Data analysis using big data tools for financial crime prevention
Author(s): Robyn Norfolk, Posted on June 28th, 2012
As financial institutions allow access to services via new channels, the risk of new attack vectors increases. Mobile banking, for instance, introduces a new set of devices used to access customer accounts. While offering new possibilities for business growth, new channels also offer opportunities to the criminal community. In 2011, Juniper Networks reported a 155% increase in malware across all mobile platforms and a number of UK institutions have already asserted significant losses to fraud in the short period of time their mobile banking products have been available.
Data analysis will have been used to determine the initial set of financial crime management (FCM) solution rules by detecting the correlation between financial crime and attributes of the transaction, or series of transactions. This data analysis will have occurred against data relevant to the particular channel. However, research and experience show that having created a set of rules to detect financial crime, one cannot rest on one’s laurels. External fraudsters, for example, are nothing if not persistent, and given the size of the rewards, will spend a lot of time and effort in trying to circumvent an institution’s defences. Money launderers will go to extremes to hide the true source of their funds and internal fraudsters will exploit any procedural weakness in order to reap their rewards.
However, Big Data tools make it easier for data analysts to use a more diverse set of data sources, enabling not only the discovery of new channel-specific financial crime typologies, but also cross-channel, cross-product and cross-customer crime. Big data isn’t necessarily a panacea – there has to be IT involvement in making the data sources available – and, of course, big data tools don’t remove the need for data cleansing. But it does give new opportunities to institutions looking to tackle financial crime via new means.
Our paper – Using big data analytics to fight financial crime – discusses the opportunity for financial crime departments to better target limited IT resources in the fight against financial crime using relatively low cost commodity solutions.