As more firms adopt digital operations, the need for seamless and secure onboarding tools will become critical. A new podcast explores the role identity proofing has within financial services.
The podcast, ‘How FIs measure success in ID proofing and why we need biometrics’, was joined by IDmission founder and CEO Ashim Banerjee, Ulysses Partners managing partner David Milligan and Tearsheet editor in chief Zack Miller. The trio discussed the role of identity proofing in financial services, how success can be measured and where identity verification slots into payments.
Banerjee explained that one of the reasons IDmission has good user experiences is because it unifies the customer lifecycle. He explained that identity should be established across a customer lifecycle, through account opening, transacting, receiving account support and more. It is not just for onboarding.
For example, instead of using a login and password, users could use the selfie they submitted for the account opening. Alternatively, submitting a voice sample and authenticating a user through that is easier, Banerjee said.
“The experience over the lifecycle is fragmented. And that can be easily unified using the same biometrics that you’ve collected at the time of ID proofing, which makes the experience significantly better while simultaneously improving security.”
The podcast also explores the idea of benchmarking identity proofing success for financial institutions. Banerjee explained that user experience often dictates the metrics that are of value to the bank. Using a digital identity proofing process and integrating it across onboarding processes will result in larger numbers of signups in shorter amounts of time.
Milligan added that financial institutions often focus on metrics like false positive rates, but these might not be the best. The best metrics are going to change depending in the nature of the transaction and what the goal is.
Similarly, Banerjee stated many firms are not always picking the right model for the job. With identity proofing companies use AI or machine learning, and people want to know what performance should be expected from the models.
Banerjee said, “For example, accuracy is often considered the benchmark, but it’s actually the least useful model, depending on your use case. Accuracy in itself gives you a small part of the picture, while there are other metrics created by machine learning to indicate how the model will perform for your particular business case. So it’s important that business people spend more time understanding the different metrics before deciding which model is likely to work for them.”
Listen to the full podcast here.
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