REPORTING REQUIREMENTS ON STATISTICAL DATA ON FRAUD UNDER PSD2 PROMPT BANKS TO RETHINK FRAUD INTERVENTION

Steve Morgan, Managing Director of UK/Europe at Intelenet® Global Services, says banks can partner with external digital experts to tackle the problem of fraud

Today the European Banking Association closes its consultation on draft guidelines for reporting fraud under revised PSD2. Banking fraud is one of the most challenging issues facing the industry, with more than 5,000 cases of fraud reported every day in Britain in the first half of 2017.[1]

According to research from Barclays’, a third of British adults have fallen victim to a scam, with an average theft of £893.[2]  The global banking sector is continuously updating its defences against all types of financial fraud, with criminals using increasingly sophisticated tactics to access personal or security data.

Customers seek the convenience and speed offered by digital banking, but fear exposing themselves to fraudsters who will steal information to gain rapid access to accounts. Technologies such as machine learning and predictive analytics can identify customers’ spending patterns and flag up suspicious transactions.

Steve Morgan, MD of UK/Europe at Intelenet® Global Services, comments: “Risks are shifting with every new digital banking innovation. As a result of this, banks are turning to digital means to reduce fraud. Technologies which provide interactive messaging combining speech synthesis and voice recognition, can help banks reach customers quickly and efficiently to determine if a suspect transaction is taking place.  This can significantly lower average loss per account and loss per fraud.

“One leading bank saw a 50 percent reduction in anti-money laundering alerts and a 98 percent increase in fraud detection rate after being advised by a digital expert on how to best manage risk. This can be achieved through digital profiling, which examines customer data available from an existing information source, alongside determining how data can be stored and changed.

Steve continues:  “Fraud aftercare is crucial in allowing banks to identify loopholes in the system and pick up the pieces for customers after criminal activity has taken place. Machine learning is increasingly being applied to help speed up the resolution process, resulting in improved customer support for those who have fallen prey to fraud.”

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