Identity verification has come a long way in the last five years. Even before the pandemic made digital identity verification a critical element of commerce, 86% of businesses saw it as a strategic differentiator. Its value has also increased in the mind of the consumer. Seventy-seven percent of Americans report that having an unauthorized account opened in their name would impact their likelihood to patronize or do business with that company. Furthermore, consumers who don’t trust the digital identity verification process are more likely to use guest checkout (54%) and less likely to keep a payment card on file (43%), thereby creating a drag on profits while compromising the end-user experience.
At the same time, the push to locate and approve new customers without friction, deter fraud, and streamline the customer journey, especially for those who are newly digitized, requires businesses to execute numerous complex processes. Fortunately, innovation has led to significant advances in identity verification solutions and the technology behind them.
Finding the right solution in a crowded marketplace and separating truth from buzz can be a challenge so let’s look at some of the “must-have” features needed for truly effective identity verification solutions.
- Multiple Layers of Intelligence. Forty-five percent of businesses report analyzing multiple layers of identity attributes as a best practice. As fraudsters increasingly add sophistication to their schemes, additional layers or “blankets” of attributes that work together are the key to a seamless customer experience. Solutions that “orchestrate” multiple dynamic data sets, not only detect and deter fraud, but they also deliver a seamless customer experience based on decisioning that is easy to explain and defend. With multiple layers at the heart of the identity verification process, legitimate customers are identified more quickly and accurately. Additionally, step-up verification methods are used only when absolutely necessary.
- Machine Learning + Human Intelligence. By applying machine learning to the identity verification process, businesses have the power to analyze massive amounts of digital transaction data, create efficiencies and recognize patterns that can improve decision making. As an early adopter of ML, we believe it plays an important role in deterring fraud and building customer relationships, but we don’t rely solely on machines to do the critical task of identity verification.
Counter to the many benefits of utilizing ML are risks in its propensity for bias, lack of data transparency and absence of governance. While machines are great at detecting trends that have already been identified as suspicious, a critical blind spot is their inability to detect novel forms of fraud. Thus, a provider that layers human fraud expertise onto machine learning is critical.
- Data Transparency. Many ML-based solutions provide a pass or fail, or a simple score. Without visibility into decisioning data, businesses are left to depend on restrictive and hazy score-based identity proofing models. These “Black box” solutions fail to offer data intelligence visibility and instead apply common engine logic across multiple customers and industries.
An effective identity verification solution should provide a continuous data feedback loop to help businesses understand and explain to regulators and customers why decisions were made. This is nearly impossible to do with a system that relies on “black box” algorithms. With data transparency and actionable scoring intelligence, businesses can explain to regulators and consumers why certain decisions were made, better assess risk and fine-tune identity verification processes to best fit their industry and business needs.
- Customization. The ability to customize identity verification settings to meet individual business and customer needs is important today but quickly becoming mission critical. Every business is different and should verify differently based on its unique needs. This includes the ability to tweak and tune identity verification settings in real time, without the help of IT. All businesses need the ability to act quickly as they anticipate attacks, adapt to systemic changes in human behavior, and respond to the emergence of new customer segments, profiles and needs.
- Cross-Industry Fraud Intelligence. It’s common for fraudsters to jump from industry to industry as they carry out their plans, which is why effectively fighting fraud is a group effort. Visibility into fraud data across industries and channels can help businesses spot repeated activity across the network. As an example, our consortium network provides real-time fraud intelligence between companies and across industries, so our customers can leverage the fraud mitigation efforts of every IDology customer.
For the first time in IDology’s annual Fraud Report, digital identity verification became the most daunting challenge to fraud deterrence across industries in 2021. Today, identity verification is an opportunity for businesses to set themselves apart from competitors. Locating and approving more legitimate customers without friction boosts revenue while offering a smooth, seamless onboarding user experience. It also allows businesses to develop competitive differentiators. Nonetheless, yesterday’s online processes and technology can’t be expected to satisfy today’s consumers.
For more information on a modern, comprehensive and multi-layered verification approach that balances customer security with friction download our 8th Annual Fraud Report.