Strengthening security: how digital ID tools are fighting identity theft

(Image credit: Photo by Markus Winkler on Unsplash)

In 2019, unauthorized financial fraud losses in the UK amounted to £824.8 million, according to UK Finance. Identity theft is a critical contributor to this problem and has become a significant issue in recent years. Anti-fraud measures designed to counter identity theft have forced fraudsters to come up with new and evolving fraud methods, and despite advancements in identity theft protection, these methods are increasingly difficult to detect and prevent.

For example, in account takeover fraud, criminals utilize information stolen through phishing scams to gain access to an individual's account and make unauthorized payments or apply for credit. The challenge in detecting this fraud is that it appears as if the customer is logging into their account. Thus, the alarm may only be raised when the customer notices unusual activity on their account.

Synthetic identity fraud, also known as Frankenstein fraud, is even more challenging to detect. Here, the criminal creates a new identity by combining factual information stolen from various sources to create a new persona. Over time, the fraudster builds legitimacy for this identity, becoming a model customer of bank accounts and short-term credit, always paying on time to build their score. Finally, they "cash out" by applying for as much credit as possible, with no intention of ever paying it back.

Digital ID tools

Digital identity tools are a crucial weapon in the fight against identity theft. At a basic level, they use a limited set of attributes, such as name, date of birth, credit bureau data, and electoral roll data, to identify the individual in question and determine the probability of them being genuine. But as we’ve already heard, these can be easily stolen or faked.

This is where cutting-edge technology can help. The latest digital identity tools analyze broader attributes from when the ‘customer’ attempts to log in. These can include behavioral characteristics that check against established patterns of behavior unique to an individual – how they enter their details, how quickly they type, how they hold their devices, or physical traits, such as the device they’re using and their location in the world. Measuring these attributes helps companies make a risk judgment even before a successful login and dynamically add additional layers of authentication in milliseconds if there’s any suspicion it’s not the genuine customer.

Other layers of digital security use knowledge-based authentication (KBA), one-time passwords (OTP), and advanced biometrics such as liveness tests and facial recognition to add additional layers of security designed to thwart fraudsters using stolen details. These multifactor authentication methods allow businesses to authenticate people with a much higher probability of success and improve and speed up the experience for genuine customers.

Tackling fraudsters using manufactured identities is trickier, but technology can help. Using artificial intelligence machine learning tools, firms can analyze vast sets of customer data to detect patterns and linkages between common attributes like address and phone number to uncover potential fraud networks that would otherwise remain invisible.