Top Stories
Annual Fraudscape report reveals record number of identity frauds
Published by Gbaf News
Posted on April 25, 2018
4 min readLast updated: January 21, 2026
Global Banking and Finance Review
Company
Published by Gbaf News
Posted on April 25, 2018
4 min readLast updated: January 21, 2026
Fraud prevention service Cifas recently published its annual Fraudscape report, citing that there were 174,523 cases in 2017, which is the highest number of identity frauds ever recorded.[1]
The data provides one of the most comprehensive pictures of fraud and fraudulent attempts made in the UK.
The global banking sector is continuously updating its defences against all types of financial fraud, with criminals using ever-more sophisticated tactics to access personal or security data.
As reported by The Evening Standard, non-cash payment racked up more than 522.4 billion transactions worldwide, highlighting the customer need for convenience and speed offered by digital banking, despite the risk of exposing personal information to fraudsters.[2]
To counteract sophisticated fraudulent attempts, banks are using technologies such as machine learning and predictive analytics to identify customers’ spending patterns and flag up suspicious transactions.
Bhupender Singh, CEO of Intelenet® Global Services, comments: “With fraud scams expected to skyrocket in 2018, banks are turning to technologies which provide interactive messaging, by combining speech synthesis and voice recognition. This can help banks reach customers quickly and accurately determine if a suspect transaction is taking place.
“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 accomplished through digital profiling, which examines customer data available from an existing information source, alongside determining how data can be stored and changed.
Bhupender continues: “But often forgotten is the need for fraud aftercare. It is in fact a crucial aspect in allowing banks to identify gaps in the system and assist customers after criminal activity has taken place. Machine learning is increasingly playing a key part in speeding up the resolution process, which in turn, improves customer support for those who have been victims of fraud.”
Fraud prevention service Cifas recently published its annual Fraudscape report, citing that there were 174,523 cases in 2017, which is the highest number of identity frauds ever recorded.[1]
The data provides one of the most comprehensive pictures of fraud and fraudulent attempts made in the UK.
The global banking sector is continuously updating its defences against all types of financial fraud, with criminals using ever-more sophisticated tactics to access personal or security data.
As reported by The Evening Standard, non-cash payment racked up more than 522.4 billion transactions worldwide, highlighting the customer need for convenience and speed offered by digital banking, despite the risk of exposing personal information to fraudsters.[2]
To counteract sophisticated fraudulent attempts, banks are using technologies such as machine learning and predictive analytics to identify customers’ spending patterns and flag up suspicious transactions.
Bhupender Singh, CEO of Intelenet® Global Services, comments: “With fraud scams expected to skyrocket in 2018, banks are turning to technologies which provide interactive messaging, by combining speech synthesis and voice recognition. This can help banks reach customers quickly and accurately determine if a suspect transaction is taking place.
“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 accomplished through digital profiling, which examines customer data available from an existing information source, alongside determining how data can be stored and changed.
Bhupender continues: “But often forgotten is the need for fraud aftercare. It is in fact a crucial aspect in allowing banks to identify gaps in the system and assist customers after criminal activity has taken place. Machine learning is increasingly playing a key part in speeding up the resolution process, which in turn, improves customer support for those who have been victims of fraud.”
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