In today’s highly competitive digital marketplace, consumers are more empowered than ever.


Copyright: – “Responsible use of machine learning to verify identities at scale”


They have the freedom to choose which companies they do business with and enough options to change their minds at a moment’s notice. A misstep that diminishes a customer’s experience during sign-up or onboarding can lead them to replace one brand with another, simply by clicking a button.

Consumers are also increasingly concerned with how companies protect their data, adding another layer of complexity for businesses as they aim to build trust in a digital world. Eighty-six percent of respondents to a KPMG study reported growing concerns about data privacy, while 78% expressed fears related to the amount of data being collected.

At the same time, surging digital adoption among consumers has led to an astounding increase in fraud. Businesses must build trust and help consumers feel that their data is protected but must also deliver a quick, seamless onboarding experience that truly protects against fraud on the back end.

As such, artificial intelligence (AI) has been hyped as the silver bullet of fraud prevention in recent years for its promise to automate the process of verifying identities. However, despite all of the chatter around its application in digital identity verification, a multitude of misunderstandings about AI remain.

Machine learning as a silver bullet

As the world stands today, true AI in which a machine can successfully verify identities without human interaction doesn’t exist. When companies talk about leveraging AI for identity verification, they’re really talking about using machine learning (ML), which is an application of AI. In the case of ML, the system is trained by feeding it large amounts of data and allowing it to adjust and improve, or “learn,” over time.

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When applied to the identity verification process, ML can play a game-changing role in building trust, removing friction and fighting fraud. With it, businesses can analyze massive amounts of digital transaction data, create efficiencies and recognize patterns that can improve decision-making. However, getting tangled up in the hype without truly understanding machine learning and how to use it properly can diminish its value and in many cases, lead to serious problems. When using machine learning ML for identity verification, businesses should consider the following.[…]

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