For many financial firms, the tyranny of the regulators can be somewhat deadening to the creative juices of risk managers everywhere. As we have seen, although firms have been following Basel requirements for operational risk, there has hardly been a stemming of the tide of losses. If anything, there has been an acceleration in their occurrence.

As we have seen, the tools required under Basel are inadequate, on their own, for both tasks of measuring and managing the risk that financial institutions now carry. In the face of a seemingly unending supply of large losses, CEOs are turning to their risk colleagues with exasperation: Why did you not see this coming? How do I know what's next? In addition to changing the organization to empower all employees to be risk managers, new tools need to be put in their hands so that organizations can avoid rabbit holes and see around corners.

Putting People to Good Use and Avoiding Rabbit Holes

As any good doctor will tell you, prevention is better than a cure. While it is possible to improve the tools available to identify risks, and we will discuss these, would it not be preferable to stop the risk activity from occurring in the first place? Clawbacks and other methods to adjust bonus and pay based on negative surprises are just part of a sea change that is very much needed in the way employees are compensated on Wall Street.

To be successful in reducing operational risk requires behavior change. For years, the assumption has been that the primary way to motivate employees is financially. Research, however, by the school of Positive Psychology, of which Martin Seligman is a key proponent,¹ has shown that there are other paths to happiness that tap into deeper parts of the human psyche and bring higher levels of motivation. Fulfilling employees' drives for leading engaged and interconnected lives will yield better results over the long term. If motivation can be found in ways other than providing a narrow set of financial rewards, then potentially ways can be found to reduce the temptation in employees to cheat.

The human resources model used by banks has, of course, been traditionally focused on rewarding traders and lines of businesses for profitable deal making and trading. There are good reasons for tinkering with this model from a risk management perspective, as we have discussed in prior chapters. However, there are other reasons for changing the emphasis. First, not that such desires can ever really be satisfied, but the gates have been closing on large bonuses due to a combination of shareholder, regulatory pressure, and declining profits. If firms make it just about profitable trading, then traders will most likely leave. Second, how is the performance of those who are not trading or making deals but working in other important functions such as risk and compliance to be measured when they cannot point to a tangible dollar amount in sales or trading profits? Developing a solution to this is critical when their role is increasingly important. Third, based on Seligman's and others' work, there are other ways to more effectively motivate your workforce.

It is worth asking why people still work in these jobs. For many, it is because they are actively engaged in the intellectual effort required to execute their day‐to‐day work. The happiest and most fulfilled employees, according to Seligman, are identifiable by their level of expertise, years of experience, and ability to focus on work that engages them intellectually. It is also these employees who are the most valuable because their ease with the work is such that they can focus the bulk of their brain power on those problems that present genuine quandaries and judgment calls: the problems that require real thought. Confronted with complex risk issues, a less practiced and less fluent risk manager will spend too much of his thinking time coming to terms with understanding the issue and not enough on addressing it. Understanding this, one would hope that firms would place a premium on experience and expertise in making its decisions about the types of employees to attract and retain. However, it is not always clear that, with the repeated waves of layoffs, firms have been placing a premium on such attributes. With the vanishing of skilled, experienced, and engaged employees, total residual risk probably goes up. Does this help to explain the increase in cost of operational risk incidents in the past few years? Maybe.

It is also true that with the impact of regulation and their requirements, traditional financial firms and investment banks have been unable to keep their employees engaged in the work they want to do, as opposed to bureaucratic tasks they have to do. When considering the reasons for employees leaving banks to join hedge funds, one can't ignore the draw of fewer bureaucratic demands. But banks should still do as much as they can to ensure that job design maximizes employee engagement. Bored employees are risky employees, either because they will take unnecessary risks or, more likely, because they will not be on the lookout for risk.

If employees don't get sufficient fulfillment from engagement with their work, there is still a third source of happiness to tap—that of being part of and contributing to a wider community. Again, building a sense of community is not something that investment banks have done too much of over the years but there are signs that this is changing. Morgan Stanley, for instance, with its 75th anniversary campaign “Morgan Stanley and You,” made a concerted effort to strengthen the sense of the community and its history in employees. Other things such as community involvement month, pro bono business planning, employee photography competitions, and charity runs suggest similar efforts. It is not just banks that struggle with this. In a different industry, Yahoo's CEO's call to employees to spend more time in the office is also a tacit recognition of the need for people to have to meet, greet, and work face to face.² Banks have a version of this problem, as they have increasingly created physical separation between traders and functions such as financial control and operations. As one hears the complaints of COOs regarding the downgrade in service they receive following such a change, one wonders if the inherent need for interconnectedness can so easily be compensated for by email and telephone contacts, and if this impacts work quality and relationships.

There are many Wall Street employees, tired and battered by recent events as they might be, who still go to work each day fired up and ready to meet new challenges. There are also many who are not, and by taking into account the insights of positive psychology firms could have a positive impact on those employees; first, by de‐emphasizing the bonus and money culture; second, by designing work and jobs to engage the intellect of employees; and third, by placing employees within a context of a broad community of like‐minded people. Lastly, in thinking through layoffs, leaders of groups need to consider levels of engagement and interconnectedness of employees as factors in determining their decisions. Leaving gaps in knowledge and community is not the best approach in the long term. This is all, of course, without forgetting to remind people of the positive impact of their work on that community and the broader society.

There are other tools firms can leverage to change behavior in a more direct way. First, clawbacks and metrics around compensation could be one effective tool for promoting such a culture in investment banks but require much greater transparency and consistency around their application if they are to become so. Are clawbacks to be applied, for example, for inappropriate or risky behaviors or for trades that in the short term were profitable but in the longer term were not? Will traders be given two or three chances before action is taken by management to activate a clawback? Without a clear message on what is acceptable and what is not, it is unlikely that behaviors will be modified. Also, why not reward employees and their businesses for identifying risks and preventing losses from growing or occurring? Firms that build such metrics into their talent management model will build a solid risk management culture and drive behaviors in the right direction.

Recent research has indicated a growing disparity in income within society generally, and this disparity is also played out within the typical Wall Street firm. People can be told that there are no bonuses to be paid out because the business has not performed but when this is not seen to impact the financial rewards of the most senior executives, that has a noticeably negative impact on employee motivation. Making equity a priority in compensation makes a lot of sense if employee motivation is to be improved.

Putting Data to Good Use

Everyone is talking about the use of big data these days, and so now is a good time to reflect on the potential uses of big data by different industries and policy makers to solve some of their longstanding issues. Here we look at how banks and regulators can use the principles of big data to solve problems like how to identify traders who are taking undue risks or investment salespeople who are fronting a Ponzi scheme.

First, banks should leverage data to expose the objective reality of different traders' performance. Coaches in baseball, memorably portrayed in book and film in Michael Lewis's Moneyball, have mined data to expose when long‐held views about the value of certain types of players do not coincide with the reality.³ Mining trade data could similarly be done to challenge views about the value and consistency of different traders. It would be very useful to counteract with actual data the halo effect bestowed on occasion on certain traders for past heroic trading exploits. Such heroism, achieved by successfully taking high levels of risk in a tough market, is often rewarded by supervisors with latitude to take greater risks. In certain cases, such latitude can be disastrous. A disciplined data‐driven approach would serve to assess traders' performance on a more objective basis relative, for example, to contextual factors such as amount of risk taken relative to reward, performance of market benchmarks, and volatility of returns over a longer‐run period. Such data, by providing a more objective basis for performance assessment, would enable better calibration of pay, risk limits, and trader mandates and would lay bare the reality behind a trader's reputation, which may or may not have been fairly earned. Solid data analysis of ongoing performance should help to separate out myth from reality and help to prevent encouragement of excessive risk taking.

Objective data analysis of the type that might enable managers to identify the truly high and consistent performers is hard to do, however, when the data upon which it is based is bad. How to evaluate the true performance of Lance Armstrong, when we now know he artificially inflated his performance by taking performance enhancing drugs.⁴ This takes us to the second use of big data: identifying manipulative or cheating patterns of behavior. Now this is a field of great promise because, underlying many banks' top risks, are patterns of behavior that are hard to detect but that can lead to disaster. Identifying the hallmarks of such patterns would be a major advance. Let's look at three areas: Rogue Traders, insider traders, and fraudulent investment schemes.

The Rogue Trader, of course as has been demonstrated several times, can work at the margins for several years. He typically starts off by taking relatively small unauthorized risks, and generates profits at first that are set aside in a nonactive account to smooth out, through interaccount transfers, emerging as losses in the main trading account. As the losses grow, the trader is forced to put on riskier, larger positions, all unbeknownst to management and supervisors. His patterns of behavior include: certain cash transfers between accounts; trades with certain possibly fictitious counterparties; trades with unusual settlement periods; large numbers of canceled trades; and failure to take vacation. These patterns, however, appear as isolated data points in a sea of daily processed data that includes thousands of other, benign, data points. Developing an ability to draw out the patterns, to connect the dots between these different behaviors, can help filter out the risky behaviors from the benign.

Insider traders work across a network of contacts and activities to execute trades in anticipation of an event that will give rise to a lift or a dent in the stock price. Any types of trades that fall outside the normal domain or expertise of a trader that are executed ahead of a market announcement should be fertile ground for analysis.

Finally, big data could be part of an enhanced tool set to catch the people behind the next Ponzi scheme. The Commodities Futures Trading commissioner, after the failure to identify the fraudster behind the Peregrine Ponzi scheme, has talked about mining the data of futures brokers—this could include patterns of asset transfers, for example—to become more effective in policing segregated customer assets.⁵ There are other tools with wide applications and of different origins that can be put into the hands of risk managers, investors, and those who conduct due diligence on traders and investment managers. Analysis of speech patterns by James Pennebaker of Texas University, for example, showed that liars tend to use more upbeat words like “pal” and “friend” but fewer excluding words like “but,” except,” and “without.”⁶ Risk factor analysis brings together these different data points to create predictive capabilities, both in the measurement and the management sense.

Can access to this type of knowledge help those who are seeking to identify potential fraudsters and Rogue Traders? Perhaps. These are just three areas that could be advanced for risk management purposes with effective data mining tools and techniques. Will banks and regulators succeed in making such advances, or will we be hearing about the next Madoff, Adoboli, or Iksil in 12 months' time? Only time will tell but it is certainly worth a try. Cognitive technologies provide a whole new class of tools and a new platform to start to make these possibilities a reality. That will be explored further in Chapter 21.

Putting Psychological Insight to Use

Psychology and heuristics can also offer insights that can be helpful in assessing risk events. Errors and failures to escalate to management may at times be a function of various cognitive biases and limitations. By understanding better these intellectual limitations of ourselves and our colleagues, we can find additional ways to manage risk. We will draw on the work of psychologist Daniel Kahneman in considering this question.⁷

First, Kahneman talks about the substitution effect, explaining that “when faced with a difficult question, we often answer an easier one instead, usually without noticing the substitution.”⁸ From time to time, we are all faced with difficult decisions that may require us to completely rethink the decisions we have made in life or in business or simply to confront uncomfortable realities. It is a human tendency that is worthwhile being acquainted with.