According to UK Finance, in 2019, losses related to unauthorized financial fraud in payment cards, branchless banking and checks amounted to €824,8 million. One type of fraud that contributes significantly to this loss is identity theft (opens in a new tab), which has become a serious problem in recent years. Anti-fraud measures designed to detect identity theft force fraudsters to find ways to trick people, leading to ever-evolving new types of fraud that are increasingly difficult to detect and stop.

In the case of account takeover, for example, the criminal uses information stolen through phishing scams to gain access to an individual's account, make unauthorized payments, or apply for credit. The difficulty in detecting fraud is that it appears that the customer is logging into their account. Therefore, the alarm can only be triggered when the customer detects abnormal activity on their account.

Even more difficult to detect is synthetic identity fraud, sometimes called Frankenstein fraud, where criminals create an identity by putting together factual information stolen from various sources to create an entirely new person. Nurtured over time, fraudsters build identity legitimacy, becoming model customers of bank accounts and short-term credit, always paying on time to build their score. Eventually, they "get it": they request as much credit as possible simultaneously, with no intention of paying.

According to recent research, account takeover fraud accounts for 19% of all third-party fraud (in which people's data is stolen), while synthetic identity fraud accounts for 15% of all first-party fraud in the United Kingdom. In other words, these are big problems. So how do we deal with it?

Digital identity tools are a crucial weapon in the fight against identity theft. At the basic level, they use a limited set of attributes, such as name, date of birth, credit bureau data, and voter registration data, to identify the person in question and determine how likely they are to be authentic. . But as we have heard before, these can be easily stolen or counterfeited.

This is where advanced technology can help. The latest digital identity tools look at a broader set of attributes when the "client" tries to log in. These may include behavioral characteristics that check an individual's established behavior patterns: how they enter information, how fast they type, how they hold their device, or physical characteristics, such as the device they use and their location in the world. Measuring these attributes helps organizations assess risk even before a successful login and dynamically add additional layers of authentication in milliseconds if the actual client is suspected to be a non-client.

Other layers of digital security use knowledge-based authentication (KBA), one-time passwords (OTP), and advanced biometrics like proof of life and facial recognition to add additional layers of security designed to thwart fraudsters using stolen details. These multi-factor authentication methods allow businesses to authenticate people with a much higher probability of success and improve and speed up the experience of actual customers.

Tackling fraudsters using fabricated identities is more complicated, but technology can help. Using artificial intelligence machine learning tools, companies can analyze large sets of customer data to detect patterns and links between common attributes, such as address and phone number, to uncover potential fraud networks that would otherwise Otherwise, they would remain invisible.

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