Ai Ml For Decision And Risk Analysis

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AI-ML for Decision and Risk Analysis

Louis Anthony Cox Jr
AI ML for Decision and Risk Analysis Book PDF

Author : Louis Anthony Cox Jr
Publisher : Unknown
Page : 0 pages
File Size : 49,6 Mb
Release : 2023
Category : Electronic
ISBN : 3031320158

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AI-ML for Decision and Risk Analysis by Louis Anthony Cox Jr Pdf

This book explains and illustrates recent developments and advances in decision-making and risk analysis. It demonstrates how artificial intelligence (AI) and machine learning (ML) have not only benefitted from classical decision analysis concepts such as expected utility maximization but have also contributed to making normative decision theory more useful by forcing it to confront realistic complexities. These include skill acquisition, uncertain and time-consuming implementation of intended actions, open-world uncertainties about what might happen next and what consequences actions can have, and learning to cope effectively with uncertain and changing environments. The result is a more robust and implementable technology for AI/ML-assisted decision-making. The book is intended to inform a wide audience in related applied areas and to provide a fun and stimulating resource for students, researchers, and academics in data science and AI-ML, decision analysis, and other closely linked academic fields. It will also appeal to managers, analysts, decision-makers, and policymakers in financial, health and safety, environmental, business, engineering, and security risk management.

AI-ML for Decision and Risk Analysis

Louis Anthony Cox Jr.
AI ML for Decision and Risk Analysis Book PDF

Author : Louis Anthony Cox Jr.
Publisher : Springer Nature
Page : 443 pages
File Size : 54,5 Mb
Release : 2023-07-05
Category : Business & Economics
ISBN : 9783031320132

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AI-ML for Decision and Risk Analysis by Louis Anthony Cox Jr. Pdf

This book explains and illustrates recent developments and advances in decision-making and risk analysis. It demonstrates how artificial intelligence (AI) and machine learning (ML) have not only benefitted from classical decision analysis concepts such as expected utility maximization but have also contributed to making normative decision theory more useful by forcing it to confront realistic complexities. These include skill acquisition, uncertain and time-consuming implementation of intended actions, open-world uncertainties about what might happen next and what consequences actions can have, and learning to cope effectively with uncertain and changing environments. The result is a more robust and implementable technology for AI/ML-assisted decision-making. The book is intended to inform a wide audience in related applied areas and to provide a fun and stimulating resource for students, researchers, and academics in data science and AI-ML, decision analysis, and other closely linked academic fields. It will also appeal to managers, analysts, decision-makers, and policymakers in financial, health and safety, environmental, business, engineering, and security risk management.

Risk Modeling

Terisa Roberts,Stephen J. Tonna
Risk Modeling Book PDF

Author : Terisa Roberts,Stephen J. Tonna
Publisher : John Wiley & Sons
Page : 214 pages
File Size : 50,5 Mb
Release : 2022-09-20
Category : Business & Economics
ISBN : 9781119824947

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Risk Modeling by Terisa Roberts,Stephen J. Tonna Pdf

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.

Disrupting Finance

Theo Lynn,John G. Mooney,Pierangelo Rosati,Mark Cummins
Disrupting Finance Book PDF

Author : Theo Lynn,John G. Mooney,Pierangelo Rosati,Mark Cummins
Publisher : Springer
Page : 194 pages
File Size : 55,6 Mb
Release : 2018-12-06
Category : Business & Economics
ISBN : 9783030023300

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Disrupting Finance by Theo Lynn,John G. Mooney,Pierangelo Rosati,Mark Cummins Pdf

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.

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

El Bachir Boukherouaa,Mr. Ghiath Shabsigh,Khaled AlAjmi,Jose Deodoro,Aquiles Farias,Ebru S Iskender,Mr. Alin T Mirestean,Rangachary Ravikumar
Powering the Digital Economy Opportunities and Risks of Artificial Intelligence in Finance Book PDF

Author : El Bachir Boukherouaa,Mr. Ghiath Shabsigh,Khaled AlAjmi,Jose Deodoro,Aquiles Farias,Ebru S Iskender,Mr. Alin T Mirestean,Rangachary Ravikumar
Publisher : International Monetary Fund
Page : 35 pages
File Size : 40,8 Mb
Release : 2021-10-22
Category : Business & Economics
ISBN : 9781589063952

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Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance by El Bachir Boukherouaa,Mr. Ghiath Shabsigh,Khaled AlAjmi,Jose Deodoro,Aquiles Farias,Ebru S Iskender,Mr. Alin T Mirestean,Rangachary Ravikumar Pdf

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 High-Risk Applications

Patrick Hall,James Curtis,Parul Pandey
Machine Learning for High Risk Applications Book PDF

Author : Patrick Hall,James Curtis,Parul Pandey
Publisher : "O'Reilly Media, Inc."
Page : 496 pages
File Size : 46,6 Mb
Release : 2023-04-17
Category : Computers
ISBN : 9781098102395

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Machine Learning for High-Risk Applications by Patrick Hall,James Curtis,Parul Pandey Pdf

The past decade has witnessed the broad adoption of artificial intelligence and machine learning (AI/ML) technologies. However, a lack of oversight in their widespread implementation has resulted in some incidents and harmful outcomes that could have been avoided with proper risk management. Before we can realize AI/ML's true benefit, practitioners must understand how to mitigate its risks. This book describes approaches to responsible AI—a holistic framework for improving AI/ML technology, business processes, and cultural competencies that builds on best practices in risk management, cybersecurity, data privacy, and applied social science. Authors Patrick Hall, James Curtis, and Parul Pandey created this guide for data scientists who want to improve real-world AI/ML system outcomes for organizations, consumers, and the public. Learn technical approaches for responsible AI across explainability, model validation and debugging, bias management, data privacy, and ML security Learn how to create a successful and impactful AI risk management practice Get a basic guide to existing standards, laws, and assessments for adopting AI technologies, including the new NIST AI Risk Management Framework Engage with interactive resources on GitHub and Colab

Emerging Methods in Predictive Analytics: Risk Management and Decision-Making

Hsu, William H.
Emerging Methods in Predictive Analytics Risk Management and Decision Making Book PDF

Author : Hsu, William H.
Publisher : IGI Global
Page : 425 pages
File Size : 45,8 Mb
Release : 2014-01-31
Category : Business & Economics
ISBN : 9781466650640

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Emerging Methods in Predictive Analytics: Risk Management and Decision-Making by Hsu, William H. Pdf

Decision making tools are essential for the successful outcome of any organization. Recent advances in predictive analytics have aided in identifying particular points of leverage where critical decisions can be made. Emerging Methods in Predictive Analytics: Risk Management and Decision Making provides an interdisciplinary approach to predictive analytics; bringing together the fields of business, statistics, and information technology for effective decision making. Managers, business professionals, and decision makers in diverse fields will find the applications and cases presented in this text essential in providing new avenues for risk assessment, management, and predicting the future outcomes of their decisions.

Artificial Intelligence for Risk Management

Archie Addo,Srini Centhala,Muthu Shanmugam
Artificial Intelligence for Risk Management Book PDF

Author : Archie Addo,Srini Centhala,Muthu Shanmugam
Publisher : Business Expert Press
Page : 127 pages
File Size : 46,6 Mb
Release : 2020-03-13
Category : Business & Economics
ISBN : 9781949443523

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Artificial Intelligence for Risk Management by Archie Addo,Srini Centhala,Muthu Shanmugam Pdf

Artificial Intelligence (AI) for Risk Management is about using AI to manage risk in the corporate environment. The content of this work focuses on concepts, principles, and practical applications that are relevant to the corporate and technology environments. The authors introduce AI and discuss the different types, capabilities, and purposes–including challenges. With AI also comes risk. This book defines risk, provides examples, and includes information on the risk-management process. Having a solid knowledge base for an AI project is key and this book will help readers define the knowledge base needed for an AI project by developing and identifying objectives of the risk-knowledge base and knowledge acquisition for risk. This book will help you become a contributor on an AI team and learn how to tell a compelling story with AI to drive business action on risk.

Handbook on Decision Making

Jie Lu,Lakhmi C Jain,Guangquan Zhang
Handbook on Decision Making Book PDF

Author : Jie Lu,Lakhmi C Jain,Guangquan Zhang
Publisher : Springer Science & Business Media
Page : 454 pages
File Size : 55,6 Mb
Release : 2012-03-15
Category : Technology & Engineering
ISBN : 9783642257551

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Handbook on Decision Making by Jie Lu,Lakhmi C Jain,Guangquan Zhang Pdf

This book presents innovative theories, methodologies, and techniques in the field of risk management and decision making. It introduces new research developments and provides a comprehensive image of their potential applications to readers interested in the area. The collection includes: computational intelligence applications in decision making, multi-criteria decision making under risk, risk modelling,forecasting and evaluation, public security and community safety, risk management in supply chain and other business decision making, political risk management and disaster response systems. The book is directed to academic and applied researchers working on risk management, decision making, and management information systems.

Alternative Data and Artificial Intelligence Techniques

Qingquan Tony Zhang,Beibei Li,Danxia Xie
Alternative Data and Artificial Intelligence Techniques Book PDF

Author : Qingquan Tony Zhang,Beibei Li,Danxia Xie
Publisher : Palgrave Macmillan
Page : 0 pages
File Size : 41,8 Mb
Release : 2023-11-15
Category : Business & Economics
ISBN : 3031116143

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Alternative Data and Artificial Intelligence Techniques by Qingquan Tony Zhang,Beibei Li,Danxia Xie Pdf

This book introduces a state-of-art approach in evaluating portfolio management and risk based on artificial intelligence and alternative data. The book covers a textual analysis of news and social media, information extraction from GPS and IoTs data, and risk predictions based on small transaction data, etc. The book summarizes and introduces the advancement in each area and highlights the machine learning and deep learning techniques utilized to achieve the goals. As a complement, it also illustrates examples on how to leverage the python package to visualize and analyze the alternative datasets, and will be of interest to academics, researchers, and students of risk evaluation, risk management, data, AI, and financial innovation.

Artificial Intelligence in Asset Management

Söhnke M. Bartram,Jürgen Branke,Mehrshad Motahari
Artificial Intelligence in Asset Management Book PDF

Author : Söhnke M. Bartram,Jürgen Branke,Mehrshad Motahari
Publisher : CFA Institute Research Foundation
Page : 95 pages
File Size : 48,5 Mb
Release : 2020-08-28
Category : Business & Economics
ISBN : 9781952927034

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Artificial Intelligence in Asset Management by Söhnke M. Bartram,Jürgen Branke,Mehrshad Motahari Pdf

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.

Interpretable Machine Learning

Christoph Molnar
Interpretable Machine Learning Book PDF

Author : Christoph Molnar
Publisher : Lulu.com
Page : 320 pages
File Size : 41,7 Mb
Release : 2020
Category : Artificial intelligence
ISBN : 9780244768522

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Interpretable Machine Learning by Christoph Molnar Pdf

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

Abdullah Karasan
Machine Learning for Financial Risk Management with Python Book PDF

Author : Abdullah Karasan
Publisher : "O'Reilly Media, Inc."
Page : 334 pages
File Size : 52,9 Mb
Release : 2021-12-07
Category : Business & Economics
ISBN : 9781492085225

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Machine Learning for Financial Risk Management with Python by Abdullah Karasan Pdf

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.

Machine Learning and Artificial Intelligence for Credit Risk Analytics

Tiziano Bellini
Machine Learning and Artificial Intelligence for Credit Risk Analytics Book PDF

Author : Tiziano Bellini
Publisher : Wiley
Page : 304 pages
File Size : 54,6 Mb
Release : 2023-06-26
Category : Business & Economics
ISBN : 1119781051

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Machine Learning and Artificial Intelligence for Credit Risk Analytics by Tiziano Bellini Pdf

Machine Learning and Artificial Intelligence for Credit Risk Analytics provides a comprehensive, practical toolkit for applying ML and AI to day-to-day credit risk management challenges. Beginning with coverage of data management in banking, the book goes on to discuss individual and multiple classifier approaches, reinforcement learning and AI in credit portfolio modelling, lifetime PD modelling, LGD modelling and EAD modelling. Fully worked examples in Python and R appear throughout the book, with source code provided on the companion website. Machine Learning and Artificial Intelligence for Credit Risk Analytics fully covers the key concepts required to understand, challenge and validate credit risk models, whilst also looking to the future development of AI applications in credit risk management, demonstrating the need to embed economics and statistics to inform short, medium and long-term decision-making.

Artificial Intelligence and Machine Learning in Business Management

Sandeep Kumar Panda,Vaibhav Mishra,R. Balamurali,Ahmed A. Elngar
Artificial Intelligence and Machine Learning in Business Management Book PDF

Author : Sandeep Kumar Panda,Vaibhav Mishra,R. Balamurali,Ahmed A. Elngar
Publisher : CRC Press
Page : 278 pages
File Size : 46,8 Mb
Release : 2021-11-05
Category : Business & Economics
ISBN : 9781000432114

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Artificial Intelligence and Machine Learning in Business Management by Sandeep Kumar Panda,Vaibhav Mishra,R. Balamurali,Ahmed A. Elngar Pdf

Artificial Intelligence and Machine Learning in Business Management The focus of this book is to introduce artificial intelligence (AI) and machine learning (ML) technologies into the context of business management. The book gives insights into the implementation and impact of AI and ML to business leaders, managers, technology developers, and implementers. With the maturing use of AI or ML in the field of business intelligence, this book examines several projects with innovative uses of AI beyond data organization and access. It follows the Predictive Modeling Toolkit for providing new insight on how to use improved AI tools in the field of business. It explores cultural heritage values and risk assessments for mitigation and conservation and discusses on-shore and off-shore technological capabilities with spatial tools for addressing marketing and retail strategies, and insurance and healthcare systems. Taking a multidisciplinary approach for using AI, this book provides a single comprehensive reference resource for undergraduate, graduate, business professionals, and related disciplines.