All posts by Brittany Ennis

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.

Why data analytics matters more to fintech lenders Unlike traditional financial institutions, fintechs grow through rapid experimentation. They build, iterate and deploy at a pace that rewards agility but often exposes gaps in visibility. That’s why unified, trusted data has become essential infrastructure. Many fintech leaders note that building technology is rarely the barrier; the real challenge is ensuring their data can move as quickly as their decisions. Analytics plays a central role in closing that gap by providing real-time insight that supports speed, accuracy and confidence. Fintech analytics goes far beyond reporting. It’s about connecting credit, cash flow and behavioral data to reveal intent, detect risk early and personalize offers. The leaders in this space aren’t those with the most data, but those who can turn it into confident, compliant action. How fintechs are using analytics to stay ahead 1. Managing risk in real timeFintech lenders are increasingly recognizing that the boundary between fraud and credit risk is disappearing. Rather than treating them as separate disciplines, leading firms are developing unified approaches that detect early behavioral signals that indicate financial stress or potential fraud well before losses occur. By fusing transactional and credit data, they are creating adaptive risk models that evolve in real time and deliver faster, more confident decisions.  2. Unlocking value from cash flow and alternative dataFintechs are finding that cash flow tells a richer story than credit alone. By layering bank transaction data on top of bureau insights, many have improved model accuracy and expanded their reach to consumers who might otherwise be overlooked. Analysis of BNPL activity, primary account behavior and income patterns is also helping lenders tailor offers with greater precision and fairness.  3. Accelerating innovation with governed AIAI is driving model development and decisioning speed, but governance remains a universal concern. Fintech leaders acknowledge the challenge of balancing innovation with regulatory transparency, emphasizing the need for faster validation, clearer audit trails and explainable outputs. The next frontier isn’t just building smarter models but ensuring those models are trusted by compliance teams, investors and consumers alike. Persistent pain points in fintech data integration For many fintechs, they are challenged by knowing, that the data exists, but the stitching between sources slows everything down. Even the most advanced fintechs face familiar challenges: Fragmented data ecosystems: Transactional, credit, and behavioral data often live across disconnected systems, creating blind spots and latency. Data quality and recency: Incomplete or outdated information weakens the accuracy of AI models. Scalability and governance: Rapid growth amplifies infrastructure strain and regulatory complexity. Where Experian gives fintechs an edge Fintechs have a need for control, speed and trust — a balance that’s difficult to achieve with point solutions or legacy integrations. That’s where Experian differentiates. The Experian Ascend Platform™ brings data, analytics and decisioning together in a single, secure environment so fintechs can: Access unified, model-ready data that combines credit, cash flow and alternative sources. Build, test, and deploy predictive models through sandbox capabilities that mirror real-world conditions. Enhance transparency and compliance with built-in AI governance and audit tools. Integrate seamlessly through flexible APIs designed for engineering-led teams. Several fintech leaders have stated that Experian’s Ascend platform’s performance and transparency help them move faster without compromising oversight, giving them the speed of an in-house build with the reliability of a proven data partner. The takeaway: from data collection to confident decisioning For fintech lenders, analytics is no longer a back-end function. It is a strategic capability that drives every decision. Those who unify their data, operationalize insights responsibly and automate decisions with transparency will set the pace for the next wave of credit innovation. Experian continues to partner with leading fintechs to transform fragmented data into real-time intelligence, powering smarter lending, sharper risk controls and stronger customer experiences built on trusted data. Discover how Experian’s fintech solutions are helping fintechs harness analytics to accelerate growth and innovation. Learn more

Reporting positive rental payment histories to credit bureaus has been in the news more than once in recent months. In early November, Freddie Mac announced it will provide closing cost credits on multifamily loans for owners of apartment properties who agree to report on-time rental payments. In July, California began requiring multifamily properties that receive federal, state or local subsidies to offer each resident in a subsidized apartment home the option of having their rental payments reported to a major credit bureau. And while reporting positive rental payments to credit bureaus may not yet be part of the multifamily mainstream, forward-thinking operators have already been doing it for years. Below is a quick primer on this practice and its benefits. Why do renters need this service? A strong, positive credit history is critical to securing car loans, credit cards and mortgages – and doing so at favorable interest rates. Unfortunately, unlike homeowners, apartment residents traditionally have not seen a positive impact on their credit reports for making their rent payments on time and in full, even though these payments can be very large and usually make up their largest monthly expense. In fact, renters are seven times more likely to be credit invisible – meaning they lack enough credit history to generate a credit score – than homeowners, according to the Credit Builders Alliance (CBA). This especially impacts lower-income households and communities of color. Renters make up approximately 60% of the U.S. households that make less than $25,000 a year, while Black and Hispanic households are twice as likely as White households to rent, according to the CBA. Experian is among the organizations working with the Consumer Data Industry Association (CDIA) on the association's Rental Empowerment Project. Through the REP, CDIA and its partner organizations seek to increase the reporting of rental payment history information by landlords and property managers through the development and adoption of a uniform, universal data reporting format for landlords and property managers to use. How does reporting positive rental payments to credit bureaus have an impact on a resident's credit history? The impact on any individual renter will obviously vary because of a wide array of factors. But to get some sense of the potential impact reporting on-time rental payments can have, consider the results of the CBA's Power of Rent Reporting pilot. In that test, 100% of renters who started off with no credit score became scorable at the near prime or prime level. In addition, residents with subprime scores saw their score increase by an average of 32 points. How does reporting positive rent payments benefit rental-housing owners and operators? Reporting positive rental payments provides residents with a powerful incentive to pay their rent on time and in full. And because there’s not a huge percentage of apartment communities currently doing this, helping residents build their credit history in this manner can offer a real competitive advantage. Learn more