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Using Big Data for KYC and AML efforts is elementary!

Big Data elevates KYC, AML efforts. Why not? What's next?

Business and personal and governmental histories are littered with examples of decisions that were made without using data which was available with a little digging (or analysis, or software spend) which always generates a facepalm in retrospect. I (and many others) have highlighted the lingering and incredible disconnect in problems to be solved versus the lack of comfort with and trust in the assets to solve them. The tide is shifting, as, for example, we have heard of how Big Data (in the guise of digitization of texts and graphic recognition) has become an invaluable tool in diagnosing diseases, especially cancer, well in advance of previous abilities, opening up extremely promising fronts in medicine. I see a Nobel Prize awarded for use of this technology before long.

But, you say, the article is supposed to be about Know Your Customer and Anti-Money Laundering...what parallels exist with cancer research? My friends, if you manage an affected financial services business and do not pay attention to KYC, AML, OFAC and other requirements, the resultant problems will spread like a cancer across all levels of the enterprise, resulting in incredible monetary and reputational losses.

So, what to do? Look at it this way. Evildoers and malefactors typically create patterns as they try to infiltrate otherwise law-abiding organizations, so that they can achieve their nefarious goals. Those patterns could and should be thought of as fingerprints, as books and articles to be read and keywords to be harvested, as pictures to be studied (think x-rays or even galaxies out in space, the ongoing depictions of which are yielding incredible insights about the development of the universe.), as puzzles to be solved. Remember, like hackers, those seeking to misrepresent themselves or launder/hide money and assets will come up with new tactics as soon as old ones have been blocked/detected. sot he work will be ongoing.

To partner with the BigData ecosystem in highlighting problems your financial services firm may face with illicit transactions and fictitious customers you'll need to know exactly your specific situation. You should have been in this position all along, though, right? Good data/data management and poor knowledge of your specifics is comparable to having bad data and complete comfort with  your own situation. Personally, though, I would rather have the data and data management properly locked in and have to spend the time and effort at internal studies than the other way. Cleaning up a data mess is never pretty or easy and always costs big money.

Think of risk and personal profiles, typical timing of and sizes of cash flows and transactions, characteristics of associated businesses, of anything that just might smell bad. When you go to buy paint, the clerk can create just about any shade by mixing other shades in very specific amounts. Same here. The profile of a bad guy, which can and will change over time, is established by looking at the data regarding previous moves by bad guys. Different types of bad guys will have different profiles, different mixes of paint shades, but given the complexity of the activities, a firm needs high-end data management and data analytics to establish and codify the patterns to make it all much easier to follow. 

I am personally excited at how much more efficient and effective a firm's regulatory efforts can be by not just responding to incidents, but by using Big Data to identify and head off bad guys before they do damage. Sherlock Holmes would suggest that this is completely elementary (and isn't it interesting that his partner was 'Watson'.)

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Comment by Richard Ordowich on September 14, 2015 at 7:00am

More often than not, the "bad guys" hide in plain sight. They operate legitimate business, are often philanthropists and pillars in their community and well known to the financial industry. Petty criminals maybe exposed using data but the major criminal activities are rarely discovered through data. Informants are usually the cause of their discovery.

The consequences of fraud to those institutions who are regulated are minimal and the compensation so large, they remain resolved to give the appearance of compliance. Discovering galaxies and medical diagnosis are easier than discovering fraud. Nature doesn't try to hide but waits to be discovered. Fraud on the other hand is not bound by nature, only by human behavior. Algorithms to analyze human behavior are naïve when compared to the creativity of humans. Greed is not an algorithm.

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