Amiram Pinto of NICE discusses how you can guard you contact centre from the dangers of fraud.
I’ve been dealing with contact centre fraud for the better part of my career, as a product manager and as a marketing professional. One thing that can’t be disputed is that fraud is on the rise. Stories of major data breaches now regularly make international news and something as significant as 100 million records being stolen doesn’t seem to surprise anyone.
I’ve been following closely how fraud losses have increased over the past 5 years, and even I am surprised by the magnitude.
34% of personal information has been compromised in 2018 alone, according to First Data, and fraud losses in 2018 are estimated at $12 billion in the email channel only!
Yet every time I talk to organisations and ask how much they lose because of fraud attacks, I get the same answer: “Fraud is not a concern for us.” This is the real problem. They don’t know.
Can organisations afford not to know that their customers were victims of fraud?
The answer is no. They can’t!
The problem is that until now, they had tools that help them block “known fraudsters”. But as the fraud detection tools get better, the fraudsters become more sophisticated. Organisations need to stay one step ahead and expose the “unknown fraudsters”… now that’s the tricky part.
Many organisations already implement stronger authentication methods to prevent fraud, especially in the contact centre. But they know about a fraudulent activity only after a customer complains that he fell victim to one. When that happens, the fraud officer needs to listen to all the suspicious calls and search for the fraudster.
With millions of calls a day to the contact centre, it’s as useful as looking for a needle in a haystack. Virtually a lost battle.
How can we expose unknown fraudsters?
Let the technology find the needle!
Using voice biometrics, machine learning and deep neural networks, we developed the perfect solution that enables organisations to be proactive and expose the unknown fraudsters.
Imagine, you can automatically scan all the recorded calls, find behavioural patterns unique to specific voices and expose them as fraudsters! Moreover, those exposed fraudsters are added to the watchlist so the next time they attack they will be blocked in real time.
This changes the way organisations fight fraud. Now they can be proactive against evolving threats targeting their customers.