People Risk Management

A Practical Approach to Managing the Human Factors That Could Harm Your Business

Keith Blacker, Patrick McConnell

• Editorial: Kogan Page
• Año: 2015
• ISBN:
  9780749471361
• Edición: 1
• Categoría: Commerce
• Categoría: Management

People Risk Management provides unique depth to a topic that has garnered intense interest in recent years. Based on the latest thinking in corporate governance, behavioural economics, human resources and operational risk, people risk can be defined as the risk that people do not follow the organization's procedures, practices and/or rules, thus deviating from expected behaviour in a way that could damage the business's performance and reputation. From fraud to bad business decisions, illegal activity to lax corporate governance, people risk - often called conduct risk - presents a growing challenge in today's complex, dispersed business organizations. Framed by corporate events and challenges and including case studies from the LIBOR rate scandal, the BP oil spill, Lehman Brothers, Royal Bank of Scotland and Enron, People Risk Management provides best-practice guidance to managing risks associated with the behaviour of both employees and those outside a company. It offers practical tools, real-world examples, solutions and insights into how to implement an effective people risk management framework within an organization.

04

Case studies in People Risk

A few lucky gambles can crown a reckless leader with a halo of prescience and boldness. DANIEL KAHNEMAN¹

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KEY MESSAGES

• People Risk events occur within organizations at multiple levels with varying likelihood of occurrence and unpredictable consequences when they do.
• People Risk events occur because of bad decisions arising from individual and group biases and conflicts of interest.
• People Risks are apparent at every level from a simple incident/accident to a whole industry.
• People use common rationalizations to explain their actions and mistakes.

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4.1 Summary of cases

The cases described in this chapter are many but nonetheless are only a fraction of those that could be covered in this book. These cases were chosen because they cut across industries, countries and organizational types, although all involve large corporations. The losses run from the almost risible, such as the reputational damage in the HBOS statement case, to untold misery following the Global Financial Crisis. But they have one thing in common – they were caused by people, not natural events. They occurred on the frontline, such as in the BP Gulf of Mexico oil spill, and in the boardroom, such as the failure of RBS or the disastrous business strategies of Tesco and Lehman Brothers.

The cases also illustrate the fickleness of human nature, as corporate heroes are showered with praise one day only to be castigated as fools the next. But as Professor Barry Turner² noted perceptively, disasters are rarely caused by one person no matter how senior, but instead are caused by the biases and inaction of many, who should have known and done better. The failure of one firm at one time may be passed off as merely due to bad management or, if generous, bad luck. However, massive losses in or even the collapse of so many hitherto successful organizations must raise the question, are there common factors in these cases and if so what are they? The answers lie deep within each one of us, particularly in our inability to see ourselves as being less than perfect. Humility not hubris is the key to averting such disasters.

Table 4.1 summarizes the cases discussed in this chapter using the 4Is Model described in section 4.1 below. It summarizes the losses incurred and describes the main rationalizations discussed in Chapter 3.

TABLE 4.1 People Risk – summary of case studies

Case	Description	Losses	Rationalizations
INCIDENTS
HBOS – mistaken bank statement	Delivery of 75,000 statements	Reputation – embarrassment	Mistake – unknown operator
Air New Zealand – Flight 901	Fatal air crash	257 deaths	Miscommunication and lack of situational awareness
Medical fatality – Elaine Bromiley	Medical emergency	One death	Experts’ blind spots, and lack of situational awareness
INDIVIDUALS
Bank fraud – Ms Rajina Subramaniam	Fraud	AUD $40 million	Just Desserts – revenge for perceived abuse
Rogue trading – Barings, AIB, etc	Fraud	Various	Temporary Use – covering up for losses
Medical fatalities – Dr Harold Shipman	Patient murders	> 215 deaths	Unknown – possible addictive personality
Fraud – JPMorgan and Bernie Madoff	Failure to report fraud	US $2 billion fine	Distrust of Law
HBOS – banking fraud	Fraud	Unknown > £35 million	Economic Necessity, Just Desserts
Insider trading – Goldman Sachs	Fraud	£118 million	Distrust of Law and Victimless Crime
INSTITUTIONS
Royal Bank of Scotland	Strategic overreach	Acquired by UK Government	Ubiquity, Distrust of Law
Enron	Fraud – corporate	Bankruptcy	Distrust of Law, Victimless Crime
WorldCom	Fraud – corporate	Bankruptcy	Temporary Use, Distrust of Law
Washington Mutual	Bad lending	Bankruptcy	Ubiquity, Distrust of Law
HBOS	Bad lending	Acquired by Lloyds Bank	Ubiquity, Economic Necessity
Co-operative Bank	Strategic overreach	Acquired by Private Investors	Economic Necessity
JPMorgan – The Whale	Trading losses	US $6 billion	Economic Necessity, Distrust of Law
HSBC	Money laundering	US $1.9 billion fine	Ubiquity, Distrust of Law
Eli Lilly	Misselling drugs	US $1.4 billion fine	Distrust of Law, Victimless Crime
BAE Systems	Bribery	£280 million fine	Distrust of Law
Siemens AG	Bribery	US $1.3 billion fine	Distrust of Law
NHS – Mid Staffordshire	Excess deaths (unknown number)	Forced administration	Economic Necessity, Distrust of Law
RIM – Blackberry	Strategic overreach	Forced acquisition	Economic Necessity
Tesco plc	Strategic overreach	> £1 billion	Economic Necessity
Lehman Brothers	Strategic overreach	Bankruptcy	Economic Necessity, Ubiquity
BP – Gulf of Mexico oil spill	Death and pollution	US $4 billion ++	Economic Necessity, Ubiquity, Distrust of Law
Fukushima nuclear plant	Nuclear accident	Unknown	Economic Necessity, Distrust of Law
INDUSTRY
Big Tobacco	Unsuitable products	Unknown	Ubiquity and Distrust of Law
New Zealand tax	Tax avoidance	NZ $2.5 billion	Ubiquity, Distrust of Law, Victimless Crime
PPI	Misselling products	£18 billion	Ubiquity, Distrust of Law, Victimless Crime
IRHP	Misselling products	£2.5 billion	Ubiquity Distrust of Law, Victimless Crime
Auto parts	Price fixing	US $2.5 billion fines	Ubiquity, Economic Necessity, Victimless Crime
LIBOR	Market manipulation	> US $4 billion fines	Ubiquity, Distrust of Law, Victimless Crime
Global Financial Crisis (GFC)	Misselling	US $6–14 trillion	Ubiquity, Distrust of Law, Victimless Crime

4.2 The 4Is Model

In considering fraud or errors, especially those that lead to serious losses, researchers tend to look to the organization or the system as the primary cause for disasters. It is not that they ignore the role of the individual but argue that on their own it is difficult for one individual or even a small group of individuals to cause a major loss or create a major disaster. While difficult, it is not, however, impossible and the higher and more entrenched an individual or small group of individuals are in an organization the greater the potential damage can be. It is true that there have to be pre-existing latent conditions³ that provide the opportunity for creating large losses. But in order to create a significant disaster it takes the power of thousands of individuals in multiple organizations or institutions across an industry. Figure 4.1 shows the 4Is model⁴ that juxtaposes the likelihood of losses occurring against the consequences if they do, at four different levels:

• Incident: in every business situation there will be many incidents that, as Frank Bird and others showed, are mainly ‘near-misses’ but can sometimes produce losses, including fatalities.
• Individual: in every business there may be many individuals who may perpetrate a fraud or make a serious error, but in general, management and financial controls in an organization/system and follow-up prosecution will tend to minimize but, unfortunately, not eliminate resulting losses.
• Institution: most institutions/firms will make losses from time to time but every now and then an institution will fail with disastrous consequence for shareholders, as with Enron, and even for the global economy as in the case of Lehman Brothers.
• Industry: it is at the level of an industry that the consequences of People Risk can be greatest, such as, for example, the costs to the shareholders of banks as a result of the PPI scandal in the UK.

FIGURE 4.1 The 4Is Model

In the 4Is model, it is at the level of an Industry that the greatest losses do occur. For example, US economists Tim Curry and Lynn Shibut,⁵ have estimated that the Savings and Loan (S&L) crisis of the 1980s cost the US taxpayer at least $150 billion. The term S&L refers to the many thousands of small banks that provided basic lending and savings services to local customers and dominated the US banking system until massive consolidation of the system in the 1990s as a result of this S&L crisis. Before and just after the Second World War, S&Ls were an integral part of life in the United States as typified by the fictitious Bailey Building and Loan Association in the classic film⁶ It’s a Wonderful Life, which showed what could happen if such a trusted institution were to fail. The S&L crisis was precipitated by changes in the late 1970s in the laws that deregulated the so-called ‘thrift’ industry, in effect giving S&Ls the same privileges as banks but without the same levels of regulation.

In terms of Turner’s model, introduced in Chapter 2, the ‘Precipitating Event’ for the S&L disaster was a series of interest rate rises engineered by the US Federal Reserve to dampen inflation in the US economy. These rises meant that S&Ls, which tended to give out fixed rate mortgages, were suddenly faced with increased funding requirements. In effect, like several banks that failed in the GFC, their assets did not match their liabilities and many S&Ls failed. But the losses were compounded by the fact that, freed by the new regulations, the boards of several large S&Ls had been lending recklessly, chasing profits and, for some, personal gain whereas previously they had sought safety and security. In some S&Ls there had also been rampant fraud, particularly control frauds where creative accounting was used by individuals to deceive shareholders into believing that an S&L was solvent when, in fact, it wasn’t.

In the same way that Bird, Cressey, Perrow and Turner argued, as described in Chapter 2, that one must look beyond individuals to organizations as the source of mistakes, errors and frauds, one must also look beyond Institutions to the Industry as the source of potentially even larger losses. This chapter is organized in line with the 4Is model and describes case studies of loss events at each level starting from the bottom – Incidents. Many, but not all of the cases are taken from the financial services industry, which is the one in which major financial losses have occurred in the last decade. Importantly, unlike other private corporations, firms in the financial services industry are highly regulated and therefore exceptional events tend to be the subject of intensive inquiry, analysis and opportunities for learning. It should also be noted that within each section the cases are o...