Article by Emil Eifrem for Professional Security Magazine Online
UK businesses lose just under a hundred billion pounds every year to fraud, according to a report by the University of Portsmouth’s Centre for Counter Fraud Studies and accountancy firm PKF Littlejohn (https://www.pkf-littlejohn.com/the-financial-cost-of-fraud-2015.php).
Think about it: that’s the cost of Trident, per year, draining out of businesses. Is there anything that can be done about that waste and criminality? While no procedures are 100 per cent foolproof, the same study found that fraud can be reduced by up to 40 per cent if the right security measures are put in place. One of the key ways of doing this is to look beyond the individual data points to the connections between them – joining the dots to uncover any suspicious patterns amongst a deluge of uninteresting transactional data.
Unfortunately these intricate pictures too often go under the radar. But if we could spot this malicious behaviour early enough, that would be a huge aid in terms of preventing or rapidly shutting down fraud. Doing that, though, is never a simple case of dot-to-dot. Making connections between the ‘dots,’ i.e. the data, is hard. The good news is that, thanks to technology, meaningful insight can be mined from these complex datasets – like customer banking or online credit card transactions – detailing new connections and hidden patterns to build up a picture previously invisible.
That new technology that delivers this vision is the graph database. Unlike other ways of managing data, graph databases were developed to express data relationships, doing this by uncovering patterns notoriously difficult to identify using relational databases/SQL tables. As a result, a growing number of enterprises, from banks and financial institutions to online retailers, are adopting graph databases to solve a variety of data problems.
This is especially true in the fight to beat criminal activity, especially around fraud.