As the December 31st deadline approaches for FTC enforcement of the Red Flags Rule, we still seem quite a ways off from getting out from under the cloud of confusion and debate related to the definition of ‘creditor’ under the statutory provisions. For example, the Thune-Begich amendment to “amend the Fair Credit Reporting Act with respect to the applicability of identity theft guidelines to creditors” looks to greatly narrow the definition of creditor under the Rule, and therefore narrow the universe of businesses and institutions covered by the Red Flags Rule. The question remains, and will remain far past the December 31 enforcement deadline, as to how narrow the ‘creditor’ universe gets. Will this amendment be effective in excluding those types of entities generally not in the business of extending credit (such as physicians, lawyers, and other service providers) even if they do provide service in advance of payment collection or billing? Will this amendment exclude more broadly, for example ‘buy-here, pay-here’ auto dealers who don’t extend credit or furnish data to a credit reporting agency? Finally, is this the tip of an iceberg in which more entities opt out of the requirement for robust and effective identity theft prevention programs? So one has to ask if the original Red Flags Rule intent to “require many businesses and organizations to implement a written Identity Theft Prevention Program designed to detect the warning signs – or “red flags” – of identity theft in their day-to-day operations, take steps to prevent the crime, and mitigate the damage it inflicts” still holds true? Or is the idea of protecting consumer identities only a good one when it is convenient? It doesn’t appear to be linked with fraud risk as healthcare fraud, for example, is of major concern to most practitioners and service providers in that particular industry. Lastly, from an efficiency perspective, this debate would likely have been better timed at the drafting of the Red Flags Rule, and prior to the implementation of Red Flags programs across industries that may be ultimately excluded.
Red Flags Rule – just weeks until the FTC enforcement date of December 31. Well beyond that for clarity.
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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.
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