5 Best Practices Or Consumer Identity Verification

Consumer Identity Verification

Digital transformation has changed how businesses function across enterprises, particularly after the global pandemic. It has transformed business models and operational processes faster than ever before. The changing business landscape is shifting from a business-focused market to a consumer-centric one, where changing customer behaviours drive change and digital innovation. Therefore, implementing secure consumer identity verification has become a critical part of operations for online payment service providers.

Additionally, after the pandemic, industries like hospitality, travel and tourism, public services and online gaming have been forced to reconsider their existing models for customer engagement and use a user-centric approach instead. With a large section of the global population living under restrictive conditions for months on end, physical store visits stopped completely and businesses had to shut physical stores. Since then, remote access to products and services has become critical for several sectors and domains.

During this time, physical interaction faced numerous challenges. But with more and more customers shifting to digital or online purchases, it became critical for businesses to ensure increased security around data and product access. The challenge lies in asking customers to provide proof of authenticity without disrupting the smooth customer experience.

Customer identity Verification For Remote Accessibility:

In layman’s terms, identity verification means ensuring a person is exactly who they say they are. In the digital version of identity verification for customers, there are four simple steps to follow:

  1. A customer clicks a picture of an official government identity, for example, a driving licence or a passport.
  2. The identity service provider identifies the authenticity of the ID by checking the integrity and consistency of the given data.
  3. The customer then takes a picture of themselves and uploads it.
  4. The provider confirms whether the person is live or using a photo, whether they are present physically and whether the face matches the one on the document. 

With advancing technological integrations and developments like AI, machine learning, and facial biometrics, customer verification, and onboarding processes have become more secure and efficient. It is all possible because of events and conferences like a CHRO event. Additionally, these technologies also combine to help with remote user verification and enhance the onboarding experience, 

Best Practices for Customer Verification:

  1. Analyse several layers of data:

According to data from several reports, 45% of all businesses surveyed reported that they analyse several identity attribute layers to ensure accurate user identity verification. With cybercriminals growing more sophisticated in their attacks, additional attribute blankets must work together to ensure a seamless experience for the customer. Using a solution that orchestrates large volumes of dynamic data sets not only detects and prevents fraud, but also delivers a seamless experience for the customer based on data collection practices that are simple to defend and explain. 

  1. Add a layer of human fraud expertise to Machine Learning:

FinTech companies can combine advanced technologies with human expertise to meet the identity verification standards for a balanced customer experience. Businesses that apply manually supervised ML to their identity verification card processing service have the power to analyse large volumes of data from digital transactions, recognise patterns and create efficiencies for improved decision-making. 

When human expertise is added to this mix, FinTech companies get maximised benefits of both human intervention and machine learning. This enhances existing anti-fraud processes and creates new and more usable data sets that aid identity verification processes. Machines are good at identifying trends labelled as suspicious behaviour, but cannot see any novel fraud behaviours or patterns. Therefore, adding a human touch to the machine learning process makes the journey that much simpler. 

  1. Ensure greater data transparency:

Several machine learning solutions rate their data security scores on a pass or fail scale. This method is simple but doesn’t provide sufficient transparency. Without any visibility into data needed for decision-making, a business depends on hazy models of identity-proofing with no solid data to back it up. Such solutions generally don’t provide data or insight visibility and end up applying common engine logic across several industries and customers. 

As a best practice for consumer identity verification, solutions must offer continuous data feedback for better understanding and explanations targeted to consumers and regulators to inform them why any decisions are made. This feedback loop also helps in assessing data risks and fine-tuning identity verification processes to meet business needs and industry standards.

  1. Implement personalised workflows for identity verification:

Every business is different, and so are its customer verification requirements. Businesses today must be prepared to act immediately while anticipating attacks, adapting to changes in behaviour and responding to new customer profiles, needs and segments. Simultaneously, FinTech companies must empower business decision-makers to implement pre-qualification formats and collect less sensitive data for specific applications to help streamline customer onboarding without diminishing identity verification standards. 

  1. Maintain cross-industry fraud intelligence:

Fraudsters commonly move from one industry/business to another as they attempt to steal or leak data. Businesses today need consumer identity verification solutions that can identify fraud trends across industries and domains.
An end-to-end consumer identity verification solution is the best way to ensure authenticity, transparency and a smooth customer experience.

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