Core Insights - Equifax has launched a new product utilizing artificial intelligence to combat synthetic identity fraud, a rapidly growing threat in the consumer lending ecosystem [1][4] - The Synthetic Identity Risk product employs machine learning algorithms to identify fraud patterns and flag potentially fraudulent activities, aiding enterprises in decision-making regarding identity verification and fraud prevention [3][4] Group 1: Product Overview - The Synthetic Identity Risk product can be utilized during account opening or as a continuous account management tool [3] - The product aims to enhance lenders' fraud defenses by shifting from reactive loss recovery to proactive prevention [4] Group 2: Industry Context - Synthetic identity fraud is increasingly common and costly for financial services firms, exploiting automation and traditional verification checks [4][5] - The rise of artificial intelligence tools has exacerbated the threat, enabling fraudsters to create fake identities and circumvent fraud prevention measures [5] Group 3: Company Strategy - Equifax is focusing on leveraging advanced AI capabilities and unique data assets to develop a new generation of fraud prevention tools [7] - The company has acknowledged that fraud remains a significant and evolving threat faced by its customers [7]

Core Insights - Equifax has launched a new product called Synthetic Identity Risk, which utilizes AI to combat synthetic identity fraud, a growing issue that leads to significant financial losses for lenders [1][4] - Synthetic identity fraud involves the creation of fictitious identities using real and fabricated elements, allowing fraudsters to open credit accounts and loans without detection [2] - The average loss per known synthetic identity is approximately $13,000, highlighting the financial impact on lenders [2] Product Features - Synthetic Identity Risk employs patent-pending technology to analyze identity data, credit history, and behavioral signals to assess the risk of synthetic identity activity [3] - The product can be used both at the account opening stage and as a continuous account management tool to identify hidden risks within a portfolio [3] - This holistic approach enables businesses to make informed, real-time decisions regarding identity verification and fraud prevention [3] Industry Impact - The rise of synthetic identity fraud poses a significant threat to the consumer lending ecosystem, prompting the need for enhanced fraud detection measures [4] - By implementing Synthetic Identity Risk, lenders can transition from reactive loss recovery to proactive fraud prevention, thereby reducing financial losses and fostering trust with legitimate customers [4]

Core Insights - TransUnion's study indicates that Canadian businesses lost an estimated CAD$111 billion to fraud in the past year, representing 7.2% of their revenues, a significant increase from CAD$78 billion in 2024 [3][6] - Synthetic identity fraud has risen to 26% of total fraud losses, up from 18% in 2024, marking the largest year-over-year increase among fraud types in Canada [7][6] - Despite an overall decline in suspected digital fraud rates, online communities and gambling transactions have seen significant increases in fraud attempts, with online communities experiencing a 68% year-over-year increase [8][9] Fraud Losses and Trends - Scams and authorized fraud account for 29% of reported losses among Canadian businesses, while synthetic identity fraud is becoming increasingly prevalent [5][6] - The rate of suspected digital fraud attempts in Canada decreased from 5.4% in H1 2024 to 4.2% in H1 2025, although this rate remains higher than the global average [3][6] - Online communities had the highest suspected digital fraud attempt rate at 11.4%, followed closely by gambling at 10.9% [10][8] Business Leaders' Concerns - 48% of Canadian business leaders reported that their customers were victims of fraudsters spoofing their business' phone number or name, while 60% cited fake emails impersonating their brand [3][6] - There is a significant concern regarding spoofing scams, indicating a need for enhanced security measures [3][4] Fraud Mitigation Strategies - Canadian businesses are focusing on various technologies to combat fraud, including identity verification (53%), device reputation (46%), and behavioral biometrics (42%) [11][8] - The study emphasizes the importance of a collaborative approach and advanced tools to stay ahead of evolving fraud tactics [8][4] Consumer Awareness and Actions - A separate survey found that 46% of Canadians were targeted by fraud attempts, yet only 6% reported falling victim, indicating high awareness [12][13] - Despite this awareness, 37% of Canadians took no action in the last 60 days due to cybersecurity concerns, with many unsure of what steps to take [12][13]

Core Insights - The rise of synthetic identities has led U.S. lenders to face over $3.3 billion in exposure for the year ending 2024, highlighting the urgent need for enhanced fraud detection methods [1] - TransUnion's research indicates that public data attributes can significantly aid in identifying synthetic identities, which are often constructed using a mix of real and fabricated information [2][3] Group 1: Synthetic Identity Fraud - Synthetic identities are created using stolen Social Security numbers, fictitious names, and digital contact details, making them difficult to detect with traditional verification systems [2] - The complexity of synthetic identity fraud arises from the lack of a single method used by criminals, complicating the differentiation between genuine and synthetic customers [3] Group 2: Detection Strategies - Key living characteristics, such as the absence of vehicle ownership or voter registration, can indicate a higher likelihood of an identity being synthetic, with such traits appearing in 30-50% of synthetic identities [4] - TransUnion's Synthetic Fraud Model is designed to identify public data indicators and risk factors to uncover synthetic identities early in the customer journey, allowing for proactive measures [5] Group 3: Operational Efficiency - The model enhances operational efficiency by reducing manual reviews and customer friction, enabling lenders to improve fraud detection rates while streamlining processes [6] - By identifying the absence of real-life attributes, lenders can prevent fraud and minimize financial losses throughout the customer lifecycle [7]