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Big Data, analytics and fraud prevention

19/10/2015

We are all acutely aware that the amount of data that organisations use and store has been growing exponentially over the past few years. Much emphasis is being placed on the potential rewards of unlocking valuable information from such data volumes – the so-called promise of Big Data.

However, the associated potential risks for companies that may be buried in these voluminous datasets are clearly growing too.

Big Data is largely untagged file-based and unstructured data, about which little is known. This means not only that large quantities of potentially useful data is getting lost, for example data which could allow a consumer goods company to draw insights on consumer behavior and generate greater value from marketing campaigns, but that fraud, bribery and corruption, money laundering and other white collar criminal activity may remain either very difficult to detect or, even worse, undetected because it lies buried in the morass of data.

A company that does not have sufficiently robust internal controls and security measures is at risk of both internal and external perpetrators leveraging the proliferation of data to their advantage in order to hide fraudulent transactions, improper payments, layering of funds and even “forge” electronic documentation in support of illicit acts. Equally, we find that companies with systems are un-integrated or poorly integrated are unable to effectively identify compliance breaches such as the payment of bribes, procurement fraud, money laundering, etc. as they have no means of analysing the complete transaction flow or to identify anomalous or unauthorized payments.

Data mining combines data analysis techniques with high-end technology for use within a process. The more data sources can be mined, analysed and tested, the more likely anomalies will be detected and/or alerts raised. Further, we find that companies that take an holistic approach and are... continued on page two >

 

 

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