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17 produkter

New Frontiers in Enterprise Risk Management
AvDavid L. Olson,Desheng Wu
Häftad, Engelska, 2008
1 124 kr
Skickas inom 10-15 vardagar
Risk management has become a critical part of doing business in the twenty-first century. This book is a collection of material about enterprise risk management, and the role of risk in decision making. Part I introduces the topic of enterprise risk management. Part II presents enterprise risk management from perspectives of finance, accounting, insurance, supply chain operations, and project management. Technology tools are addressed in Part III, including financial models of risk as well as accounting aspects, using data envelopment analysis, neural network tools for credit risk evaluation, and real option analysis applied to information techn- ogy outsourcing. In Part IV, three chapters present enterprise risk management experience in China, including banking, chemical plant operations, and information technology. Lincoln, USA David L. Olson Toronto, Canada Desheng Wu February 2008 v Contents Part I Preliminary 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 David L. Olson & Desheng Wu 2 The Human Reaction to Risk and Opportunity . . . . . . . . . . . . . . . . . . . 7 David R. Koenig Part II ERM Perspectives 3 Enterprise Risk Management: Financial and Accounting Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Desheng Wu & David L. Olson 4 An Empirical Study on Enterprise Risk Management in Insurance . . 39 Madhusudan Acharyya 5 Supply Chain Risk Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 David L. Olson & Desheng Wu 6 Two Polar Concept of Project Risk Management. . . . . . . . . . . . . . . . . . 69 Seyed Mohammad Seyedhoseini, Siamak Noori & Mohammed AliHatefi Part III ERM Technologies 7 The Mathematics of Risk Transfer. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . 95 Marcos Escobar & Luis Seco 8 Stable Models in Risk Management. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
New Frontiers in Enterprise Risk Management
AvDesheng Wu,David L. Olson
E-bok
PDF, Engelska, 2008
1 470 kr
Läs direkt efter köp
Risk management has become a critical part of doing business in the twenty-first century. This book is a collection of material about enterprise risk management, and the role of risk in decision making. Part I introduces the topic of enterprise risk management. Part II presents enterprise risk management from perspectives of finance, accounting, insurance, supply chain operations, and project management. Technology tools are addressed in Part III, including financial models of risk as well as accounting aspects, using data envelopment analysis, neural network tools for credit risk evaluation, and real option analysis applied to information techn- ogy outsourcing. In Part IV, three chapters present enterprise risk management experience in China, including banking, chemical plant operations, and information technology. Lincoln, USA David L. Olson Toronto, Canada Desheng Wu February 2008 v Contents Part I Preliminary 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 David L. Olson & Desheng Wu 2 The Human Reaction to Risk and Opportunity . . . . . . . . . . . . . . . . . . . 7 David R. Koenig Part II ERM Perspectives 3 Enterprise Risk Management: Financial and Accounting Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Desheng Wu & David L. Olson 4 An Empirical Study on Enterprise Risk Management in Insurance . . 39 Madhusudan Acharyya 5 Supply Chain Risk Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 David L. Olson & Desheng Wu 6 Two Polar Concept of Project Risk Management. . . . . . . . . . . . . . . . . . 69 Seyed Mohammad Seyedhoseini, Siamak Noori & Mohammed AliHatefi Part III ERM Technologies 7 The Mathematics of Risk Transfer. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . 95 Marcos Escobar & Luis Seco 8 Stable Models in Risk Management. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
New Frontiers in Enterprise Risk Management
AvDavid L. Olson,Desheng Wu
Häftad, Engelska, 2010
1 627 kr
Skickas inom 10-15 vardagar
Risk management has become a critical part of doing business in the twenty-first century. This book is a collection of material about enterprise risk management, and the role of risk in decision making. Part I introduces the topic of enterprise risk management. Part II presents enterprise risk management from perspectives of finance, accounting, insurance, supply chain operations, and project management. Technology tools are addressed in Part III, including financial models of risk as well as accounting aspects, using data envelopment analysis, neural network tools for credit risk evaluation, and real option analysis applied to information techn- ogy outsourcing. In Part IV, three chapters present enterprise risk management experience in China, including banking, chemical plant operations, and information technology. Lincoln, USA David L. Olson Toronto, Canada Desheng Wu February 2008 v Contents Part I Preliminary 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 David L. Olson & Desheng Wu 2 The Human Reaction to Risk and Opportunity . . . . . . . . . . . . . . . . . . . 7 David R. Koenig Part II ERM Perspectives 3 Enterprise Risk Management: Financial and Accounting Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Desheng Wu & David L. Olson 4 An Empirical Study on Enterprise Risk Management in Insurance . . 39 Madhusudan Acharyya 5 Supply Chain Risk Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 David L. Olson & Desheng Wu 6 Two Polar Concept of Project Risk Management. . . . . . . . . . . . . . . . . . 69 Seyed Mohammad Seyedhoseini, Siamak Noori & Mohammed AliHatefi Part III ERM Technologies 7 The Mathematics of Risk Transfer. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . 95 Marcos Escobar & Luis Seco 8 Stable Models in Risk Management. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Enterprise Risk Management Models
AvDesheng Wu,David L. Olson
E-bok
Engelska, 2020
1 798 kr
Läs direkt efter köp
This book offers a comprehensive guide to several aspects of risk, including information systems, disaster management, supply chain and disaster management perspectives. A major portion of the book is devoted to presenting a number of operations research models that have been (or could be) applied to enterprise supply risk management, especially from the supply chain perspective. Each chapter of this book can be used as a stand-alone module on a respective topic, with dedicated examples, definitions and discussion notes.
This book comes at a time when the world is increasingly challenged by different forms of risk and how to manage them. Events of the 21st Century have made enterprise risk management even more critical. Risks such as suspicions surrounding top-management structures, financial and technology bubbles (especially since 2008), as well as the risk posed by terrorism, such as the 9/11 attacks in the U.S. as well as more recent events in France, Belgium, and otherEuropean countries, have a tremendous impact on many facets of business. Businesses, in fact, exist to cope with risk in their area of specialization.
Enterprise Risk Management Models
Focus on Sustainability
AvDavid L. Olson,Desheng Wu
Inbunden, Engelska, 2023
1 460 kr
Skickas inom 10-15 vardagar
This textbook, now in its fourth edition, serves as a comprehensive guide to learning various aspects of risk, encompassing supply chain management, artificial intelligence, and sustainability. It demonstrates a wide range of operations research models that have been successfully applied to enterprise supply chain risk management. Each chapter of the book can function as a standalone module focusing on a specific topic, offering dedicated examples, definitions, and discussion notes.The publication of this book comes at a crucial time when the world is facing increasing challenges from various forms of risk. Events such as Covid-19, the energy crisis, wars, and terrorism in the 21st century have all disrupted supply chains, thus highlighting the critical importance of enterprise risk management. Additional risks, such as financial and technological bubbles, along with concerns surrounding rampant artificial intelligence, contribute to a climate that demands enhanced risk management within organizations.
Enterprise Risk Management Models
Focus on Sustainability
AvDesheng Wu,David L. Olson
E-bok
Engelska, 2023
1 780 kr
Läs direkt efter köp
This textbook, now in its fourth edition, serves as a comprehensive guide to learning various aspects of risk, encompassing supply chain management, artificial intelligence, and sustainability. It demonstrates a wide range of operations research models that have been successfully applied to enterprise supply chain risk management. Each chapter of the book can function as a standalone module focusing on a specific topic, offering dedicated examples, definitions, and discussion notes.
The publication of this book comes at a crucial time when the world is facing increasing challenges from various forms of risk. Events such as Covid-19, the energy crisis, wars, and terrorism in the 21st century have all disrupted supply chains, thus highlighting the critical importance of enterprise risk management. Additional risks, such as financial and technological bubbles, along with concerns surrounding rampant artificial intelligence, contribute to a climate that demands enhanced risk management within organizations.
Predictive Data Mining Models
AvDesheng Wu,David L. Olson
E-bok
Engelska, 2016
1 292 kr
Läs direkt efter köp
This book reviews forecasting data mining models, from basic tools for stable data through causal models, to more advanced models using trends and cycles. These models are demonstrated on the basis of business-related data, including stock indices, crude oil prices, and the price of gold. The book’s main approach is above all descriptive, seeking to explain how the methods concretely work; as such, it includes selected citations, but does not go into deep scholarly reference. The data sets and software reviewed were selected for their widespread availability to all readers with internet access.
Predictive Data Mining Models
AvDavid L. Olson,Desheng Wu
Häftad, Engelska, 2018
1 329 kr
Skickas inom 5-8 vardagar
This book reviews forecasting data mining models, from basic tools for stable data through causal models, to more advanced models using trends and cycles. These models are demonstrated on the basis of business-related data, including stock indices, crude oil prices, and the price of gold. The book’s main approach is above all descriptive, seeking to explain how the methods concretely work; as such, it includes selected citations, but does not go into deep scholarly reference. The data sets and software reviewed were selected for their widespread availability to all readers with internet access.
Predictive Data Mining Models
AvDavid L. Olson,Desheng Wu
Inbunden, Engelska, 2019
1 258 kr
Skickas inom 10-15 vardagar
This book provides an overview of predictive methods demonstrated by open source software modeling with Rattle (R’) and WEKA. Knowledge management involves application of human knowledge (epistemology) with the technological advances of our current society (computer systems) and big data, both in terms of collecting data and in analyzing it. We see three types of analytic tools. Descriptive analytics focus on reports of what has happened. Predictive analytics extend statistical and/or artificial intelligence to provide forecasting capability. It also includes classification modeling. Prescriptive analytics applies quantitative models to optimize systems, or at least to identify improved systems. Data mining includes descriptive and predictive modeling. Operations research includes all three. This book focuses on prescriptive analytics.The book seeks to provide simple explanations and demonstration of some descriptive tools. This second editionprovides more examples of big data impact, updates the content on visualization, clarifies some points, and expands coverage of association rules and cluster analysis. Chapter 1 gives an overview in the context of knowledge management. Chapter 2 discusses some basic data types. Chapter 3 covers fundamentals time series modeling tools, and Chapter 4 provides demonstration of multiple regression modeling. Chapter 5 demonstrates regression tree modeling. Chapter 6 presents autoregressive/integrated/moving average models, as well as GARCH models. Chapter 7 covers the set of data mining tools used in classification, to include special variants support vector machines, random forests, and boosting. Models are demonstrated using business related data. The style of the book is intended to be descriptive, seeking to explain how methods work, with some citations, but without deep scholarly reference. The data sets and software are all selected for widespread availability and access by any reader with computer links.
Predictive Data Mining Models
AvDesheng Wu,David L. Olson
E-bok
Engelska, 2019
1 150 kr
Läs direkt efter köp
This book provides an overview of predictive methods demonstrated by open source software modeling with Rattle (R’) and WEKA. Knowledge management involves application of human knowledge (epistemology) with the technological advances of our current society (computer systems) and big data, both in terms of collecting data and in analyzing it. We see three types of analytic tools. Descriptive analytics focus on reports of what has happened. Predictive analytics extend statistical and/or artificial intelligence to provide forecasting capability. It also includes classification modeling. Prescriptive analytics applies quantitative models to optimize systems, or at least to identify improved systems. Data mining includes descriptive and predictive modeling. Operations research includes all three. This book focuses on prescriptive analytics.The book seeks to provide simple explanations and demonstration of some descriptive tools. This second editionprovides more examples of big data impact, updates the content on visualization, clarifies some points, and expands coverage of association rules and cluster analysis. Chapter 1 gives an overview in the context of knowledge management. Chapter 2 discusses some basic data types. Chapter 3 covers fundamentals time series modeling tools, and Chapter 4 provides demonstration of multiple regression modeling. Chapter 5 demonstrates regression tree modeling. Chapter 6 presents autoregressive/integrated/moving average models, as well as GARCH models. Chapter 7 covers the set of data mining tools used in classification, to include special variants support vector machines, random forests, and boosting. Models are demonstrated using business related data. The style of the book is intended to be descriptive, seeking to explain how methods work, with some citations, but without deep scholarly reference. The data sets and software are all selected for widespread availability and access by any reader with computer links.
Predictive Data Mining Models
AvDavid L. Olson,Desheng Wu
Häftad, Engelska, 2020
901 kr
Skickas inom 10-15 vardagar
This book provides an overview of predictive methods demonstrated by open source software modeling with Rattle (R’) and WEKA. Knowledge management involves application of human knowledge (epistemology) with the technological advances of our current society (computer systems) and big data, both in terms of collecting data and in analyzing it. We see three types of analytic tools. Descriptive analytics focus on reports of what has happened. Predictive analytics extend statistical and/or artificial intelligence to provide forecasting capability. It also includes classification modeling. Prescriptive analytics applies quantitative models to optimize systems, or at least to identify improved systems. Data mining includes descriptive and predictive modeling. Operations research includes all three. This book focuses on prescriptive analytics.The book seeks to provide simple explanations and demonstration of some descriptive tools. This second editionprovides more examples of big data impact, updates the content on visualization, clarifies some points, and expands coverage of association rules and cluster analysis. Chapter 1 gives an overview in the context of knowledge management. Chapter 2 discusses some basic data types. Chapter 3 covers fundamentals time series modeling tools, and Chapter 4 provides demonstration of multiple regression modeling. Chapter 5 demonstrates regression tree modeling. Chapter 6 presents autoregressive/integrated/moving average models, as well as GARCH models. Chapter 7 covers the set of data mining tools used in classification, to include special variants support vector machines, random forests, and boosting. Models are demonstrated using business related data. The style of the book is intended to be descriptive, seeking to explain how methods work, with some citations, but without deep scholarly reference. The data sets and software are all selected for widespread availability and access by any reader with computer links.
Smart Healthcare Engineering Management and Risk Analytics
AvShuai Ding,Desheng Wum. fl.
Inbunden, Engelska, 2022
566 kr
Skickas inom 10-15 vardagar
This book aims to stay one step beyond the innovations of information and communication technologies and smart healthcare management and provides an overview of the risks smart healthcare management could help to alleviate, and those risks it would create or amplify. Inclusive discussions of the core of smart healthcare services in the perspective of system engineering are enclosed, such as smart healthcare definition, data information knowledge service, and intelligent hospital management. Summaries of technological and theoretical innovations spanning each step of the modern healthcare system are included, from health screening, clinical diagnosis, cancer screening, to in-hospital mortality monitoring, minimally invasive surgeries, and medical data storages. Analytics of risks reduced and induced by these innovations are provided, with potential solutions to such risks in healthcare management discussed. This book seeks to provide demonstrative examples of incidence capable innovations of healthcare technologies, which, while greatly enhancing abilities of healthcare workers and institutions, could pose risks to patients and sometimes even greater threats to the integrity of the healthcare system. The style of the book is intended to be demonstrative but most suited for researchers and graduate students, explaining the methodology behind healthcare innovations, with some citations and some deep scholarly reference.
Smart Healthcare Engineering Management and Risk Analytics
AvXueyan Li,Luyue Zhaom. fl.
E-bok
Engelska, 2022
732 kr
Läs direkt efter köp
This book aims to stay one step beyond the innovations of information and communication technologies and smart healthcare management and provides an overview of the risks smart healthcare management could help to alleviate, and those risks it would create or amplify. Inclusive discussions of the core of smart healthcare services in the perspective of system engineering are enclosed, such as smart healthcare definition, data information knowledge service, and intelligent hospital management. Summaries of technological and theoretical innovations spanning each step of the modern healthcare system are included, from health screening, clinical diagnosis, cancer screening, to in-hospital mortality monitoring, minimally invasive surgeries, and medical data storages. Analytics of risks reduced and induced by these innovations are provided, with potential solutions to such risks in healthcare management discussed. This book seeks to provide demonstrative examples of incidence capable innovations of healthcare technologies, which, while greatly enhancing abilities of healthcare workers and institutions, could pose risks to patients and sometimes even greater threats to the integrity of the healthcare system. The style of the book is intended to be demonstrative but most suited for researchers and graduate students, explaining the methodology behind healthcare innovations, with some citations and some deep scholarly reference.
Smart Healthcare Engineering Management and Risk Analytics
AvShuai Ding,Desheng Wum. fl.
Häftad, Engelska, 2023
566 kr
Skickas inom 10-15 vardagar
This book aims to stay one step beyond the innovations of information and communication technologies and smart healthcare management and provides an overview of the risks smart healthcare management could help to alleviate, and those risks it would create or amplify. Inclusive discussions of the core of smart healthcare services in the perspective of system engineering are enclosed, such as smart healthcare definition, data information knowledge service, and intelligent hospital management. Summaries of technological and theoretical innovations spanning each step of the modern healthcare system are included, from health screening, clinical diagnosis, cancer screening, to in-hospital mortality monitoring, minimally invasive surgeries, and medical data storages. Analytics of risks reduced and induced by these innovations are provided, with potential solutions to such risks in healthcare management discussed. This book seeks to provide demonstrative examples of incidence capable innovations of healthcare technologies, which, while greatly enhancing abilities of healthcare workers and institutions, could pose risks to patients and sometimes even greater threats to the integrity of the healthcare system. The style of the book is intended to be demonstrative but most suited for researchers and graduate students, explaining the methodology behind healthcare innovations, with some citations and some deep scholarly reference.
Complex Social Economic System Modeling 2
Application
AvDesheng Wu,Minfeng Xum. fl.
Inbunden, Engelska, 2026
1 107 kr
Kommande
The book adopts a technology-driven, application-oriented, and interdisciplinary approach to study social complex systems. It centers on the development of a simulation platform: first, it builds core technical supports; then, it integrates key functional modules; finally, it applies the platform to multiple real-world fields to solve practical problems through simulation. This approach combines theoretical modeling with technical implementation and scenario application, forming a "theory-technology-application" closed loop.The book’s content covers three main dimensions, with a broad and systematic scope:Technical foundation: Design and implementation of the simulation platform, including no-code modeling environment, high-performance parallel simulation engine, and core functional modules.Field applications: Simulation research on typical social complex systems, such as global supply chain resilience, energy transition socio-technical systems, financial market systemic risk, urbanization and land-use change, and online social network information diffusion.Theoretical and forward-looking discussion: Review of theoretical foundations of social complex systems, research framework construction, and prospects for simulation platforms as social science infrastructure, along with ethical and governance issues.The book targets a multi-disciplinary audience, including: 1) Researchers and graduate students in social sciences and technical fields Practitioners in policy-making and enterprise management who need simulation tools for decision support; 3) Developers and technicians engaged in simulation platform design and application.
Financial Fraud Detection Using Machine Learning
AvXiyuan Ma,Desheng Wu
Inbunden, Engelska, 2025
1 683 kr
Skickas inom 10-15 vardagar
This book serves as a comprehensive guide to learning various aspects of financial fraud, encompassing the related research, the current situation, potential causes, implementation process, detection methods, regulatory penalties and management challenges in publicly listed companies. In this book, readers learn about the fraudulent practices that may occur in corporate operations, the executing mechanisms, an identifying indicators framework, and diverse detection methods including qualitative and quantitative models. Quantitative models include discriminant analysis, econometric analysis, and machine learning (ML) models. This book highlights the application of ML algorithms to detect financial fraud detection and discusses their limitations, such as high false-positive costs, delayed detection, the demand for interdisciplinary expertise, dependency on specific application scenarios, and issues with fraud data quality. Each related chapter provides a structured overview of the problems addressed, the algorithms used, experimental result and comparisons. Additionally, this book examines the cost-benefit trade-offs faced by companies engaging in financial fraud, considering factors such as ethical dilemmas, opportunities, practical needs, exposure risks, and litigation costs. This book is written for financial regulation institutions, business leaders, auditors, academics, and anyone interested in financial fraud detection. It offers practical insights into effectively preventing and controlling financial fraud and an overview of the latest advancements in ML technologies. Through real-world case studies, readers will gain a deeper understanding of the financial fraud, how ML can be used to detect it, as well as its pitfalls and limitations. Overall, this book bridges the gap between theory and application, equipping readers to understand how to detect financial fraud with the power of accounting and ML in the modern business environment.
Financial Fraud Detection Using Machine Learning
AvDesheng Wu,Xiyuan Ma
E-bok
Engelska, 2025
2 105 kr
Läs direkt efter köp
This book serves as a comprehensive guide to learning various aspects of financial fraud, encompassing the related research, the current situation, potential causes, implementation process, detection methods, regulatory penalties and management challenges in publicly listed companies. In this book, readers learn about the fraudulent practices that may occur in corporate operations, the executing mechanisms, an identifying indicators framework, and diverse detection methods including qualitative and quantitative models. Quantitative models include discriminant analysis, econometric analysis, and machine learning (ML) models. This book highlights the application of ML algorithms to detect financial fraud detection and discusses their limitations, such as high false-positive costs, delayed detection, the demand for interdisciplinary expertise, dependency on specific application scenarios, and issues with fraud data quality. Each related chapter provides a structured overview of the problems addressed, the algorithms used, experimental result and comparisons. Additionally, this book examines the cost-benefit trade-offs faced by companies engaging in financial fraud, considering factors such as ethical dilemmas, opportunities, practical needs, exposure risks, and litigation costs. This book is written for financial regulation institutions, business leaders, auditors, academics, and anyone interested in financial fraud detection. It offers practical insights into effectively preventing and controlling financial fraud and an overview of the latest advancements in ML technologies. Through real-world case studies, readers will gain a deeper understanding of the financial fraud, how ML can be used to detect it, as well as its pitfalls and limitations. Overall, this book bridges the gap between theory and application, equipping readers to understand how to detect financial fraud with the power of accounting and ML in the modern business environment.

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Desheng Wu – författare

New Frontiers in Enterprise Risk Management

New Frontiers in Enterprise Risk Management

New Frontiers in Enterprise Risk Management

Enterprise Risk Management Models

Enterprise Risk Management Models

Enterprise Risk Management Models

Predictive Data Mining Models

Predictive Data Mining Models

Predictive Data Mining Models

Predictive Data Mining Models

Predictive Data Mining Models

Smart Healthcare Engineering Management and Risk Analytics

Smart Healthcare Engineering Management and Risk Analytics

Smart Healthcare Engineering Management and Risk Analytics

Complex Social Economic System Modeling 2

Financial Fraud Detection Using Machine Learning

Financial Fraud Detection Using Machine Learning

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