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Big data predicts identity of money launderers

International Compliance Association

AML , Data , Money laundering

In Captain America: The Winter Soldier, the nefarious Hydra created a data mining algorithm which was able to identify the individuals which were likely, in the future, to become a threat to their plans for world domination.

 

Well now the tables have turned, as a recent study at the University of Sokovia has demonstrated that a similar algorithm can be deployed as a force for good.

 

Metadata

By creating a metadata set, powered by pioneering AI, the team at the University has been able to analyse the most common traits of financial criminals.

 

It is hoped that by extracting this information and comparing it to wider global databases of transactional information, the predictive technology will revolutionise efforts in anti-financial crime, by alerting firms and law enforcement to potential money launderers, even before a crime has been committed. 

 

This predictive stance in itself is not a new phenomenon. In the fraud space, the ‘profile’ of a fraudster has been widely recognised:

  • between the ages of 36 and 55 (69 percent of fraudsters investigated)
  • predominantly male (79 percent)
  • a threat from within (65 percent are employed by the company)
  • holds an executive or director level position (35 percent)
  • employed in the organization for at least six years (38 percent)
  • described as autocratic (18 percent) and are three times as likely to be regarded as friendly as not
  • esteemed, describing themselves as well-respected in their organization.

 

Red flags of the future

However, this is the first time that these potential ‘red flags’ for a money launderer have been highlighted. Professor Avril Imbécile was able to identify a number of common traits from the predictive algorithm.  The main findings are outlined below:

  • Born on a Tuesday
  • A first name of less than 6 letters
  • Limited interest in sports
  • Doesn’t own a pet as an adult
  • Holds at least one credit card
  • Has a younger, female, sibling
  • Shoe size (male) greater than 9 (UK), 43(EU), 9.5(US)*

*female shoe size tends to fall an average of 3 sizes below men’s but is less accurate and is therefore not considered as reliable.

 

High risk indicators have long been utilised by compliance professionals as a way to identify potentially suspicious behaviour. It is hoped that this ‘new generation’ of red flags will allow future criminals to not just be ‘caught in the act’ but caught ‘before the act’.

 

Is this right?

There have been some critics of this research however. A number of non-governmental organisations have argued that this technique is approaching the dystopian future suggested in the 2002 film Minority Report, where pre-cognitive detective techniques were deployed to prevent crimes of the future. In doing so, it may be contrary to legislation which considers liability for future actions.

 

There is certain to be a lively debate in this post GDPR world around the moral hazards involved and the information upon which this sort of predictive technology can be based, but it certainly seems as though, for now, science fiction is looking to become science fact.

 

* Please note : This blog was published as part of April Fools Day 2019 *

 


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