Kathleen Long is using a combination of behavioral analytics, Bayesian engineering and big data to help companies better determine and mitigate business risk.
Kathleen Long (Montage Analytics), interviewed by Renee Boucher Ferguson.
A socio-cybernetician and behavioral scientist, Dr. Kathleen Long, CEO of Montage Analytics, is taking on operational risk the big, unwieldy, nearly undetectable kind known as black swans (and the perhaps more well known type of operational risk too, like risky business practices or employee fraud). Dr. Long has combined her training in behavioral science with Bayesian network design to help organizations better understand and mitigate operational risk
This isn’t an easy undertaking. Part of the problem, according to Dr. Long, is that not everyone knows how to define operational risk (if you can’t define it, you can’t guard against it). At the same time, the risk landscape is changing so fast that what happened yesterday is no longer a marker for what might happen tomorrow.
“We are living in unprecedented times. You can’t say the past is a reliable predictor of the future,” says Dr. Long. “Too many events happen that are black swans that come out of the blue Bernie Madoff, 9/11. Things have changed dramatically. And the fact that we are living in an increasingly globalized and networked world means that even small events happening somewhere on the other side of the world can quickly cascade around the globe and affect us in places we didn’t expect.
In a conversation with Renee Boucher Ferguson, a researcher and contributing editor at MIT Sloan Management Review, Dr. Long discussed the changing risk landscape and how big data and behavioral science can help.
2 Comments On: Risky Business: How Data Analytics Can Help
Great content. Thanks
It is a pity that so many Risk Managers are not well versed in the new advantages that technology can bring to the discovery and mitigation of Risk.
Do “black swan” risks like “Bernie Madoff” and “9/11,” as cited in this post, really “come out of the blue?”
If you read the history of actions associated with these events, there were pre-event risk indicators that were noted but never acted upon from a leadership and authority perspective. The available data and information did not become actionable insights.
Ms. Long makes excellent points when she states: “Data-based analytics have limitations in terms of their utility in understanding risk that involves humans. And the actual ironic thing is that operational risk is all about people. It’s caused by people. It’s people who make mistakes, who get greedy, who are inexperienced. And it’s people who design the systems that sometimes fail, and the processes that sometimes don’t work. So at the core, it’s about people.”
Effective risk management is not just about people. Ultimately, it comes down to leaders taking meaningful actions to mitigate risks. This is alluded to when Ms. Long states, “Oftentimes what these risks come down to is a disincentive to mitigate risk because of the cost. [Leaders] make the decision that a catastrophe is probably unlikely: I don’t think it’s going to happen; we could use the money elsewhere; our numbers were down last quarter; I’ve got to impress my boss — whatever.”
It would have been great to have seen more emphasis on this aspect of risk management, e.g. what’s the reaction when Ms. Long’s company tells corporate management that, based on their analysis, employees are reluctant to surface risk issues to management?
Additionally, it’s also important to remember that effective risk management is a direct reflection of risk preparedness. Risk preparedness involves the institutionalization of processes and activities designed to create a culture that mitigates risks that are part of its business processes and enables effective and efficient rescue actions when things go wrong. See http://withintheslipstream.blogspot.com/2012/06/effective-risk-management-prepares.html for a further discussion on this last point.