May 11, 2016
I was one of the many who lost a weekend to the most recent season of House of Cards back in March. As always I was captivated by the evolution of the Underwoods, the underlying complexity and intrigue buried beneath the gleaming smiles and handshakes; but this season had another gem for me, a data analytics scientist at the heart of one of Frank’s schemes.
While I was excited that Data Science (D.S.) and analytics creeping into pop culture, I was concerned about the perception of analytics, particularly given the context of the House of Cards. Like science in general, D.S. is objective and impartial; the morality lies in the application. Ethics in data analytics science is a vast subject in itself, one which I’d love to explore further, but it’s a topic for another blog.
I’m fortunate in my job as I don’t face much moral ambiguity. I focus on Financial Crime, helping both public and private organisations combat fraud, money laundering, and terrorist financing. The world is evolving rapidly, and it’s not only the good guys who innovate, but it’s also a never-ending battle as criminals probe for vulnerabilities, and the real guys look to close the gaps. Over the past decade, advances in technology have allowed businesses to enhance the customer experience, for example by expanding channel interactions and making tasks that were previously very time to consume much more convenient. This innovation is fantastic. However, it can expose cracks that can be exploited by fraudsters.
So what about big data? Doesn’t that make it easier to find the bad guys? That’s one perspective, but it also creates more places to hide and more noise for the good guys to sift through; and this can mean that by the time you’ve found the pattern, the criminals are long gone.
By borrowing data analytic techniques from statistics and time series analysis (old school), graph theory (getting newer), and even topology (I was a little surprised) we can increase our ability to detect anomalous behaviours – moving away from the traditional methods that are often retrospective. This reduces the amount of noise and allows us to focus on what matters, limiting our exposure to criminals.
In evaluating financial crime through this new lens, if we invest wisely in the right skills and technology we can deploy and execute these techniques, find the fraudsters, dismantle money launderers, limit terrorist financing and most importantly help to make the world a better place.
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