Attacks on the financial sector have increased steadily for two decades, and the volume of reported attempts surged during the pandemic. This was especially evident as fintech lenders provided rapid dispersal of government loans to small businesses. Fintech firms were nearly five times more likely than traditional financial institutions to approve “highly suspicious” loans, accounting for almost $80 billion in questionable disbursements. Meanwhile, fraud in the account opening processes is endemic.
It’s easy to see why 86 percent of businesses view identity verification as a strategic differentiator, allowing platforms to capitalize on digital adoption while delivering a seamless customer experience. 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.
The following best practices can help fintechs locate and approve new customers without friction or fraud while streamlining the customer journey.
#1 Analyze Multiple Layers of Data
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 also deliver a seamless customer experience predicated on data collection practices that are easy to explain and defend.
With multiple layers at the heart of the identity verification process, legitimate customers are identified more quickly and accurately, and additional verification methods are used only when absolutely necessary.
#2 Layer Machine Learning with Human Fraud Expertise
Fintechs can balance user experience with identity verification standards by combining burgeoning technologies with human fraud expertise. By applying supervised machine learning (ML) 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.
When coupled with human expertise, fintechs get the best of both worlds, enhancing anti-fraud protocols while creating new, more usable data sets that enhance identity verification efforts moving forward. More specifically, 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.
#3 Embrace Data Transparency
Many ML-based solutions provide a pass or fail score that is as opaque as it is simple. 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 understand and 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.. This is nearly impossible to do with a system that relies on “black box” algorithms.
#4 Implement Customized Identity Verification Workflows
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.
At the same time, fintechs need to empower decision-makers to collect less sensitive information or enact pre-qualification formats for certain applications, streamlining customer onboarding without compromising identity verification standards.
#5 Cross-Industry Fraud Intelligence.
It’s common for fraudsters to jump from industry to industry as they carry out their plans. Effectively fighting fraud is a group effort. With the right identity verification solution in place, businesses will have visibility into fraud trends and data across industries and channels.
As the fintech sector moves towards a post-pandemic reality, fraud attempts are likely to intensify alongside growing customer expectations. Identity verification will be an operational necessity and a moral imperative, keeping fintechs and their customers safe in a challenging digital environment.
This article was originally published on the author’s blog and reprinted with permission.