4 minute read

Authenticating “up front” to improve customer experience and boost revenue

Tuesday, February 27, 2024

4 Minute Read

This might sound surprising, but did you know that the cost of declined transactions may exceed the value of actual credit card fraud? In fact, false positives can cost merchants up to 75 times more than the fraud itself1, both in the actual value of canceled transactions and in the opportunity cost of losing further business from customers who decide to abandon a merchant for a competitor. So it should be no surprise that financial institutions (FIs) are looking for new ways to reduce false declines without increasing the friction on their actual cardholders.

How can FIs do that? Some savvy FIs are turning to authentication data as a valuable data source to help them make intelligent decisions about the authenticity of a customer’s identity at the time of transaction. Better identification of the cardholder, or transactor, can lead to cost-savings in the fight against fraud, and a better customer journey with fewer disruptions in the onboarding and transaction processes.

Taking stock of all your data

Ask yourself: are you making the extra effort to authenticate up front in the fight against fraud? Or, are your fraud teams constantly chasing fraud throughout the entire transaction process, only to learn in the end that it wasn’t worth performing all of those back-end system checks?

Issuers of all types—FIs, retailers, fintechs, etc.—should be authenticating customers at every interface throughout the lifecycle journey from onboarding to closed account. Authentication data can then become a complimentary data source applied as both live data points and historical data throughout the customer journey. From application to onboarding, to purchases, log-ins, credit limit change requests and other inquiries, adaptive data may be screened in real time as the customer interacts with your systems and applications. When you authenticate up front, this historical data may then be applied in multiple instances throughout the life cycle for machine learning behavioral and predictive analytics.

Let’s take a look at two pre-authorization approaches that you can take, to put authentication data to use as a valuable additional data source for identifying genuine cardholders. These approaches can help card issuers save on fraud screening costs, experience a decrease in subsequent friction experienced by cardholders in applications and transactions, and see an increase in operational efficiencies.

Authentication data as an additional data source to identify genuine customers:

Identity graph: use of authentication data and the authentication process throughout onboarding for a single unified view of customers and prospects, based on their interactions with a product, an account, a website, or a mobile app across a set of devices and identifiers

Predictive data: use of historical data for authenticating a user

Adaptive data: use of current or live data as a user interacts with a system or takes actions

Onboarding a new credit application

First, let’s take a look at an example of how authenticating up front can help with a new customer’s credit application. Cardholders go to their bank’s website and select the credit product they want and hit ‘apply’. They’re directed to a form that asks them to input all sorts of data: name, address, phone number, etc., while the site itself captures their device information.

While the customer is applying, the application system performs a number of standard checks in the background: Know Your Customer (KYC), sanctions screening, identification and credit scoring. The system can aggregate all of those checks into an application approval or denial. There’s a lot of overhead in this process, as the issuer implements all of these services, coordinates all the calls, and checks against the identity sources and credit bureaus.

But after going through a number of these checks and balances, it may turn out to be a synthetic identity or automated bot. A fraudster may have used a manufactured or stolen identity on the application, or an artificial intelligence (AI) system could be filling out the application. By consistently authenticating up front, the issuer may catch a greater amount of fraud before it even gets started—potentially realizing significant cost savings.

Reducing abandonment rates

Equally as important to issuers is achieving a reduction in abandonment rates on legitimate applications.

If an issuer can complete multiple identity checks by authenticating up front, it’s possible to reduce abandonment rates and complete more applications with limited friction.

Introducing the right amount of authentication actions establishes trust that issuers need while keeping the experience in the forefront. - Kasey Boyd, Head of Fraud, TSYS

Introducing the right amount of authentication actions establishes trust that issuers need while keeping the experience in the forefront. - Kasey Boyd, Head of Fraud, TSYS

Abandonment may occur when an applicant feels that they are spending too much time looking up personal information while filling out an application. An issuer can combat this in the pre-application process by feeding the applicant a series of questions from information that’s readily available on their account, primarily from consortium-sourced data. This could be a previous address, a birth date or driver’s license number. It’s known in the industry as document scanning. Identity and behavior are authenticated and the form is pre-filled. Using this, the applicant can quickly select their known data and complete the application. Issuers appreciate this approach because it can produce a queue for additional operator validation, while eliminating many bot, AI or synthetic identity attempts. Pre-filling information also reduces how much ‘work’ the customer has to do when applying, and therefore it can boost the user experience.

Issuers using this approach to authentication are also beginning to tap into other non-bank file information. Open banking can generate query data from non-traditional sources such as Buy Now, Pay Later transactions, or they may consider an applicant’s rental history—data typically not included in bureau reports. Mining these data sources is a win-win for both the issuer and the applicant by potentially reducing overall fraud and generating greater account opening.

Layering of authentication with 3-D Secure

Now, let’s consider the power of layering 3-D Secure (3DS) authentication methods for card not present (CNP) transactions. What do we learn the most from this use case? CNP transactions can be unique to an individual in how they interact with the application, which device is used, or even the time of day when they typically transact. The combination of these factors, along with 3DS authentication methods ahead of the transaction decision, can help combat CNP fraud.

One way to achieve CNP fraud mitigation is through the authentication on the front-end or pre-authorization with 3DS. Then, integrate the outcomes within fraud systems for better fraud results. This, in turn, can generate a 360-degree view for analysis to vet all transactions and actions together to layer within your fraud rules.

Here’s an example of how it works together. Authentication details—both successful and unsuccessful—are captured with monetary and non-monetary events and fraud results are shared with both the 3DS model on the authentication side, along with rules-based fraud parameters for future fraud predictions on the authorization side. For 3DS solution providers that capture accurate fraud based on this data to feed into score models, 3DS can provide better experiences across millions of devices and make the solution powerful and effective for future CNP transactions.

"Integration of 3-D Secure authentication within fraud systems is a clear benefit to both issuers and to genuine end cardholders for more accurate future predictions with improved intelligence,” said Boyd.

The power in authenticating up front

Issuers using authentication data as a complimentary data source throughout the customer lifecycle for intelligent decisioning may reduce downstream fraud, decrease cardholder friction and increase operational efficiency. The power lies in using machine learning to layer authentication data up front with historical authentication data in the form of monetary and non-monetary events to mitigate downstream fraud. With this information, issuers can make real-time decisions on how to best apply step-up authentication for questionable users, or to reduce the friction for trusted customers.

For onboarding new customers, authenticating up front has the potential to defray fraud costs in compliance screening, KYC checks, sanction screening, identity, and behavioral checks when pre-screening identifies a synthetic or automated bot in the application process. Through document scanning and the use of open banking data, customer abandonments can be reduced for a better customer experience.

If you are interested in learning more about how to use authentication data to enhance your fraud prevention efforts, click here.

  1. PYMNTS.com “Deep Dive: How Merchants Can Reduce the Risk of False Positives Through AI and ML” Sept 10, 2021
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