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Responsible usage of machine understanding how to verify identities at scale

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In todays highly competitive digital marketplace, individuals are more empowered than ever before. They will have the freedom to select which companies they work with and enough options to improve their minds at a moments notice. A misstep that diminishes a customers experience during sign-up or onboarding may lead them to displace one brand with another, by just clicking a button.

Individuals are also increasingly worried about how companies protect their data, adding another layer of complexity for businesses because they try to build rely upon an electronic world. Eighty-six percent of respondents to a KPMG study reported growing concerns about data privacy, while 78% expressed fears linked to the quantity of data being collected.

Simultaneously, surging digital adoption among consumers has resulted in an astounding upsurge in fraud. Businesses must build trust and help consumers believe that their data is protected but must deliver an instant, seamless onboarding experience that truly protects against fraud on the trunk end.

Therefore, artificial intelligence (AI) has been hyped because the silver bullet of fraud prevention recently because of its promise to automate the procedure of verifying identities. However, despite all the chatter around its application in digital identity verification, a variety of misunderstandings about AI remain.


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Machine learning as a silver bullet

Because the world stands today, true AI when a machine can successfully verify identities without human interaction doesnt exist. When companies discuss leveraging AI for identity verification, theyre really discussing using machine learning (ML), that is a credit card applicatoin of AI. Regarding ML, the machine is trained by feeding it huge amounts of data and and can adjust and improve, or learn, as time passes.

When put on 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 levels of digital transaction data, create efficiencies and recognize patterns that may improve decision-making.However, getting tangled up in the hype without truly understanding machine learning and how exactly to utilize it properly can diminish its value and perhaps, result in serious problems. When working with machine learning ML for identity verification, businesses should think about the next.

The prospect of bias in machine learning

Bias in machine learning models can result in exclusion, discrimination and, ultimately, a poor customer experience. Training an ML system using historical data will translate biases of the info in to the models, which may be a significant risk. If working out data is biased or at the mercy of unintentional bias by those building the ML systems, decisioning could possibly be predicated on prejudiced assumptions.

When an ML algorithm makes erroneous assumptions, it could develop a domino effect where the system is consistently learning the incorrect thing. Without human expertise from both data and fraud scientists, and oversight to recognize and correct the bias, the issue will undoubtedly be repeated, thereby exacerbating the problem.

Novel types of fraud

Machines are excellent at detecting trends which have already been defined as suspicious, but their crucial blind spot is novelty. ML models use patterns of data and for that reason, assume future activity will observe those same patterns or, leastwise, a frequent pace of change. This leaves open the chance for attacks to reach your goals, since they haven’t yet been seen by the machine during training.

Layering a fraud review team onto machine learning means that novel fraud is identified and flagged, and updated data is fed back to the machine. Human fraud experts can flag transactions that could have initially passed identity verification controls but are suspected to be fraud and offer that data back again to the business enterprise for a closer look. In this instance, the ML system encodes that knowledge and adjusts its algorithms accordingly.

Understanding and explaining decisioning

One of the primary knocks against machine learning is its insufficient transparency, that is a basic tenet in identity verification. One needs in order to explain how and just why certain decisions are created, in addition to tell regulators home elevators each stage of the procedure and customer journey. Insufficient transparency may also foster mistrust among users.

Most ML systems give a simple pass or fail score. Without transparency in to the process behind a choice, it could be difficult to justify when regulators come calling. Continuous data feedback from ML systems might help businesses understand and explain why decisions were made and make informed decisions and adjustments to identity verification processes.

There is absolutely no doubt that ML plays a significant role in identity verification and can continue to achieve this later on. However, its clear that machines alone arent enough to verify identities at scale without adding risk. The energy of machine learning is most beneficial realized alongside human expertise sufficient reason for data transparency to create decisions that help businesses build customer loyalty and grow.

Christina Luttrell may be the ceo for GBG Americas, made up of Acuant and IDology.


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