Jan. 24, 2026 /Mpelembe Media/ — Equifax has launched a sophisticated fraud detection tool designed to combat the rising threat of synthetic identity theft, which involves merging real and fake data to deceive lenders. By utilizing artificial intelligence and machine learning, this new system identifies complex patterns and behavioral cues that traditional security measures often overlook. The technology aims to provide real-time risk assessment during account creation and ongoing portfolio monitoring to prevent substantial financial losses. These efforts are part of a broader shift toward proactive security in the financial sector, helping institutions build trust while mitigating the costs associated with fabricated credentials. Related news highlights also emphasize the increasing role of advanced automation and global technological trends in modern business environments.
AI detects synthetic identities—which are created by combining real identity elements with manufactured components—by utilizing sophisticated machine learning algorithms to uncover complex fraud patterns that traditional detection methods often miss.
According to the sources, the detection process involves the following methods:
Multidimensional Data Analysis: AI-driven tools, such as Equifax’s “Synthetic Identity Risk,” analyze a combination of identity data, credit history, and behavioral signals to assess the likelihood that an identity is synthetic.
Holistic Assessment: Rather than looking at data points in isolation, the technology applies a holistic approach to evaluate the legitimacy of an applicant in real-time. This is particularly important because synthetic identities often appear legitimate and can go undetected for long periods.
Pattern Recognition: Machine learning is used to identify hidden risks and specific fraud signatures within a lender’s portfolio, allowing for the flagging of fraudulent activity before it results in financial loss.
Continuous Monitoring: Beyond the initial account opening, AI can be used as an account management tool to continuously scan for “hidden portfolio risk,” identifying synthetic identities that may have already been established but have not yet defaulted.
By shifting from reactive recovery to proactive prevention, these AI capabilities help lenders identify fictitious identities used to open credit accounts or obtain loans before the fraudsters stop making payments.
The average financial loss per known synthetic identity is approximately $13,000. This figure represents the average cost or charged-off loss for tradelines reported to Equifax Consumer Credit Files as of December 2025, specifically for those occurring on or after January 1, 2022.
Because these fabricated identities often appear legitimate and can go undetected for long periods, they leave lenders exposed to significant revenue loss and charge-offs when the fraudsters eventually stop making payments on credit accounts or loans.
Lenders can identify hidden risks within their existing portfolios by transitioning from reactive recovery methods to proactive prevention through the use of AI-driven account management tools.
According to the sources, lenders can manage these risks through the following strategies:
Continuous Account Monitoring: Rather than only screening at the time of account opening, lenders can use next-generation fraud detection products as an account management tool to continuously scan their portfolios for synthetic identities that may have gone undetected for long periods.
Multidimensional Analysis: These tools utilize patent-pending technology to analyze a combination of identity data, credit history, and behavioral signals to calculate the likelihood of fraudulent activity.
Machine Learning Algorithms: By leveraging sophisticated machine learning, lenders can uncover complex fraud patterns and specific signatures that traditional detection methods often miss.
Holistic Assessment: Applying a holistic approach allows enterprises to evaluate applicants and existing accounts in real-time, helping to pinpoint fictitious identities before they result in financial loss.
Implementing these AI capabilities helps lenders uncover hidden risks and mitigate potential losses, which average approximately $13,000 per known synthetic identity. This proactive stance not only reduces charge-offs but also helps build long-term trust with legitimate customers.
AI distinguishes between legitimate users and synthetic profiles by moving beyond simple data verification to analyze the deep, underlying patterns of an identity’s history and behavior. Because synthetic identities—which combine real identity elements with manufactured components—often appear legitimate to traditional systems, AI is required to uncover the “hidden risks” that human review or legacy software might miss.
According to the sources, the AI-driven distinction process relies on several key mechanisms:
Multidimensional Analysis: Rather than just checking if a Social Security Number is valid, AI-driven tools like Equifax’s “Synthetic Identity Risk” analyze a combination of identity data, credit history, and behavioral signals. By comparing these data points, the AI can calculate a likelihood of synthetic activity.
Pattern Recognition and Machine Learning: Sophisticated machine learning algorithms are trained to recognize specific fraud signatures and complex patterns that characterize synthetic profiles. These profiles may behave differently in the credit ecosystem than a typical legitimate user, and AI can detect these subtle discrepancies in real-time.
A Holistic Assessment: AI applies a holistic approach to every applicant, assessing the totality of the identity rather than looking at data points in isolation. This allows the system to make more informed decisions about whether a profile is a real person or a fictitious creation designed to obtain loans.
Proactive Portfolio Scanning: AI can also distinguish between legitimate and synthetic profiles within an existing portfolio. By continuously monitoring accounts, it identifies “hidden portfolio risk”—identities that have been established and appear “good” but possess the characteristics of a synthetic identity that has not yet defaulted.
By shifting from reactive recovery to proactive prevention, these AI capabilities allow lenders to identify fictitious identities before they cause the average $13,000 in charged-off loss typically associated with a known synthetic identity.
Source: Equifax