Loading...

Stop Trying to Collect from Synthetic Identities

by Guest Contributor 4 min read July 15, 2019

Friend or foe?

Sophisticated criminals put a great deal of effort into creating convincing, verifiable personas (AKA synthetic identities). Once the fictional customer has embedded itself in your business, everything from the acquisition of financial instruments to healthcare benefits, utility services, and tax filings and refunds become vulnerable to synthetic identity fraud.

Information attached to synthetic IDs can run several levels deep and be so complete that it includes public record data, credit information, documentary evidence and social media profiles that may even contain photo sets and historical details intended to deceive—all complicating your efforts to identify these fake customers before you do business with them.

See real-world examples of how synthetic identity fraud is souring various markets – from auto and healthcare to financial services and public sector – in our tip sheet, Four common synthetic scenarios.

Stopping synthetic ID fraud — at the door and thereafter.

There are efforts underway in the market to collectively improve your ability to identify, shut down and prevent synthetic identities from entering your portfolio. This overall trend is great news for the future, but there are also near-term solutions you can apply to protect your business starting now. While it’s important to identify synthetic identities when they knock on your door, it’s just as important to conduct regular portfolio checkups to prevent negative impacts to your collections efforts. Every circumstance has its own unique parameters, but the overarching steps necessary to mitigate fraud from synthetic IDs remain the same:

  1. Identify current and near-term exposure using targeted segmentation analysis.
  2. Apply technology that alerts you when identity data doesn’t add up.
  3. Differentiate fraudulent identities from those simply based on bad data.
  4. Review front- and back-end screening procedures until they satisfy best practices.
  5. Achieve a “single view of the customer” for all account holders across access channels—online, mobile, call center and face-to-face.

The right tools for the job.

In addition to the steps mentioned above, stopping these fake customers from entering and then stealing from your organization isn’t easy—but with the right tools and strategies, it is possible. Here are a few of our top recommendations:

Forensics

Isolate and segment identities based on signals received during early account pathing, from both individuals and their device. For example, even sophisticated fraud networks can’t mimic natural per-device user interaction because these organizations work with hundreds or thousands of synthetic identities using just a few devices. It’s highly unlikely that multiple geographically separate account holders would share the same physical device.

High-risk fraud scores

Not all synthetic identity fraud manifests the same way. Using sophisticated logic and unique combinations of data, a high-risk fraud score looks at a consumer’s credit behavior and credit relationships over time to uncover previously undetectable risk. These scores are especially successful in detecting identities that are products of synthetic identity farms. And by targeting a specific data set and relationships, you can maintain a frictionless customer experience and reduce false positives.

Analytics

Use a solution that develops models of bad applicant behavior, then compares and scores your portfolio against these models. There isn’t a single rule for detecting fraudulent identities, but you can develop an informed set of rules and targeted models with the right service partner. Cross-referencing models designed to isolate high-risk identity theft cases, first-party or true-name fraud schemes, and synthetic identities can be accomplished in a decisioning strategy or via a custom model that incorporates the aggregate scores and attributes holistically.

Synthetic identity detection rules

These specialized rules consist of numerous conditions that evaluate a broad selection of consumer behaviors. When they occur in specific combinations, these behaviors indicate synthetic identity fraud. This broad-based approach provides a comprehensive evaluation of an identity to more effectively determine if it’s fabricated. It also helps reduce the incidence of inaccurately associating a real identity with a fictitious one, providing a better customer experience.

Work streams

Address synthetic identities confidently by applying analytics to work streams throughout the customer life cycle:

  • Credit risk assessment
  • Know Your Customer/Customer Identification Program checks
  • Risk-based identity proofing and authentication
  • Existing account management
  • Manual reviews, investigations and charge-offs/collections activities

Learn more about these tools and others that can help you mitigate synthetic identities in our white paper, Synthetic identities: getting real with customers.

If your organization is like most, detecting SIDs hasn’t been your top priority. So, there’s no time to waste in preventing them from entering your portfolio. Criminals are highly motivated to innovate their approaches as rapidly as possible, and it’s important to implement a solution that addresses the continued rise of synthetic IDs from multiple engagement points.

With the right set of analytics and decisioning tools, you can reduce exposure to fraud and losses stemming from synthetic identity attacks from the beginning and across the customer life cycle. We can help you detect and mitigate these fake customers before they become delinquent. Learn more

Related Posts

For many banks, first-party fraud has become a silent drain on profitability. On paper, it often looks like classic credit risk: an account books, goes delinquent, and ultimately charges off. But a growing share of those early charge-offs is driven by something else entirely: customers who never intended to pay you back. That distinction matters. When first-party fraud is misclassified as credit risk, banks risk overstating credit loss, understating fraud exposure, and missing opportunities to intervene earlier.  In our recent Consumer Banker Association (CBA) partner webinar, “Fraud or Financial Distress? How to Differentiate Fraud and Credit Risk Early,” Experian shared new data and analytics to help fraud, risk and collections leaders see this problem more clearly. This post summarizes key themes from the webinar and points you to the full report and on-demand webinar for deeper insight. Why first-party fraud is a growing issue for banks  Banks are seeing rising early losses, especially in digital channels. But those losses do not always behave like traditional credit deterioration. Several trends are contributing:  More accounts opened and funded digitally  Increased use of synthetic or manipulated identities  Economic pressure on consumers and small businesses  More sophisticated misuse of legitimate credentials  When these patterns are lumped into credit risk, banks can experience:  Inflation of credit loss estimates and reserves  Underinvestment in fraud controls and analytics  Blurred visibility into what is truly driving performance   Treating first-party fraud as a distinct problem is the first step toward solving it.  First-payment default: a clearer view of intent  Traditional credit models are designed to answer, “Can this customer pay?” and “How likely are they to roll into delinquency over time?” They are not designed to answer, “Did this customer ever intend to pay?” To help banks get closer to that question, Experian uses first-payment default (FPD) as a key indicator. At a high level, FPD focuses on accounts that become seriously delinquent early in their lifecycle and do not meaningfully recover.  The principle is straightforward:  A legitimate borrower under stress is more likely to miss payments later, with periods of cure and relapse.  A first-party fraudster is more likely to default quickly and never get back on track.  By focusing on FPD patterns, banks can start to separate cases that look like genuine financial distress from those that are more consistent with deceptive intent.  The full report explains how FPD is defined, how it varies by product, and how it can be used to sharpen bank fraud and credit strategies. Beyond FPD: building a richer fraud signal  FPD alone is not enough to classify first-party fraud. In practice, leading banks are layering FPD with behavioral, application and identity indicators to build a more reliable picture. At a conceptual level, these indicators can include:  Early delinquency and straight-roll behavior  Utilization and credit mix that do not align with stated profile  Unusual income, employment, or application characteristics High-risk channels, devices, or locations at application Patterns of disputes or behaviors that suggest abuse  The power comes from how these signals interact, not from any one data point. The report and webinar walk through how these indicators can be combined into fraud analytics and how they perform across key banking products.  Why it matters across fraud, credit and collections Getting first-party fraud right is not just about fraud loss. It impacts multiple parts of the bank. Fraud strategy Well-defined quantification of first-party fraud helps fraud leaders make the case for investments in identity verification, device intelligence, and other early lifecycle controls, especially in digital account opening and digital lending. Credit risk and capital planning When fraud and credit losses are blended, credit models and reserves can be distorted. Separating first-party fraud provides risk teams a cleaner view of true credit performance and supports better capital planning.  Collections and customer treatment Customers in genuine financial distress need different treatment paths than those who never intended to pay. Better segmentation supports more appropriate outreach, hardship programs, and collections strategies, while reserving firmer actions for abuse.  Executive and board reporting Leadership teams increasingly want to understand what portion of loss is being driven by fraud versus credit. Credible data improves discussions around risk appetite and return on capital.  What leading banks are doing differently  In our work with financial institutions, several common practices have emerged among banks that are getting ahead of first-party fraud: 1. Defining first-party fraud explicitly They establish clear definitions and tracking for first-party fraud across key products instead of leaving it buried in credit loss categories.  2. Embedding FPD segmentation into analytics They use FPD-based views in their monitoring and reporting, particularly in the first 6–12 months on book, to better understand early loss behavior.  3. Unifying fraud and credit decisioning Rather than separate strategies that may conflict, they adopt a more unified decisioning framework that considers both fraud and credit risk when approving accounts, setting limits and managing exposure.  4. Leveraging identity and device data They bring in noncredit data — identity risk, device intelligence, application behavior — to complement traditional credit information and strengthen models.  5. Benchmarking performance against peers They use external benchmarks for first-party fraud loss rates and incident sizes to calibrate their risk posture and investment decisions.  The post is meant as a high-level overview. The real value for your teams will be in the detailed benchmarks, charts and examples in the full report and the discussion in the webinar.  If your teams are asking whether rising early losses are driven by fraud or financial distress, this is the moment to look deeper at first-party fraud.  Download the report: “First-party fraud: The most common culprit”  Explore detailed benchmarks for first-party fraud across banking products, see how first-payment default and other indicators are defined and applied, and review examples you can bring into your own internal discussions.  Download the report Watch the on-demand CBA webinar: “Fraud or Financial Distress? How to Differentiate Fraud and Credit Risk Early”  Hear Experian experts walk through real bank scenarios, FPD analytics and practical steps for integrating first-party fraud intelligence into your fraud, credit, and collections strategies.  Watch the webinar First-party fraud is likely already embedded in your early credit losses. With the right analytics and definitions, banks can uncover the true drivers, reduce hidden fraud exposure, and better support customers facing genuine financial hardship.

by Brittany Ennis 4 min read February 12, 2026

Discover why Experian’s unified fraud prevention platform, backed by decades of data stewardship and AI innovation, is the trusted choice for enterprises seeking scalable, compliant, and transparent identity verification solutions.

by Laura Davis 4 min read December 8, 2025

Learn how you can mitigate e-commerce fraud with identity verification and fraud prevention best practices.

by Theresa Nguyen 4 min read December 3, 2025