and norms for cooperation. These chapters examine organizational learning, community resilience, societal risk management, and intergenerational collaboration and justice in managing risks.
Risk Analytics From Concept To Deployment
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Author : Edward Hon Khay Ng
language : en
Publisher: World Scientific
Release Date : 2021-10-04
Risk Analytics From Concept To Deployment written by Edward Hon Khay Ng and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-04 with Business & Economics categories.
This book is written to empower risk professionals to turn analytics and models into deployable solutions with minimal IT intervention. Corporations, especially financial institutions, must show evidence of having quantified credit, market and operational risks. They have databases but automating the process to translate data into risk parameters remains a desire.Modelling is done using software with output codes not readily processed by databases. With increasing acceptance of open-source languages, database vendors have seen the value of integrating modelling capabilities into their products. Nevertheless, deploying solutions to automate processes remains a challenge. While not comprehensive in dealing with all facets of risks, the author aims to develop risk professionals who will be able to do just that.
Data Science And Risk Analytics In Finance And Insurance
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Author : Tze Leung Lai
language : en
Publisher: CRC Press
Release Date : 2024-10-02
Data Science And Risk Analytics In Finance And Insurance written by Tze Leung Lai and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-02 with Business & Economics categories.
This book presents statistics and data science methods for risk analytics in quantitative finance and insurance. Part I covers the background, financial models, and data analytical methods for market risk, credit risk, and operational risk in financial instruments, as well as models of risk premium and insolvency in insurance contracts. Part II provides an overview of machine learning (including supervised, unsupervised, and reinforcement learning), Monte Carlo simulation, and sequential analysis techniques for risk analytics. In Part III, the book offers a non-technical introduction to four key areas in financial technology: artificial intelligence, blockchain, cloud computing, and big data analytics. Key Features: Provides a comprehensive and in-depth overview of data science methods for financial and insurance risks. Unravels bandits, Markov decision processes, reinforcement learning, and their interconnections. Promotes sequential surveillance and predictive analytics for abrupt changes in risk factors. Introduces the ABCDs of FinTech: Artificial intelligence, blockchain, cloud computing, and big data analytics. Includes supplements and exercises to facilitate deeper comprehension.
Towards Supply Chain Risk Analytics
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Author : Iris Heckmann
language : en
Publisher: Springer
Release Date : 2016-07-20
Towards Supply Chain Risk Analytics written by Iris Heckmann and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-07-20 with Business & Economics categories.
In this thesis, Iris Heckmann develops a profound conceptual basis of supply chain risk analytics. She transfers the newly defined concepts for the modelling and operationalization of supply chain risk within simulation and optimization approaches, in order to ease unexpected deviations and disruptions, which are subsumed under the notion of supply chain risk, increasingly aggravating the planning and optimization of supply chains.
Data Science And Risk Analytics In Finance And Insurance
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Author : Tze Leung Lai
language : en
Publisher: CRC Press
Release Date : 2024-10-02
Data Science And Risk Analytics In Finance And Insurance written by Tze Leung Lai and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-02 with Business & Economics categories.
This book presents statistics and data science methods for risk analytics in quantitative finance and insurance. Part I covers the background, financial models, and data analytical methods for market risk, credit risk, and operational risk in financial instruments, as well as models of risk premium and insolvency in insurance contracts. Part II provides an overview of machine learning (including supervised, unsupervised, and reinforcement learning), Monte Carlo simulation, and sequential analysis techniques for risk analytics. In Part III, the book offers a non-technical introduction to four key areas in financial technology: artificial intelligence, blockchain, cloud computing, and big data analytics. Key Features: Provides a comprehensive and in-depth overview of data science methods for financial and insurance risks. Unravels bandits, Markov decision processes, reinforcement learning, and their interconnections. Promotes sequential surveillance and predictive analytics for abrupt changes in risk factors. Introduces the ABCDs of FinTech: Artificial intelligence, blockchain, cloud computing, and big data analytics. Includes supplements and exercises to facilitate deeper comprehension.
Financial And Credit Risk Analytics
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Author : Raj Kumar Singh
language : en
Publisher: Thakur Publication Private Limited
Release Date : 2025-06-19
Financial And Credit Risk Analytics written by Raj Kumar Singh and has been published by Thakur Publication Private Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-19 with Business & Economics categories.
MBA, FOURTH SEMESTER According to the New Syllabus of ‘Dr. A.P.J. Abdul Kalam Technical University’ Lucknow
Enterprise Risk Analytics For Capital Markets
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Author : Raghurami Reddy Etukuru
language : en
Publisher: iUniverse
Release Date : 2014-10-09
Enterprise Risk Analytics For Capital Markets written by Raghurami Reddy Etukuru and has been published by iUniverse this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-10-09 with Business & Economics categories.
While quantitative models can help predict the trends in Capital Markets, forecasts dont always hold up and can quickly cause things to spiral out of control and can lead to global risk. In order to reduce systemic risk, the G20 committed to a fundamental reform of the financial system, to correct the fault lines, and to rebuild the financial system as a safer, more resilient source of finance that better serves the real economy. This requires Financial Institutions to develop sound Risk Management practices. In straightforward language, youll learn about key components of risk management, including risk knowledge, risk quantification, risk data management, risk data aggregation, risk architectures, risk analytics and reporting, risk regulation. Youll also get definitions explaining how different financial products work, mathematical formulas with explanations, and insights on different asset classes, different approaches to hedging, and much more. This book Enterprise Risk Analytics for Capital Markets will help whether you are just beginning a career in risk management or advancing your career with in risk management.