Machine Learning In Risk Measurement

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Machine Learning Risk Assessments in Criminal Justice Settings

Author : Richard Berk
Publisher : Springer
ISBN 13 : 3030022722
Total Pages : 178 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Machine Learning Risk Assessments in Criminal Justice Settings by : Richard Berk

Download or read book Machine Learning Risk Assessments in Criminal Justice Settings written by Richard Berk and published by Springer. This book was released on 2018-12-13 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book puts in one place and in accessible form Richard Berk’s most recent work on forecasts of re-offending by individuals already in criminal justice custody. Using machine learning statistical procedures trained on very large datasets, an explicit introduction of the relative costs of forecasting errors as the forecasts are constructed, and an emphasis on maximizing forecasting accuracy, the author shows how his decades of research on the topic improves forecasts of risk. Criminal justice risk forecasts anticipate the future behavior of specified individuals, rather than “predictive policing” for locations in time and space, which is a very different enterprise that uses different data different data analysis tools. The audience for this book includes graduate students and researchers in the social sciences, and data analysts in criminal justice agencies. Formal mathematics is used only as necessary or in concert with more intuitive explanations.

Disrupting Finance

Author : Theo Lynn
Publisher : Springer
ISBN 13 : 3030023303
Total Pages : 194 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Disrupting Finance by : Theo Lynn

Download or read book Disrupting Finance written by Theo Lynn and published by Springer. This book was released on 2018-12-06 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Machine Learning for Financial Risk Management with Python

Author : Abdullah Karasan
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492085200
Total Pages : 334 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Machine Learning for Financial Risk Management with Python by : Abdullah Karasan

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

Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance

Author : El Bachir Boukherouaa
Publisher : International Monetary Fund
ISBN 13 : 1589063953
Total Pages : 35 pages
Book Rating : 4.5/5 (89 download)

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Book Synopsis Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance by : El Bachir Boukherouaa

Download or read book Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance written by El Bachir Boukherouaa and published by International Monetary Fund. This book was released on 2021-10-22 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.

Machine Learning for Financial Risk Management with Python

Author : Abdullah Karasan
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492085227
Total Pages : 334 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Machine Learning for Financial Risk Management with Python by : Abdullah Karasan

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, and risk analysts will explore Python-based machine learning and deep learning models for assessing financial risk. You'll learn how to compare results from ML models with results obtained by traditional financial risk models. Author Abdullah Karasan helps you explore the theory behind financial risk assessment before diving into the differences between traditional and ML models. 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 Revisit and improve market risk models (VaR and expected shortfall) using machine learning techniques Develop a credit risk based on a clustering technique for risk bucketing, then apply Bayesian estimation, Markov chain, and other ML models Capture different aspects of liquidity with a Gaussian mixture model Use machine learning models for fraud detection Identify corporate risk using the stock price crash metric Explore a synthetic data generation process to employ in financial risk.

Interpretable Machine Learning

Author : Christoph Molnar
Publisher : Lulu.com
ISBN 13 : 0244768528
Total Pages : 320 pages
Book Rating : 4.2/5 (447 download)

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Book Synopsis Interpretable Machine Learning by : Christoph Molnar

Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Empirical Asset Pricing

Author : Wayne Ferson
Publisher : MIT Press
ISBN 13 : 0262039370
Total Pages : 497 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Empirical Asset Pricing by : Wayne Ferson

Download or read book Empirical Asset Pricing written by Wayne Ferson and published by MIT Press. This book was released on 2019-03-12 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.

Risk Modeling

Author : Terisa Roberts
Publisher : John Wiley & Sons
ISBN 13 : 1119824931
Total Pages : 214 pages
Book Rating : 4.1/5 (198 download)

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Book Synopsis Risk Modeling by : Terisa Roberts

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.

Machine Learning for Risk Calculations

Author : Ignacio Ruiz
Publisher : John Wiley & Sons
ISBN 13 : 1119791405
Total Pages : 471 pages
Book Rating : 4.1/5 (197 download)

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Book Synopsis Machine Learning for Risk Calculations by : Ignacio Ruiz

Download or read book Machine Learning for Risk Calculations written by Ignacio Ruiz and published by John Wiley & Sons. This book was released on 2021-12-20 with total page 471 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Machine Learning in Banking Risk Management

Author : Mourine Atsien
Publisher : GRIN Verlag
ISBN 13 : 334681422X
Total Pages : 17 pages
Book Rating : 4.3/5 (468 download)

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Book Synopsis Machine Learning in Banking Risk Management by : Mourine Atsien

Download or read book Machine Learning in Banking Risk Management written by Mourine Atsien and published by GRIN Verlag. This book was released on 2023-02-20 with total page 17 pages. Available in PDF, EPUB and Kindle. Book excerpt: Essay from the year 2022 in the subject Business economics - Banking, Stock Exchanges, Insurance, Accounting, grade: A, , course: Business and technology, language: English, abstract: Technological applications are playing a more influential role in management in the contemporary business environment. Machine learning, artificial intelligence, and other algorithmic applications are some of the most common influencers in business applications. They present numerous solutions to business management problems, including banking risk management. In the last decade, risk management has gained greater prominence in financial services. In the past, banks focused on the detection, measuring, and reporting of risks. However, they are now leveraging on machine learning for greater accuracy and efficacy in risk management. As such, this paper explored different ways that machine learning applies in banking risk management. To achieve the objective of this study, the researcher conducted a comprehensive literature review on the topic of machine learning in banking risk management. The researcher found considerable industry and academic research focusing on developments in the financial services industry, especially in relation to risk management. It reviewed the literature, analysing and evaluating various risk management machine-learning techniques. It identified risk management problem areas and explored various ways of addressing them. The review showed that machine learning learning in risk management in financial services sector was still under-researched. While there were many studies on credit risks, other risks such as liquidity risks, market risks, and operational risks saw minimal attention. Nevertheless, machine learning applications were found to have the potential to develop more effective risk management models. Machine learning is leveraged on different data types to predict potential events with greater accuracy and estimate losses associated with different risk types. In addition, the machine learning techniques in risk management were found to provide better and more accurate results than traditional statistical models. Though machine learning suggests improving banking risk management, there are some areas that need further study. For instance, the paper suggested in-depth studies on machine learning models for different types of banking risks.

Applications of Machine Learning

Author : Prashant Johri
Publisher : Springer Nature
ISBN 13 : 9811533571
Total Pages : 404 pages
Book Rating : 4.8/5 (115 download)

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Book Synopsis Applications of Machine Learning by : Prashant Johri

Download or read book Applications of Machine Learning written by Prashant Johri and published by Springer Nature. This book was released on 2020-05-04 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers applications of machine learning in artificial intelligence. The specific topics covered include human language, heterogeneous and streaming data, unmanned systems, neural information processing, marketing and the social sciences, bioinformatics and robotics, etc. It also provides a broad range of techniques that can be successfully applied and adopted in different areas. Accordingly, the book offers an interesting and insightful read for scholars in the areas of computer vision, speech recognition, healthcare, business, marketing, and bioinformatics.

Machine Learning in Risk Measurement

Author : Sascha Wilkens
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (13 download)

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Book Synopsis Machine Learning in Risk Measurement by : Sascha Wilkens

Download or read book Machine Learning in Risk Measurement written by Sascha Wilkens and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: While machine learning and its many variants are becoming established tools in quantitative finance, their application in a risk measurement context is less developed. This paper uses a scheme from probability theory and statistics - Gaussian Processes - and applies the corresponding non-parametric technique of Gaussian Process Regression to “train” a system suitable for revaluing instruments as required to determine a portfolio's Value-at-Risk and Expected Shortfall. Time series of historical valuation parameters and prices of the portfolio's constituents serve as the only inputs. On the example of a variety of portfolios consisting of vanilla and barrier options, it is demonstrated that, even with limited training sets, Gaussian Process Regression leads to risk figures identical to those from full revaluation and outperforms Taylor expansion. Applications for risk management and regulatory capital calculations are apparent. Research into an extension to related areas such as counterparty credit risk measurement is promising.

Criminal Justice Forecasts of Risk

Author : Richard Berk
Publisher : Springer Science & Business Media
ISBN 13 : 1461430852
Total Pages : 121 pages
Book Rating : 4.4/5 (614 download)

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Book Synopsis Criminal Justice Forecasts of Risk by : Richard Berk

Download or read book Criminal Justice Forecasts of Risk written by Richard Berk and published by Springer Science & Business Media. This book was released on 2012-04-06 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning and nonparametric function estimation procedures can be effectively used in forecasting. One important and current application is used to make forecasts of “future dangerousness" to inform criminal justice decision. Examples include the decision to release an individual on parole, determination of the parole conditions, bail recommendations, and sentencing. Since the 1920s, "risk assessments" of various kinds have been used in parole hearings, but the current availability of large administrative data bases, inexpensive computing power, and developments in statistics and computer science have increased their accuracy and applicability. In this book, these developments are considered with particular emphasis on the statistical and computer science tools, under the rubric of supervised learning, that can dramatically improve these kinds of forecasts in criminal justice settings. The intended audience is researchers in the social sciences and data analysts in criminal justice agencies.

Measuring Market Risk

Author : Kevin Dowd
Publisher : John Wiley & Sons
ISBN 13 : 0470855215
Total Pages : 395 pages
Book Rating : 4.4/5 (78 download)

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Book Synopsis Measuring Market Risk by : Kevin Dowd

Download or read book Measuring Market Risk written by Kevin Dowd and published by John Wiley & Sons. This book was released on 2003-02-28 with total page 395 pages. Available in PDF, EPUB and Kindle. Book excerpt: The most up-to-date resource on market risk methodologies Financial professionals in both the front and back office require an understanding of market risk and how to manage it. Measuring Market Risk provides this understanding with an overview of the most recent innovations in Value at Risk (VaR) and Expected Tail Loss (ETL) estimation. This book is filled with clear and accessible explanations of complex issues that arise in risk measuring-from parametric versus nonparametric estimation to incre-mental and component risks. Measuring Market Risk also includes accompanying software written in Matlab—allowing the reader to simulate and run the examples in the book.

Bio-Inspired Credit Risk Analysis

Author : Lean Yu
Publisher : Springer Science & Business Media
ISBN 13 : 3540778039
Total Pages : 248 pages
Book Rating : 4.5/5 (47 download)

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Book Synopsis Bio-Inspired Credit Risk Analysis by : Lean Yu

Download or read book Bio-Inspired Credit Risk Analysis written by Lean Yu and published by Springer Science & Business Media. This book was released on 2008-04-24 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: Credit risk analysis is one of the most important topics in the field of financial risk management. Due to recent financial crises and regulatory concern of Basel II, credit risk analysis has been the major focus of financial and banking industry. Especially for some credit-granting institutions such as commercial banks and credit companies, the ability to discriminate good customers from bad ones is crucial. The need for reliable quantitative models that predict defaults accurately is imperative so that the interested parties can take either preventive or corrective action. Hence credit risk analysis becomes very important for sustainability and profit of enterprises. In such backgrounds, this book tries to integrate recent emerging support vector machines and other computational intelligence techniques that replicate the principles of bio-inspired information processing to create some innovative methodologies for credit risk analysis and to provide decision support information for interested parties.

Machine Learning for Risk Calculations

Author : Ignacio Ruiz
Publisher : John Wiley & Sons
ISBN 13 : 1119791383
Total Pages : 471 pages
Book Rating : 4.1/5 (197 download)

DOWNLOAD NOW!

Book Synopsis Machine Learning for Risk Calculations by : Ignacio Ruiz

Download or read book Machine Learning for Risk Calculations written by Ignacio Ruiz and published by John Wiley & Sons. This book was released on 2021-12-28 with total page 471 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Artificial Intelligence in Asset Management

Author : Söhnke M. Bartram
Publisher : CFA Institute Research Foundation
ISBN 13 : 195292703X
Total Pages : 95 pages
Book Rating : 4.9/5 (529 download)

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Book Synopsis Artificial Intelligence in Asset Management by : Söhnke M. Bartram

Download or read book Artificial Intelligence in Asset Management written by Söhnke M. Bartram and published by CFA Institute Research Foundation. This book was released on 2020-08-28 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity.