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A practical guide to adopting an accurate risk analysis methodology


The Failure of Risk Management provides effective solutionstosignificantfaults in current risk analysis methods. Conventional approaches to managing risk lack accurate quantitative analysis methods, yielding strategies that can actually make things worse. Many widely used methods have no systems to measure performance, resulting in inaccurate selection and ineffective application of risk management strategies. These fundamental flaws propagate unrealistic perceptions of risk in business, government, and the general public. This book provides expert examination of essential areas of risk management, including risk assessment and evaluation methods, risk mitigation strategies, common errors in quantitative models, and more. Guidance on topics such as probability modelling and empirical inputs emphasizes the efficacy of appropriate risk methodology in practical applications.


Recognized as a leader in the field of risk management, author Douglas W. Hubbard combines science-based analysis with real-world examples to present a detailed investigation of risk management practices. This revised and updated second edition includes updated data sets and checklists, expanded coverage of innovative statistical methods, and new cases of current risk management issues such as data breaches and natural disasters.





Identify deficiencies in your current risk management strategy and take appropriate corrective measures

Adopt a calibrated approach to risk analysis using up-to-date statistical tools

Employ accurate quantitative risk analysis and modelling methods

Keep pace with new developments in the rapidly expanding risk analysis industry



Risk analysis is a vital component of government policy, public safety, banking and finance, and many other public and private institutions. The Failure of Risk Management: Why It's Broken and How to Fix It is a valuable resource for business leaders, policy makers, managers, consultants, and practitioners across industries.

The Failure of Risk Management: Why It's Broken and How to Fix It

About the Author xi





Preface xiii





Acknowledgments xvii





Part One An Introduction To The Crisis 1





Chapter 1 Healthy Skepticism for Risk Management 3





A "Common Mode Failure" 5





Key Definitions: Risk Management and Some Related Terms 8





What Failure Means 14





Scope and Objectives of This Book 17





Chapter 2 A Summary of the Current State of Risk Management 21





A Short and Entirely-Too-Superficial History of Risk 21





Current State of Risk Management in the Organization 25





Current Risks and How They are Assessed 26





Chapter 3 How Do We Know What Works? 35





Anecdote: The Risk of Outsourcing Drug Manufacturing 36





Why It's Hard to Know What Works 40





An Assessment of Self-Assessments 44





Potential Objective Evaluations of Risk Management 48





What We May Find 57





Chapter 4 Getting Started: A Simple Straw Man Quantitative Model 61





A Simple One-for-One Substitution 63





The Expert as the Instrument 64





A Quick Overview of "Uncertainty Math" 67





Establishing Risk Tolerance 72





Supporting the Decision: A Return on Mitigation 73





Making the Straw Man Better 75





Part Two Why It's Broken 79





Chapter 5 The "Four Horsemen" of Risk Management: Some (Mostly) Sincere Attempts to Prevent an Apocalypse 81





Actuaries 83





War Quants: How World War II Changed Risk Analysis Forever 86





Economists 90





Management Consulting: How a Power Tie and a Good Pitch Changed Risk Management 96





Comparing the Horsemen 103





Major Risk Management Problems to Be Addressed 105





Chapter 6 An Ivory Tower of Babel: Fixing the Confusion about Risk 109





The Frank Knight Definition 111





Knight's Influence in Finance and Project Management 114





A Construction Engineering Definition 118





Risk as Expected Loss 119





Defining Risk Tolerance 121





Defining Probability 128





Enriching the Lexicon 131





Chapter 7 The Limits of Expert Knowledge: Why We Don't Know What We Think We Know about Uncertainty 135





The Right Stuff: How a Group of Psychologists Might Save Risk Analysis 137





Mental Math: Why We Shouldn't Trust the Numbers in Our Heads 139





"Catastrophic" Overconfidence 142





The Mind of "Aces": Possible Causes and Consequences of Overconfidence 150





Inconsistencies and Artifacts: What Shouldn't Matter Does 155





Answers to Calibration Tests 160





Chapter 8 Worse Than Useless: The Most Popular Risk Assessment Method and Why It Doesn't Work 163





A Few Examples of Scores and Matrices 164





Does That Come in "Medium"?: Why Ambiguity Does Not Offset Uncertainty 170





Unintended Effects of Scales: What You Don't Know Can Hurt You 173





Different but Similar-Sounding Methods and Similar but Different-Sounding Methods 183





Chapter 9 Bears, Swans and Other Obstacles to Improved Risk Management 193





Algorithm Aversion and a Key Fallacy 194





Algorithms versus Experts: Generalizing the Findings 198





A Note about Black Swans 203





Major Mathematical Misconceptions 209





We're Special: The Belief That Risk Analysis Might Work, but Not Here 217





Chapter 10 Where Even the Quants Go Wrong: Common and Fundamental Errors in Quantitative Models 223





A Survey of Analysts Using Monte Carlos 224





The Risk Paradox 228





Financial Models and the Shape of Disaster: Why Normal Isn't So Normal 236





Following Your Inner Cow: The Problem with Correlations 243





The Measurement Inversion 248





Is Monte Carlo Too Complicated? 250





Part Three How to Fix It 255





Chapter 11 Starting with What Works 257





Speak the Language 259





Getting Your Probabilities Calibrated 266





Using Data for Initial Benchmarks 272





Checking the Substitution 280





Simple Risk Management 285





Chapter 12 Improving the Model 293





Empirical Inputs 294





Adding Detail to the Model 305





Advanced Methods for Improving Expert's Subjective Estimates 312





Other Monte Carlo Tools 315





Self-Examinations for Modelers 317





Chapter 13 The Risk Community: Intra- and Extra-organizational Issues of Risk Management 323





Getting Organized 324





Managing the Model 327





Incentives for a Calibrated Culture 331





Extraorganizational Issues: Solutions beyond Your Office Building 337





Practical Observations from Trustmark 339





Final Thoughts on Quantitative Models and Better Decisions 341





Additional Calibration Tests and Answers 345





Index 357