Consumers are more empowered than ever in today's highly competitive digital marketplace. They have the freedom to pick which businesses they choose and the flexibility to change their minds at a moments notice. A failure that ruins a customer's experience during sign-up or onboarding may result in them switching between different brands just by clicking a button.
Consumers are also becoming more concerned about how businesses safeguard their data, adding another layer of difficulty for businesses as they strive to build trust in a digital world. A total of 868 percent of respondents to a KPMG study expressed concerns about data privacy, while 78% expressed concerns about the amount of data being collected.
Despite the increase in fraud, businesses must build trust and assist consumers in believing that their data is safe, while also delivering a quick, seamless onboarding experience that protects against fraud on the back end.
Artificial intelligence (AI) has been hailed as the silver bullet of fraud prevention in recent years because of its ability to automate the process of verifying identities. However, a great deal of misconceptions remain about it.
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Machine learning is no longer a silver bullet.
True AI, in which a machine can successfully verify identities without human interaction, isn't as prevalent today. Companies are 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 quantities of data and allowing it to adjust and improve, or learn, over time.
When applied to the identity verification process, machine learning can play a significant role in building trust, removing friction, and preventing fraud. Businesses can therefore analyze huge amounts of digital transaction data, createefficiencies, and identify patterns that can improve decision-making. However, falling prey to machine learning hype or neglecting to grasp its implications can have serious consequences.
Machine learning's potential for bias
Bias in machine learning models can lead to exclusion, discrimination, and, ultimately, a negative customer experience. This can pose a serious danger if the training data is biased or subjected to unintentional bias by those designing the ML systems.
When an ML algorithm makes incorrect assumptions, it can create a domino effect in which the system is constantly learning the wrong thing. Without human expertise, both from data and fraud scientists, and oversight to identify and correct the bias, the issue will be repeated, thereby aggravating the issue.
Fraud in new forms is a problem.
Machines are exceptional at detecting patterns that have already been identified as suspicious, but their primary blind spot is novelty. ML models depend on data patterns and therefore, assume future activity will follow those same patterns or, at least, a consistent pace of change. This opens the door to attacks that have not been seen by the system during training.
Machine learning and combining a fraud review team ensures that new fraud is identified and flagged, and updated information is fed back into the system. Human fraud experts can identify transactions that may have initially passed identity verification controls but are suspected to be fraud, and provide that data back to the business for a closer look.
Decision making is a process of understanding and explaining it.
One of the biggest obstacles to machine learning is its lack of transparency, which is a core requirement in identity verification. One must be able to explain how and why certain decisions are made, as well as disclose to regulators information on each stage of the process and customer journey.
Most ML systems provide a straightforward pass or fail score. Without transparency into the decisionmaking process, it can be difficult to justify when regulators come calling. Continuous data feedback from ML systems can help businesses understand and explain why decisions were made and make informed judgments and adjustments to identity verification procedures.
There is no doubt that machine learning plays an important role in identity verification in the future, and that it will continue to do so in the future. However, it is clear that machines alone arent sufficient to verify identities at scale without adding risk. Machine learning is best realized alongside human expertise and data transparency to help businesses grow.
Christina Luttrell is the CEO of GBG Americas, which consists of Acuant and IDology.