Social network analysis - A new weapon in the fight against credit card fraud

By Françoise Soulie Fogelman

When police raided 84 separate addresses across Europe, the US and Australia one day in June and arrested 178 individuals, it was the conclusion of a two-year investigation into credit card fraud and related crimes that had netted more than €18.5 million. But when taken against a card fraud total of €5.3 billion in 2009 according to the Nilson Report, the gang’s efforts were just a drop in the ocean.

And as over-the-counter transactions involving credit cards get progressively more secure, the focus of criminals is increasingly the online world. Yet current detection and prevention mechanisms tend to be too slow to effectively fight this kind of fraud, often not highlighting problems until a bad card has been used several times.

For a long time credit card fraud detection has been rooted in data analysis techniques first developed in the 1980s, mostly with systems built around neural networks and decision trees.

While these can work adequately for cardholder present transactions where volumes are relatively low, they have not translated well into the online world where transaction volumes are much higher and speed of authorisation is everything. Nor do they spot the links between frauds that give away organised crime.

But now a new analytical technique, social network analysis, has entered the war against credit card fraud. Augmenting the already established detection methods, it promises to help lower significantly the risk of card fraud.

Instead of mining historical transaction to detect fraud, social network analysis instead maps the complex networks of relationships that exist between frauds, cards, cardholders, transactions, merchants, industry sectors, phone numbers etc. If two or more of these network nodes share something in common - for example the same card used at two different merchants - then a connection exists. Add many thousands of nodes, plot all the connections between them, and soon patterns emerge that can help reveal fraud.

For example, two cases of fraud may involve different cards and cardholders but the same telephone number or email address may have been used to make the fraudulent purchases. Further investigation may reveal other common factors with other cards (nodes) in the network and previously isolated groups - or islands - of fraud suddenly come together and start to make sense. It is being able to visualise these networks with hundreds or even thousands of nodes that is crucial to fraud detection.

When multiple node types enter the equation - for example cards and merchants - there are potentially many millions of nodes in the network. The task of mapping then becomes massively more difficult, as does navigating around them. The use of highly automated software tools can greatly ease the process and make results available in the minimum possible time.

Leading the field in social network analysis is the data mining automation vendor KXEN. Its social network analysis software module, KSN, is well proven with international telecommunications operators in combating customer churn. Now the technology is behind an initiative to fight online credit card fraud with a major European financial consortium.

Early results show great promise and single figure percentage reductions in fraud are already being projected. With the scale of the crime as large as it is, even a small reduction can add up to tens or hundreds of millions of Euros saved.

Better still, the results of social network analysis can be used to augment the findings of the traditional analytical techniques used in detecting fraud. This can add significantly to the efficiency of an existing predictive model in determining which transactions are likely to be fraudulent.

As it is now acknowledged that the large majority of credit card fraud is committed by organised gangs - something which social network analysis is most effective at detecting - the case for social network analysis becomes all the more compelling.

Biography

Françoise Soulie Fogelman is VP Strategic Business Development, KXEN and has 30+ years experience in data mining and CRM, first as a professor directing research into neural networks. She then co-founded an OCR business, started the Data Mining and CRM group at Atos Origin then - before KXEN - ran a business intelligence and CRM company.