How diversity is a boon to risk management
I have written before about the risk of financial services executives who have too much faith in their own abilities. But there is perhaps a more dangerous bias existing in us all that can harm risk-taking institutions.
There is at least some narcissism in everyone. But a bias toward oneself can also extend to how we think of other people. We are quite clearly predisposed to favor those who look and act like we do. Psychologists continue to examine these predispositions as underlying reasons for gender and race biases. But narcissism biases can go much further. A recent episode of the “Hidden Brain” podcast discussed how we are drawn to other people and things that merely have some tangential relationship to our lives. Like the original Narcissus, who fell in love with his own reflection, we all have a tendency to do the same. We are friends with people who share the same name, birthday and hometown, while we do not necessarily examine our motivation for this love. Recently, my daughter explained to me another reason why she loves the actress Emma Watson: her half birthday falls on the same as Watson’s full birthday. This partly explains why my daughter interprets every statement and behavior by Watson in the most positive way possible.
Why does this matter for financial services companies and other businesses? First, a narcissistic bias can be reflected in the way that teams hire and promote. As managers scan resumes, their eye is drawn to resumes that reflect their own background, whether it is the school that they attended, common names or that they both worked at a prior company. I have often remarked on how many colleagues I now have from the same prior firm. Apparently, this is no coincidence. How the narcissism bias can affect hiring decisions and which colleagues are brought together is in some ways similar to, but a less conscious version of, the concept originating in Great Britain of the “old boys network.”
The same bias may be reflected in whom we promote through the company. The most obvious types of this bias is when we favor colleagues and job candidates of our own race or gender, but there are other examples as well. This can lead to decisions to promote people who are not competent or worthy of the selections.
Secondly, for regulators, compliance officers and risk manager officers, personal bias felt towards certain other individuals may spill over into decisions to prosecute or litigate behaviors for potential wrongdoing or risky activities. This is similar to actions that get criticized in urban policing, when law enforcement is said to make arrest decisions influenced by “unconscious bias” against people based on skin color or neighborhood. Would a risk management specialist afford more discretion to someone, guilty of an indiscretion, if that person comes from a similar background? Maybe someone might say, “Please give him another chance. He’s a good guy.” But is he a good guy, or do we feel he deserves another chance because the common characteristics we share make us think he is inherently good? The risk is that due diligence is short-changed and discretion is given to somebody who is not worthy of it.
This type of bias not only can lead to poor general decisions on hiring and promotions, but also can also impact the internal decisions of compliance or risk management staff. Financial institutions should be particularly alert to how self-serving biases can lead to individuals being placed into situations that can put the organization at greater risk, such as on a trading desk. For regulators and stakeholders, the concern is that examiners are not objectively scrutinizing institutions if their contacts reflect their own backgrounds. These risks can also come up when investment managers are deciding which companies to add to an investment portfolio, when determining whom to lend to, and with the selection of a money manager to oversee investments large and small.
The case of Bernie Madoff was a classic example. Did Madoff cement the trust of investors and escape scrutiny for so many years because he appeared to have an untarnished Wall Street pedigree — consistent returns, affiliations with good causes and connections with people of influence? Was it because he appeared to be “one of them.” The danger of over-reliance on trusted stereotypes is that you might be turning a blind eye to danger. That is clearly what happened with Madoff.
Another scenario I have witnessed is a type of inner-layer personal network within an investment bank or trading floor that operates in a way that potentially makes its activities opaque to management. In such a scenario, a trader who has developed a strong reputation among managers is given latitude to hire and build out a trading team. The trader will hire and promote analysts and other traders who appear to reflect similar, familiar qualities.
In a hypothetical example, let’s say the head trader is from a foreign country, where he or she attended school, and hires friends, former classmates and even family — all from the same background. The team would have certain positive performance qualities, such as strong internal communications and teamwork. But the team appears to others to be like a clique. It may be harder for risk managers and supervisory staff to pin down exactly what they are working on; communication may be a challenge too because the team could be switching between languages in their communications. Furthermore, if the head trader hires people because they reflect a shared cultural background, it means the trader may be overlooking other candidates with stronger abilities for the job. All of this can lead to problems from a risk management perspective, especially if the team is handling higher-risk trades with a lot of money at stake.
To combat these risks, diversity within teams and the institution as a whole is essential. The implication of the Madoff episode means we must recognize our own biases in order to avoid using the “he’s a good guy test” when making investment or risk management decisions. A diverse team of analysts from different backgrounds can help to address this concern. Prioritizing objectivity and protecting against outliers in new data analysis and artificial intelligence tools is also helpful.
If we fail to become aware of and examine narcissist biases, we may miss out on top talent as well as open the door to risky behavior, both which are antithetical to the long-term interests of the institution.