Missed attending Transform 2022? Check out all the summit sessions now in our on-demand library. Watch here. In today's highly competitive digital marketplace, consumers are more powerful than ever. You are free to choose which companies to do business with, and you always have ample opportunity to change your mind. A mistake that ruins a customer's signup or onboarding experience can result in them replacing one brand with another at the click of a button.
Consumers are also increasingly concerned about how businesses protect their data, adding another layer of complexity for businesses looking to build trust in the digital world. In a KPMG survey, 86% of respondents reported growing privacy concerns, and 78% expressed concerns about the amount of data collected.
At the same time, fraud has risen alarmingly as consumers become increasingly digital. Businesses need to build trust and make consumers feel their data is protected, but they also need to provide a fast and seamless onboarding experience that truly protects against back-end fraud. As such, artificial intelligence (AI) has been touted as the anti-fraud silver bullet in recent years due to its promise to automate the identity verification process. However, despite various debates surrounding its application to digital identity verification, various misconceptions about AI remain.
MetaBeat gathers his leaders of sorts to provide guidance on how Metaverse technology will transform the way all industries communicate and do business on October 4th in San Francisco, CA. When companies talk about using AI for identity verification, they are really talking about using an application of AI: machine learning (ML). With ML, systems are trained by feeding them large amounts of data, allowing them to adapt and improve or "learn" over time.
When applied to the identity verification process, ML has a game-changing role in building trust, removing friction, and fighting fraud. It enables businesses to analyze vast amounts of digital transaction data and identify patterns that can increase efficiency and improve decision-making. However, getting caught up in the hype without a thorough understanding of machine learning and how to use it properly can diminish its value and, in many cases, lead to serious problems. Enterprises should consider the following when using machine learning ML for identity verification: Potential Machine Learning Bias Bias in machine learning models can lead to exclusion, discrimination, and ultimately denial of the customer experience. Training an ML system on historical data can introduce data biases into the model, introducing significant risks. If the training data is biased or unintentionally biased by the builder of the ML system, decisions can be made based on biased assumptions.
When an ML algorithm makes wrong assumptions, it can have a domino effect in which the system continues to learn wrong things. Without the human expertise of both data and fraud scientists, and without oversight to identify and correct biases, the problem will repeat itself, thereby exacerbating the problem. Machines are great at spotting trends that have already been identified as suspicious, but their fatal blind spot is novelty. ML models use data patterns, so they assume that future activity will follow the same pattern, or at least a constant rate of change. This leaves open the possibility that the attack will succeed simply because it has not yet been confirmed by the system during training.
Having a fraud screening team in machine learning ensures that new fraud is identified and flagged, and updated data is fed back into the system. Fraud experts can flag transactions that initially pass background checks but are suspected of being fraudulent and return the data to the company for further investigation. In this case, the ML system encodes this knowledge and adjusts its algorithms accordingly. Understanding and explaining decisions One of the biggest drawbacks of machine learning is its lack of transparency, a fundamental principle of identity verification. You should be able to explain how and why certain decisions are made and share information with regulators about the process and all stages of the customer's journey. Lack of transparency can also foster mistrust among users. Most ML systems offer a simple pass or fail rating. Without transparency in the process behind decisions, they can be difficult to justify when requested by regulators. Continuous data feedback from ML systems helps organizations understand and explain why decisions were made, make informed decisions, and adjust the identity verification process. ML has definitely played an important role in identity verification and will continue to do so. But machines alone are clearly not enough to verify identities at scale without added risk. The power of machine learning is best realized alongside human expertise and data visibility to make decisions that help businesses connect with customers and grow.
Christina Luttrell is the CEO of her GBG America, which consists of Acuant and IDology.
#learning #education #discipline
0 Comments