LibraryblogNeobanks: How AI can turbocharge your KYC process

Neobanks: How AI can turbocharge your KYC process

Artificial intelligence is changing the neobanking landscape, with machine learning ready to power up your identity verification process, boost your conversion rate and accelerate your ROI.

Chris Hooper
Director, Content
January 5, 2023
Blog Post
On this page
Improving accuracy
Ensuring a better experience for legitimate users
Why Veriff
Maximize conversions while minimizing risk

In traditional banking, a financial institution would try to prevent financial crimes and illegal activities, such as terrorism financing, by implementing practices to meet KYC requirements. These KYC compliance measures would include physically verifying a prospective customer before they could access day-to-day services, from opening banking accounts or a checking account to acquiring new debit cards or credit cards. In financial services, the need remains to verify the identity of would-be customers, but with the growth of the digital bank worldwide, operators are looking for more innovative solutions to complete enhanced due diligence (EDD), customer due diligence (CDD), and other compliance measures.

The application of Artificial intelligence (AI) in the form of machine learning promises to be transformative for many sectors, and neobanking is no exception. By analyzing huge volumes of digital data, automated decision engines can recognize patterns and progressively improve the speed and accuracy of processes. This kind of machine learning is already being used with great success by neobanks, both to improve fraud prevention and to deliver a better user experience.

Improving accuracy

Neobanks increasingly use identity verification (IDV) for the Know Your Customer (KYC) process required by regulators to prevent money laundering. The best IDV systems compare live biometric data from a selfie with stored biometric data to authenticate customer sessions. By incorporating machine learning algorithms programmed to identify patterns into this kind of automated IDV solution, the data collected can be used to adjust and improve the accuracy of its decision-making over time. 

Because machine learning makes predictions based on previous experience, novelty is a potential blind spot for the technology. The larger and more current the dataset, therefore, the lower the risk that an automated decision engine will make a mistake. For identity verification, for example, access to a dataset that includes plentiful biometric data for a range of ethnicities and ages is important in minimizing potential bias. In addition, cross-comparing verification sessions based on device, network and customer behaviour can provide a greater richness and variety to the data a decision engine uses to learn and improve its decision making.

By analyzing huge volumes of digital data, automated decision engines can recognize patterns and progressively improve the speed and accuracy of processes. This kind of machine learning is already being used with great success by neobanks.

Chris Hooper

Ensuring a better experience for legitimate users

Another application of AI in the IDV process is assisted image capture (AIC). This uses machine learning to identify and mitigate the most common verification errors, thereby simultaneously increasing conversion rates at the same time as ensuring accuracy. AIC acts as an active photo assistant, guiding users with multiple responses to help them take better images of their identity documents and ensure success on the first try. It alerts users to a range of errors including unreadable data, incomplete documents, unclear facial images for comparison, and unsupported documents. The result is sharp, well-lit, correctly framed images of ID documents that provide all the necessary information for an automated decision engine to confirm identity verification, without the need for human intervention on the bank’s behalf. 

Fast decisions

A 98% check automation rate gets customers through in about 6 seconds.

Simple experience

Real-time end user feedback and fewer steps gets 95% of users through on the first try.

Document coverage

An unmatched 10K+, and growing, government-issued IDs are covered.

More conversions

Up to 30% more customer conversions with superior accuracy and user experience.

Better fraud detection

Veriff’s data-driven fraud detection is consistent, auditable, and reliably detects fraudulent forms of identification.

Scalability embedded

Veriff’s POA can grow with your company’s needs and keep up with times of increased user demand.

Maximize conversions while minimizing risk

Combining AIC with the use of machine learning in IDV decision-making lets customers verify their identities quickly and easily, while simultaneously improving accuracy. The result is a higher conversion rate, less work for support teams and an overall improvement in ROI. 

Get more details

Discover more about how IDV is powering Neobank growth and customer acquisition.