Responsible use of machine learning to verify identities at scale

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In today's highly competitive digital marketplace, consumers are more empowered than ever. They have the freedom to choose the companies they do business with and enough options to change their minds at any time. A misstep that diminishes a customer's experience during signup or onboarding can lead them to switch from one brand to another, just by clicking a button.

Consumers are also increasingly concerned about how companies protect their data, which adds 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 concerns about the amount of data being collected.

At the same time, the growing adoption of digital by consumers has led to a meteoric rise in fraud. Businesses need to build trust and help consumers feel their data is protected, but also need to provide a fast and seamless onboarding experience that actually protects against upstream fraud.

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

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register here Machine learning as a silver bullet

In the current state of the world, true AI in which a machine can successfully verify identities without human interaction does not exist. When companies talk about leveraging AI for identity verification, they are actually 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.

When applied to the identity verification process, ML can be a game-changer by 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 really 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, organizations should consider the following:

The potential for bias in machine learning

Bias in machine learning models can lead to exclusion, discrimination, and ultimately a negative customer experience. Training an ML system using historical data will translate data biases into models, which can be a serious risk. If the training data is biased or subject to unintended bias by those building the ML systems, the decision may be based on biased assumptions.

When an ML algorithm makes the wrong assumptions, it can create a domino effect in which the system constantly learns the wrong thing. Without the human expertise of data and fraud scientists, and without monitoring to identify and correct bias, the problem will repeat itself, making it worse.

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