Written by Will Aitkenhead
Posted on 19/02/2015

Fraud prevention, starring big data analytics

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Fraud accounts for approximately five percent of lost revenue in the average organisation. Assessing the dangers to themselves and their clients is without question the biggest challenge for risk managers. But with the help of big data analytics there is hope that the impacts of fraud can be minimised and maybe even eradicated completely.

The perils of traditional fraud detection

Prevention and detection is the target for all companies of any size or stature, but around 50 percent of fraud is found by accident when a loss has already happened. Fraud can cause reputational damage that often far outweighs the loss of revenues, as customers lose faith in the company’s ability to protect their assets.

An increase in access channels and transaction volumes has made it significantly more difficult to cope with fraud. Tried and trusted methods of prevention such as behaviour monitoring, network analysis, pattern recognition and profiling have been struggling to keep pace with the technological advancements of the fraudsters. But there is an answer.

Changing the game with big data

Big data can be used to unearth patterns, trends and more general associations and connections of human behaviour, and it is now an essential tool to prevent fraud. Five years ago such technology didn’t exist but now we can use it to our advantage with a multi-faceted analytic approach.

Big data means companies and banks can have real time access to analytics on a large scale, helping them to produce effective risk management strategies to fit financial crime. The heart of the process involves focussing on behavioural profiling and then detecting the unusual when it occurs. It needs to be flexible, but a successful system will be able to detect and even predict suspicious scenarios before it’s too late.

In terms of banks, this real time prevention can be disruptive for customers, which is why Pactera is one company trying to develop a big data solution that can detect fraud without disrupting service levels. SAP is another, with real-time detection tools and predictive methods helping to optimise fraud-scenario assessment and lower the risk.

Who needs protecting most?

Big data is helping many businesses across a number of different areas, but when it comes to fraud prevention it’s industries like the financial services, governments and healthcare that are most at risk. This is because these industries are so data-intensive that their huge pools of data can sometimes mask any fraud that takes place.

Many of them already use analytics to assess sales patterns, and this data can have the dual purpose of detecting any strange customer activity. This can be as simple as a change in a customer’s normal connection point (e.g. phone or desktop computer) or average transaction amount.

Big data analytics can also be used to prevent internal fraud, by assessing previous fraud occurrences and drawing together the likely scenario that leads to internal fraud.

Return on investment

The critical benefit of real-time fraud detection is a reduction on revenue losses, which will mean a return on investment. This is at the heart of all business plans and it is no different when it comes to fraud. For years now the fraudsters have had their way but it seems that finally the risk managers have the tools to successfully fight them.

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