How advanced authentication improves customer experiences and reduces fraud

This might sound surprising, but did you know that the cost of declined transactions often exceeds the value of actual credit card fraud? In fact, false positives can cost more than fraud itself. Nearly half of merchants in a North American survey estimate that up to 5% of legitimate customer orders are falsely declined, leading to an estimated $50 billion in lost revenue.¹

Consequently, financial institutions (FIs) seek ways to reduce false declines without creating friction for legitimate cardholders.

How can FIs do that? Some savvy FIs are turning to advanced authentication and authentication data as valuable sources to help them assess a customer’s identity at the time of a 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.

How can authentication data help issuers identify genuine customers?

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 those back-end system checks?

Issuers of all types – FIs, retailers, fintechs, etc. – should authenticate customers at every interface throughout the lifecycle journey, from onboarding to account closure. Authentication data can then become a complementary data source applied as both live data points and historical data throughout the customer journey. From application and onboarding to purchases, logins, 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 lifecycle for machine learning behavioral and predictive analytics.

Let’s take a look at two preauthorization approaches 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.

How can advanced authentication detect synthetic identity fraud during onboarding?

First, let’s consider 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 and phone number – while the site itself captures their device information.

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

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.

How can issuers reduce abandonment on legitimate credit applications?

Second, and equally important to issuers, is achieving a reduction in abandonment rates on legitimate credit 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.

Abandonment may occur when an applicant feels they are spending too much time looking up personal information while filling out an application. An issuer can combat this in the preapplication process by feeding the applicant a series of questions from information that’s readily available on their account. This could be a previous address, a birthdate or driver’s license number. It’s known in the industry as “document scanning.” Identity and behavior are authenticated and the form is prefilled.

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. Prefilling information also reduces how much work the customer must do when applying, therefore boosting the user experience.

Issuers using this approach to authentication are also beginning to tap into other nonbank file information such as open banking data. Open banking can generate query data from nontraditional 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 the issuer and the applicant by potentially reducing overall fraud and generating greater account opening.

How does 3-D Secure authentication improve card-not-present fraud prevention?

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 in 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 preauthorization 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 – successful and unsuccessful – are captured with monetary and nonmonetary events. Fraud results are then shared with 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 issuers and to genuine end cardholders for more accurate future predictions with improved intelligence,” said Kasey.

How can authenticating early protect issuers from downstream fraud?

Issuers using advanced authentication and authentication data as complementary data sources throughout the customer lifecycle for intelligent decisioning may reduce downstream fraud, decrease cardholder friction and increase operational efficiency. The power lies in utilizing machine learning to layer authentication data up front with historical authentication data in the form of monetary and nonmonetary 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 prescreening 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.

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