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.
Author(s): Eduardo Rodriguez
Publisher: CRC Press/Auerbach
Year: 2023
Language: English
Pages: 482
City: Boca Raton
Cover
Half Title
Title Page
Copyright Page
Dedication
Contents
Author
Introduction
1. Fundamental Concepts
1.1 All organizations require risk analytics
1.2 Evolution of risk analytics
1.3 Risk analytics is a crucial concept in risk management processes
1.4 Enterprise risk management (ERM)
1.5 Measuring, metrics, and business concepts
1.6 Understanding data, scales, and data arrays
1.7 Basic probability concepts useful in risk analytics
1.8 Examples of stochastic processes
1.9 Compound Poisson distribution
1.9.1 Distribution Function
1.9.2 Distribution Function
1.9.3 Mean and Variance
1.10 Understanding Linear Algebra
1.11 Traditional models in finance
1.12 RAS: Hybrid system of human and artificial intelligences
2. Risk Management, Modeling, and Analytics Processes
2.1 Risk management and modeling processes
2.2 Risk modeling and risk knowledge development
2.2.1 Risk Knowledge Creation
2.2.2 Risk Knowledge Storage/Retrieval
2.2.3 Risk Knowledge Transfer
2.2.4 Risk Knowledge Application and Learning
2.3 General analytics process for risk management
2.4 Exploratory data analysis (EDA)
2.4.1 Illustration Example of EDA-Data Visualization
2.5 Data sources and data sampling in risk analytics
2.6 Data partition: Training, test, and validation data
2.7 Inference and generalization in risk analytics
2.8 Method of the moments
2.9 Maximum likelihood estimation
2.10 Estimators by optimization
2.11 Risk analytics road map
2.11.1 A View of Combining Marketing Risk and Credit Risk
2.11.1.1 Multivariate Analysis
2.11.1.2 Clustering Using k-Means
3. Decision Making Under Risk and Its Analytics Support
3.1 About general tools for supporting decision making
3.2 Benchmark approach for defining metrics
3.3 Steps to start the definition of metrics
3.4 Creating clusters of companies based on their returns
3.5 About risk indicators in a multivariate approach
3.6 Comparing risk metrics in groups
3.7 Decision making on investments in a risk prescriptive analytics approach
3.8 Forecasting and time series as a means to support decision making
3.8.1 Forecasting Models for Stationary Time Series
4. Risk Management and Analytics in Organizations
4.1 How to start a risk measurement system
4.2 Default rates, probabilities of default, and risk scales
4.2.1 Graphical Review of Loss Distribution Fitting
4.3 Exposure and default rate construction
4.4 An illustration of default rate calculation
4.5 Example of particular metric: Probable maximum loss
4.6 Adapting metrics to risk conditions: Banks illustration
4.6.1 Metrics in a Bank Setting
4.7 Exposure management in supply-chain risk
5. Tools for Risk Management
5.1 Prevention, resilience, and prediction
5.2 Risk analytics knowledge of return
5.3 Value of the firm, credit decisions, and intangibles in organizations
5.4 Assets and returns
5.5 Risk analytics and the analytics of return
5.6 General model for financial assets valuation
5.7 The equivalence concept
5.7.1 Equivalence Between a Future Sum and a Series of Uniform Sums
5.7.2 Special Treatment of Annuities
5.7.3 Finite Annuities with Arithmetic Progression Change
5.7.4 Principles of Loan Amortization
5.7.5 Basics of Investments, Cash Flows, and Transactions Analysis
5.8 Return analysis in stock markets and bonds
5.8.1 Return Analysis for Stocks
5.8.2 Return Analytics in Bonds
5.8.3 Price of a Bond Between Coupon Payments
5.8.3.1 Callable Bonds
5.9 Return and Term Structure of Interest: Spot and Forward
5.9.1 Spot and Forward Rates
5.9.2 Sensitivity Analysis of Interest Rates
5.9.2.1 Duration
5.9.2.2 Convexity
5.9.2.3 About Immunization
5.10 Metrics in a portfolio
5.11 Analytics of products to deal with risk
5.11.1 Interest Rate Swaps
5.11.2 Forward Contracts
5.11.3 Futures Contracts
5.11.4 Options
5.11.4.1 Parity Between Put and Call Options
Black and Scholes Model
5.11.4.2 American Options
5.11.4.3 Valuation
6. Data Analytics in Risk Management
6.1 How to use technology, and store and manage structured and unstructured data
6.2 Technology in the RAS design
7. Machine and Statistical Learning in Risk Analytics
7.1 Managing models
7.2 Basics of measurement to create groups
7.3 Models, validation, testing, and performance
7.4 Risk classification: Relationships and predictions
7.5 Search of relationships among variables using generalized linear models
7.6 Modeling risk unit
7.7 Mixed models
7.8 Logistic regression
7.9 Correspondence analysis
7.10 More about multivariate tools
7.10.1 Discriminant Analysis
7.10.2 Artificial Neural Networks (ANNs), Deep Learning, and Tensorflow
7.10.2.1 Structure of the ANN
7.10.3 Analysis Based on Trees
7.10.3.1 Additional Non-Parametric Analysis
7.10.4 Ensembles - Bagging - Boosting and Stacking
7.10.4.1 Bagging - Bootstrap Aggregating
7.10.4.2 Boosting
7.10.4.3 Stacking
7.11 Beyond classification related problems
7.11.1 Analysis of Variance and Its Components
7.11.2 Misclassification Problem
7.11.3 Migration of risk levels with Markov's model
8. Dealing with Monitoring the Risk Analytics Process
8.1 Possible barriers to create a risk analytics system (RAS)
8.2 Factors affecting the organizations' RAS
8.3 Digging deeper in RAS components
8.4 Creating a RAS and the key risk indicators analysis
9. Creation of Actions and Value
9.1 Possible bases of RAS design
9.2 Framework for the risk knowledge management component
9.2.1 Knowledge Management Processes
9.2.2 Knowledge Management System (KMS)
Implementation and Change of Plans
Lessons Learned and Future Steps
9.3 Creating risk knowledge through estimating metrics using simulation
9.4 Portfolio decisions
9.5 Analyzing pro-forma financial statements
9.6 About new products as combination of risk
9.7 About factors affecting a loss distribution (LGD)
9.8 Risk analytics contribution to LGD analysis
9.9 LGD learning processes
9.10 Classification and LGD
References
Index
