Different Identity Verification Methods

Security breaches are becoming commonplace today.  At the core of the issue is the theft of personally identifiable information (PII). As cyber criminals come up with new ways to commit identity fraud and circumvent the existing safeguards, it is imperative that banking, financial services and insurance (BFSI) organizations shift into overdrive and embrace identity verification technology to thwart their malicious intent. In doing so, businesses must be careful to maintain a smooth customer experience.

In this context, let’s evaluate a few of the options available to your business today. After all, there is a moral and legal obligation to keep customer interests safe.

What is Knowledge-Based Authentication (KBA)

KBA is among the most common identity verification methods where the customer is posed a question, the answer to which only he/she is privy to (and which may include a time limit by which the answer must be provided). This approach has a relatively high risk of failure as the challenge questions are limited in variation and fraudsters can often find the answers on social networking sites. More importantly, it does not rely on any government-issued identity, so any person with access to these answers can take undue advantage. Intrusiveness is also a concern.

What is Biometric Verification & Authentication

Biometrics can be used to identify and authenticate individuals based on physical characteristics. Biometric techniques include facial recognition and matching, voice recognition, iris and retina scanning, and fingerprinting. These methods offer a high level of convenience to customers (after initial setup has occurred) as no passwords need to be remembered, no questions need to be answered, etc.

However, biometrics has some flaws. For example, if a bad actor submits their own fingerprints under an assumed identity that is supported by forged documents, fraud can occur. Biometrics can also be stolen. Think about it. Pictures of your face are likely all over social media. Your voice can be recorded unknowingly. Databases can be hacked to retrieve fingerprints. And once these types of assets are in the hands of a bad  actor, it can be even easier to defraud some institutions.

Machine Learning-Based Solutions

Customer acquisition is without a doubt the biggest growth driver for any business, but when it comes to banking, financial services and insurance (BFSI), the complexity of acquiring good customers goes up a notch. Apart from creditworthiness, anti-money laundering, sanction enforcement and counter terrorist finance regulation, compliance must be factored in to prevent legal hassles and losses resulting from fraud.

Credit bureau-based solutions are the go-to when it comes to verifying creditworthiness of customers. It details out their repayment history and credit scores. The process is usually API based and quick. Only registered institutions are allowed access to the database. These organizations also offer identity verification services. However, some organizations mistake credit worthiness as a good tool for verifying the identity of applicants—it is not. SImply put, a bad actor can steal the identity of someone with good credit and the credit review process will verify that this person’s credit is acceptable.  However, that same process does nothing to determine if the applicant is who they say they are.

A true identity verification solution—one that captures third party and synthetic fraud—is the necessary, second part of the equation here. A solution that applies artificial intelligence and machine learning to trusted online/offline sources including email, phone, address, IP address, social media and traditional GLBA/DPPA data to authenticate identities in real-time has many advantages. A strong solution will:

• Use thousands of data points to draw correlations and assess risk levels
• Learn from itself and improve its accuracy over time (assuming proper machine learning processes are being followed and the models are being trained and retrained using actual results)
• Facilitate quick customer onboarding through real-time screening and elimination of false-positives to ensure a seamless experience
• Integrate quickly and easily with existing business platforms

In Conclusion

Digital business today relies heavily on convenience and as a result, anonymity is waning. It is important that effective identity verification measures are in place to safeguard the bottom line and to ensure a smooth customer experience. But beware of most legacy solutions—and even some modern ones—as they are many that are just not that effective.

So when you are looking for your next identity verification solution, be sure to target new age technology-led solutions that draw information from multiple sources without sacrificing customer experience and security. Think AI. THINK MACHINE LEARNING.