ID verification is an essential part of business process to provide confidence that a particular customer is who they claim to be, and to protect both the business and their customers from fraud. Identity fraud costs the UK industry roughly £3.3 billion per year, ID verification processes are critical for businesses to prevent fraud and reduce losses. But is the future using big data analytics to verify identity or will businesses continue to rely on traditional processes?
Many institutions use various questions and answers to verify ID that can be set up by the customer at signup; such as the name of their first pet or the road they grew up on. These are then used to verify their identity when they attempt to access their account details by selecting a random sample of the questions for the customer to answer. This is known as Knowledge Based Authentication (KBA). KBA is a popular method of ID verification because it tends to be inexpensive and relatively user-friendly in comparison to having to go into a branch with paper identification. More vigorous versions are often used by banks and telecoms providers such as the customer’s mortgage value or how they pay their TV license as this information is more likely to be known only to that customer and perhaps close family and friends.
While KBA is a recognised and widely used part of the verification process across multiple industries, it can create issues such as genuine customers failing to be verified, compromising the user journey by creating friction which both inconveniences the customer, and costs the business time and money. KBA systems only work if the supposed answers are only known to that particular customer but the digital world makes it easier for criminals to discover these answers through research and gain access to personal accounts and further information. Additionally, even if these answers are secret and not discovered by thieves, there is the problem of the customer themselves not even knowing the answer. Would you be able to give details of your mortgage off the top of your head? Or how you pay your council tax bill and your TV license? Questions like these often leave customers feeling like attempted fraudsters when they are told that they cannot gain access to their account information over the phone or internet and must come into the branch or high-street store to gain access to the services they want and need. In the digital age, this is both highly inconvenient and seemingly unacceptable in a world by which we are used to getting anything and everything via the world wide web.
Shockingly, KBA failure rates are, on average, 10-15% but they are sometimes as high as 30%, for example in populations high in new-to-country consumers or younger people without a lot of traditional data (known as thin-file). This failure rate not only inconveniences the business provider, meaning more time (and money) is spent verifying this customer, it also is a huge driver of customer dissatisfaction.
The rise of online data, such as that collected through social media profiles, creates many more opportunities for ID verification techniques. More 18-21 year olds are on Facebook than are on the electoral register, and more Millennials use Facebook daily than own a credit card. The availability of social data is continuously increasing, while traditional data is not building as fast as it had for previous generations. If this trend continues for future generations, we will see a huge drop in available traditional data for consumers, and an increased average age for when consumers have enough traditional data available for traditional methods to be viable. Utilising social data in the verification process could boost financial inclusion while still preventing fraud, and will potentially revolutionise businesses like banking in populations where people may not have formal identity documents or may have been excluded from traditional financial services
Considering the lack of traditional data among young and new-to-country individuals, and the rise of social data, identity verification methods such as KBA should include sources generated through alternative data to allow for those individuals with thin-files to verify their ID quickly and easily. Social data offers the potential of higher security levels than traditional KBA processes based on standard security questions and credit data, due to the vast amount of it. Additionally, alternative methods using non-traditional data could be done entirely online, combatting the need to bring formal identification papers into a branch. For example, real-time authentication of personal transactions by the user socially connecting as part of login processes, or when they purchase something online, could save the industry millions and reduce opportunities for fraudulent activity. Moreover, it would improve the user journey by enabling a frictionless customer experience.
Currently, big data is utilised within industries as an add-on to traditional ID verification methods. Big data analytics tools are still fairly up-and-coming, and businesses are wary to completely turn over their trust from the traditional methods they have used for years. It may be an add-on now, but as traditional data declines and social data rises, the benefits of utilising big data analytics tools as an alternative method for ID verification are clear and a step further into the digital age. These techniques not only open up new opportunities for thin-file consumers and those lacking formal identification documents, they also offer the ability to gain unique consumer insights to make business decisions like never before. Big data is revolutionary and will soon become indispensable, in the future it may not be an alternative, it may be the only way.
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