How financial firms can detect and prevent fraud by using network analytics

The proliferation of internet banking and digital sharing have made it easier than ever for fraudsters to attack financial institutions and organisations must use sophisticated methods to prevent financial crime and risky activities. 

In a new blog post by Quantexa chief product officer Imam Hoque highlighted the key challenges facing financial companies when encountering fraudsters.

From international credit card scams to false insurance schemes, fraud has become the most commonly experienced crime in the UK. With the multi-faceted nature of fraud, it cannot be tackled using a one-size-fits-all approach, Hoque said. Alongside being financially detrimental to companies and their customers, fraudulent activity also involves the laundering of money used to fund human trafficking, terrorist activity, nuclear proliferation and drug dealing. Furthermore, the advent of the internet has irrevocably changed the nature of fraud. He added that with the excessive amount of data shared online, it has become easier for fraudsters to harness such information for their own gain. “Detecting fraud is not an exact science, as one problem often morphs into another,” he wrote.

Indeed, investors are increasingly pumping in money towards RegTech companies and fraud detection firms.

According to Hoque, one of the reasons which is causing an increase in such cases is that current fraud prevention practices focus on isolated and linear approaches to monitoring. These outdated systems are being systematically abused by criminals to transfer criminal profits around the world. For instance, a traditional approach will look at an individual application for lending and might ask questions such as, ‘is the applicant under the age of 25?’ and ‘do they have a salary greater than £100k?’. “Whilst this may identify someone who is inflating their salary, it will also identify many applications which are not fraudulent,” Hoque explained.

Even today, a number of banks tend to use outdated, preconfigured systems that look at just a thin slice of data. Fraudsters can capitalize on this by systematically testing the thresholds of the systems through trial and error, and as a result, become familiar with the parameters. They can then systematically attack an organization, gaining access to large amounts of money.

Clearly, a new approach is needed that allows organisations to gain a better understanding of which behaviour is potentially criminal and which is legitimate. “This involves positioning entities and transactions within a wider network to draw connections between various activities that would otherwise not be seen,” he wrote.

Detailing on using context and analytics to detect fraud, Hoque said that techniques such as entity resolution and network analysis allow systems to make associations between applications, accounts, and people that otherwise would have gone unnoticed. This approach looks at all the information available rather than a single piece in isolation – in doing so, the risks are better understood.

Hoque added that methods such as network analytics have consistently helped organizations to understand the workings of a criminal and to make connections that would otherwise have gone unnoticed. He concluded, “by understanding the bigger picture and thinking like a criminal, we can limit the successes of these fraudsters and prevent them from striking again.”

Read the blog post here.

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