Red flags are used in all sorts of professions as a way to generalise from one known context to another, hopefully, similar context. They are a useful introduction for policy-makers, other non-specialists or entrants to a profession. They are a short-cut for hard data analysis and, in the anti-money laundering (AML) arena, a poor substitute for Know Your Business (KYB) and Know Your Customer (KYC). The crime story below has features that should resonate with compliance and AML experts: massive complex datasets, suspicious activity, intelligence reports, added value from law enforcement and, of course, transfers and purchases. It also has a happy ending, so we can reflect on lessons learned from real-world success.
Years ago, when intelligence-led policing was still in its infancy, I was posted as a Detective Inspector to a borough on the edge of London. I was tasked to identify burglars and thereby reduce the rate of burglaries, which was running at 400 per month and rising. The borough had about 200,000 properties. The number of burglars was unknown.
To be clear burglary (or home invasion for the American reader) is a predicate crime for money laundering. It is also, as this tale will illustrate, an organised crime. It generates finance for the burglar and supplies the pre-owned market, therefore it is also an economic crime. These italicised crime categories, that attempt to separate types of crime are one of the main reasons that we are losing the war on dirty money. This divisive terminology literally divides policing resources until they become ineffective. It clouds our analysis so that we work in crime-category siloes. Problems that could be solved collectively, don’t get solved at all because vital data is separated out and put in other siloes. If we are to solve economic, organised, financial and predicate crime we need to approach them holistically and pool our resources. Proponents of any one of these categories are making it more difficult for others to solve their crime problems and, ironically, depriving themselves of vital resources to solve their own. The Financial Action Task Force, the global AML standard-setter says that theft (and therefore burglary) is a predicate crime and therefore relevant to AML compliance.
In AML language, a successful burglary involves a transfer of property from the home-owner to the burglar and, at some later point, one or more sales and therefore purchases of second-hand property. The purchaser of the property could be a money launderer if they knew or should have known, the true circumstances. From a police perspective, the burglars and the house-owners are both ‘customers’ (as in KYC).
On arrival at my new posting to a London borough, I asked my Intelligence Unit who they thought were committing the burglaries. They explained that there had not been any recent arrests of burglars in the borough, but they had some ‘red flag’ theories. They had heard that some second-hand shops to the south of the borough were purchasing goods ‘no questions asked’. They also concurred with the commonplace Metropolitan Police view that burglars, vehicle thieves and robbers came from the same general cohort, young men between the ages of 15 to 21, sometimes acting in small groups. They had consulted with the police borough to the north and officers there thought it was possible that northern criminals were also committing crimes in the southern borough. They noted that the northern borough had a population that included a significant minority of young black males, whereas the southern borough was very predominantly white. The Intelligence Unit also noted that their borough was a commuter suburb with significant bus, train and road routes running across their ‘manor’ from the north to south.
They explained to me that their strategy was to advise officers to look out for young males, including black ones, who were en route from north to south across the borough, especially if they were carrying property. This strategy, to date, had not been successful in identifying burglars. In AML terminology, they were using red flags, without data analysis or KYC and the merest dusting of KYB (the borough had roads).
As the ‘newbie’ manager I tried a number of different approaches, which later became known as “Intelligence Led Policing” (ILP). The best technique proved to be analysis of the transfers (the burglaries) over time and location. Like all data analysis there were difficulties, the exact time of the transfer was not known, it just occurred between two known times. The transfers had qualitative differences; a shed is different from a house, a garage or a commercial building. Analysis showed that many of the transfers (burglaries) were likely to occur in family houses, with back gardens with alleys running behind the back fence. The transfers were believed to occur in the afternoons when the occupiers were out collecting children from school. Painstaking analysis identified specific places where and when a burglary was likely to occur. These became briefings (rather like ‘suspicious activity reports’) for operational police officers.
Before long this contextual analysis, led operational officers to arrest a burglar. Furthermore, he confessed to a hundred other local burglaries. The Intelligence Unit and the operational officers gained confidence that the new approach could work. The new data was given to the Intelligence Unit’s analyst and a virtuous circle was created. This led, over the next quarter, to the arrest of five further burglars, who were similarly prolific, organised, offenders. It transpired that all six burglars were white, lived in the borough and committed offences within walking distance of where they lived. They were all aged between 25 and 35 and were each feeding a rapacious heroin addiction. A rising tide of burglary – and the accompanying human misery and fear – was halted and reversed. The burglary rate halved. Over subsequent years the burglary rate (along with other crimes) halved again, an intelligence-led virtuous circle of effective policing had been created. Police readers will have a lot of questions about this; fear not, it will be fully explained in a future book.
Let’s recap to answer Phil Mason’s question about the alternative to red flags in the AML world. In the example above, the red flag approach to finding the transfers was entirelywrong. It had, perhaps, resulted in resources chasing red herrings and actually prolonging the crime wave. Success was achieved by dumping red flags and doing KYB, KYC and contextual analysis. The KYB analysis, that led the officers to suspicious activity in alleyways, was supplemented by their feedback (the successful arrest of prolific burglars). The arrests themselves improved the KYC (you really get to ‘KYC’ if you arrest them)! It was time-consuming, difficult and successful, the previous strategy was time-consuming, difficult and a complete waste of everyone’s time.
The revelation about the cause of burglaries impacted positively on the whole borough. The business of policing, much like any other business, is based on the staff gathering relevant data. Equipped with contextual analysis and KYC information the whole police station – patrol officers, detectives, their managers and supporting staff were all actively gathering data that could help. A collective example of Know Your Business.
You could argue that the new data about burglars being local, white, over 25 and motivated to commit crime by heroin addiction, are just new better ‘red flags’. I had initial sympathy with this view, but it does not stand up to analysis. To use these new red flags to solve a burglary problem you would need to identify all the people in the borough matching these criteria. This would be many thousands of young white men, but how do you identify the heroin addicts? How do you distinguish the many heroin addicts who successfully manage their addiction from those whose recourse is crime? Only contextual analysis can answer these questions. A red flag approach would be enormously time-consuming and doesn’t provide the answers you need to be successful.
An alternative would be to add the data to a contextual analysis of the market in second-hand goods. In policing, this means contextual analysis of purchases through surveillance, financial intelligence and other covert techniques. In AML, it means contextual analysis of purchases through transaction monitoring, KYB and KYC.
It turns out that both suspicious transfers and suspicious purchases at the core of money laundering are to be found through ‘Know Your Customer’ and ‘Know Your Business’. Red flags have their place as described above, but not for effective AML and its regulation.
If you are interested in finding out more please read Chapters 4 and 9 of The War on Dirty Money.