Most banks have already invested heavily in tools for electronic identity verification systems (eIDV) that include know your customer (KYC), anti-money laundering (AML), sanctions screening and behavioral analytics. But the rise in synthetic identity theft begs the question — what’s missing?
It’s the lack of precision in how identity data is validated and connected. Synthetic fraud thrives when identity attributes are verified in isolation rather than as a connected, real-world profile. For fraudsters, exploiting small disconnects across a financial institution’s legacy systems and platforms works better than tricking a single control. For financial institutions, validating identity in real time and at the top of the customer journey creates a smart, scalable fraud defense and closes the gaps.
Synthetic Fraud Capitalizes on System Weaknesses
Historically, fraud controls have focused on transaction monitoring. Systems alert banking operators to unusual payments, anomalies in the velocity of transactions or behavioral red flags that could indicate identity theft.
Synthetic fraud operates differently, blending legitimate data with fabricated details such as a false name, recycled phone number or mismatched address. The address provided does indeed exist, and the phone number connects. The individual’s credit file shows a limited but growing history, and their device appears normal. A fictional identity based on this data can pass basic validation because each element checks out individually, yet there is no real or verifiable relationship among the data points.
These are the same reasons synthetic fraud is so difficult to solve. The intended confusion among data points creates false positives and negatives, both of which impact the bottom line.
In a false positive situation, a legitimate user is flagged for fraud, almost instantly increasing cost and potential for churn. Customers get frustrated and may leave. The organization loses revenue and faces increased operational costs from manual review.
In a false negative scenario, actual fraud slips through, resulting in chargebacks, uncollectible debt, reputational damage and regulatory fines. The potential risk is huge, as synthetic identities embed themselves into portfolios and mature over time, surfacing as significant losses months or even years later.
Layer Data Quality Tools Into Existing Systems
To strengthen digital onboarding and fraud defenses, institutions should focus on five interconnected capabilities that fuel higher-level eIDV and can be executed in real-time: geolocation verification, liveness detection, facial matching, document verification and address verification.
Geolocation tools compare an applicant’s stated address with the physical location of their device during onboarding. An irregularity doesn’t automatically prove fraud, but it creates a signal worth investigating. When paired with precise address standardization and geocoding, institutions can also detect broader patterns, such as clusters of suspicious applications tied to specific properties.
Advanced liveness detection and facial matching further confirm that a real person is present during the application process. Facial matching then compares a live selfie to a government-issued ID, tolerating minor variations while flagging meaningful discrepancies.
Market Forces Accelerate Fraud Risk
U.S. institutions must meet Customer Identification Program requirements, AML monitoring standards, beneficial ownership rules and sanctions screening obligations. Yet when identity data is flawed at onboarding, every downstream control is just weaker. Regulatory compliance activities face noise created in screening systems and distorted risk models.
New digital practices also add complexity but are necessary to meet expectations of legitimate customers. Criminal elements are responding with technological prowess of their own. They leverage AI, automation, data aggregation and synthetic profile generation tools to create identities that evolve over time. When the heist occurs, the exposure is often substantial and difficult to trace back to a lone onboarding failure.
Validate Real Relationships Among Data
Strong address and identity verification is a must, but data quality tools can also be supported by AI/ML (artificial intelligence and machine learning)-based data modeling and advanced matching algorithms aligned with a business’s particular needs.
It’s a real balancing act to solve the challenge – but banks only win with authentic customers that can be well-served with confidence and trust throughout the relationship.
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