In today's increasingly digital interactions, robust Identity Verification (IDV) is crucial for security and trust. Artificial Intelligence (AI) is transforming IDV, enhancing accuracy and fraud detection. This paper introduces ``Zero to One,'' a holistic conceptual framework for developing AI-powered IDV products. This paper outlines the foundational problem and research objectives that necessitate a new framework for IDV in the age of AI. It details the evolution of identity verification and the current regulatory landscape to contextualize the need for a robust conceptual model. The core of the paper is the presentation of the ``Zero to One'' framework itself, dissecting its four essential components: Document Verification, Biometric Verification, Risk Assessment, and Orchestration. The paper concludes by discussing the implications of this conceptual model and suggesting future research directions focused on the framework's further development and application. The framework addresses security, privacy, UX, and regulatory compliance, offering a structured approach to building effective IDV solutions. Successful IDV platforms require a balanced conceptual understanding of verification methods, risk management, and operational scalability, with AI as a key enabler. This paper presents the ``Zero to One'' framework as a refined conceptual model, detailing verification layers, and AI's transformative role in shaping next-generation IDV products.

Credit card fraud incurs a considerable cost for both cardholders and issuing banks. Contemporary methods apply machine learning-based classifiers to detect fraudulent behavior from labeled transaction records. But labeled data are usually a small proportion of billions of real transactions due to expensive labeling costs, which implies that they do not well exploit many natural features from unlabeled data. Therefore, we propose a semi-supervised graph neural network for fraud detection. Specifically, we leverage transaction records to construct a temporal transaction graph, which is composed of temporal transactions (nodes) and interactions (edges) among them. Then we pass messages among the nodes through a Gated Temporal Attention Network (GTAN) to learn the transaction representation. We further model the fraud patterns through risk propagation among transactions. The extensive experiments are conducted on a real-world transaction dataset and two publicly available fraud detection datasets. The result shows that our proposed method, namely GTAN, outperforms other state-of-the-art baselines on three fraud detection datasets. Semi-supervised experiments demonstrate the excellent fraud detection performance of our model with only a tiny proportion of labeled data.

Human-in-the-loop (HITL) feedback mechanisms can significantly enhance machine learning models, particularly in financial fraud detection, where fraud patterns change rapidly, and fraudulent nodes are sparse. Even small amounts of feedback from Subject Matter Experts (SMEs) can notably boost model performance. This paper examines the impact of HITL feedback on both traditional and advanced techniques using proprietary and publicly available datasets. Our results show that HITL feedback improves model accuracy, with graph-based techniques benefiting the most. We also introduce a novel feedback propagation method that extends feedback across the dataset, further enhancing detection accuracy. By leveraging human expertise, this approach addresses challenges related to evolving fraud patterns, data sparsity, and model interpretability, ultimately improving model robustness and streamlining the annotation process.

Mike Gallagher and Raja Krishnamoorthi explain the threat from China amid growing concerns about TikTok and the country's relationship with Russia. Tech firm Ultra has developed an artificial intelligence-powered tool it believes has helped analysts identify products coming from China through the platform Temu that were created using forced labor, possibly from the Uyghur population. "We're looking at Temu from the perspective of the Forced Labor Prevention Act," Ultra founder and CEO Ram Ben Tzion told Fox News Digital. "How many things that we don't want are coming into the country using this method, right? The good cases are counterfeit. The worst cases are poor quality. "I'm quite confident that illicit elements can find themselves going through this platform into the market, so it's time to demand accountability," he added. Ben Tzion's company created the program Publican, which pulls in huge amounts of shipping data to analyze and look for patterns and red flags for any products ...

When credit card giant American Express began offering bank accounts for the first time last year, it had a foundation of fraud detection to bring to an entirely new product arena. That meant in some cases, the company could port over AI and machine-learning models used to spot phony identities or dodgy transactions for its credit card products to its consumer and business checking accounts. But it's been a process, and now, AmEx plans to invest in bringing additional AI techniques used to protect against credit card fraud to its banking products. "We have models which run to detect whether it's you or whether somebody else is logging into your account. Very straightforwardly, we transferred it to the banking product," said Abhinav Jain, vice president for Global Fraud Decision Science at AmEx, who is responsible for the company's fraud detection models.

Over 59 million Americans said they lost money as a result of phone scams in approximately the past 12 months, with an average reported loss of $502, according to the Truecaller Insights US Spam & Scam Report. "Fraud is a major consideration in the telecom industry," said Dr. Gadi Solotorevsky, CTO at Amdocs cVidya, an AI solutions provider. "Today, close to 2% or over $1.5 trillion in yearly global revenue is lost annually due to fraudulent behavior. The total losses across the industry are staggering." Solotorevsky cited a 2019 survey from the Communications Fraud Control Association (CFCA) that found that two-thirds of respondents experienced an increase in fraudulent activities. "We mostly encounter payment and subscription fraud, identify theft/impersonation, account takeover, insider threats, and SIM swap," Solotorevsky said.

Companies rely on real-world data to train artificial-intelligence models that can identify anomalies, make predictions and generate insights. To detect credit-card fraud, for example, researchers train AI models to look for specific patterns of known suspicious behavior, gleaned from troves of data. But unique, or rare, types of fraud are difficult to detect when there isn't enough data to support the algorithm's training. To get around that, companies are learning to fake it, building so-called synthetic data sets designed to augment training data. At American Express Co., machine-learning and data scientists have been experimenting with synthetic data for nearly two years in hopes of improving the company's AI-based fraud-detection models, said Dmitry Efimov, head of the company's Machine Learning Center of Excellence. The credit-card company uses an advanced form of AI to generate fake fraud patterns aimed at bolstering the real training data.

The trend toward digitization has been building momentum over the past few years. Cash apps, digital wallets and card-not-present (CNP) transactions are replacing cash, checks and physical credit cards. Meanwhile, fraudulent activity across digital channels is ramping up. Risk is high, as many consumers choose to shop online and use contactless payment methods to avoid unnecessary exposure to the virus. Unfortunately, fraud and risk teams lack experience and historical data around emerging digital payment options -- two of the key factors that traditional rules-based fraud solutions use to fight fraud.

Automation journeys are evolving with AI ML capabilities and better data management techniques. The automation of processes has advantages in many areas of business. Helping to create predictable success, like the autopilot technology on a plane has been perfect over many years and by decreasing the time that it takes to complete manual processes and removing the chance of human error. Automating processes also have the benefit of streamlining workflows. The largest gains can be achieved by automating very large or time-consuming processes.

Machine learning technology will provide the best results in detection of fraud in the future. Indeed, many organizations are actively driving replacement of human-driven rules analysis with machine-driven solutions. However, I believe that a mix of machined-led and human-led activity is the best fit for many organizations to maximize performance. There are several perceptions that suggest fraud rules are no longer fit. Ultimately a data-driven approach, regardless of human or machine involvement, is a state that organizations need to move to in order to maximize detection in the present and to ease the transition to a more machine-driven future.