Experian Celebrates National Fintech Day

MBS Pricing & New Data In our previous post, we described the Current Second Lien Balance field, which is one of over 2,000 fields in the new Experian Mortgage Loan Performance (MLP) dataset. We showed that the Current Second Lien Balance field meets our three-pronged materiality standard for new data delivery: New: Provides information not available in existing datasets (i.e., orthogonal to currently available data). Material: Impacts a sizeable portion of the MBS universe. Significant: Differentiates collateral performance by a large enough margin to influence trading and risk management decisions. In this article, we discuss another field that satisfies the above criteria: Student Loan Balance.  We evaluate this field in the context of these criteria. First, however, we provide a summary of the MLP dataset and how it compares to standard GSE loan-level data available today. Standard GSE Data vs. Experian Mortgage Loan Performance (MLP) Data The MLP dataset contains thousands of fields describing mortgage performance from each borrower, loan, and property perspective, all refreshed monthly (including, amongst other things, new credit scores and refinance inquiry activity, loan performance, filed junior liens, and AVM values).  MLP differs from loan-level data provided byFreddie Mac, Fannie Mae, and Ginnie Mae, which the vast majority of market participants solely rely on, in a number of ways: Standard data provided by the GSEs and GNMA does not contain all the information necessary for accurate forecasting of mortgage prepayment and credit performance. Basic, critical fields like borrower’s current credit score and current junior liens on the property are missing. The new Mortgage Loan Performance (MLP) dataset from Experian contains borrower, loan, and property data fields covering the entire mortgage universe, including Agency, Non-Agency, and Esoteric mortgage products (CES, HELOC, Reverse), both securitized and non-securitized. MLP enables full three-dimensional (borrower + loan + property) tracking with persistent keys for borrower (before and after refinancing), loan (in securities/deals even after exit due to payoffs or buyouts, including before and after MSR sales), and property.  This enables end-to-end analysis of each borrower’s (and property’s) mortgage experience throughout their credit lifecycle. New, Material and Significant Field:  Student Loan Debt MLP contains a number of fields describing each mortgage borrower’s student debt load, including amounts in repayment, forbearance and collections; estimated interest rate, time remaining until forbearance expiration, and more. In the interest of simplicity, for this article we’ll focus on a single student loan-related field within MLP: Student Loans Balance, which is defined as the total balance on open non-deferred student trades reported in the last 3 months. Is Information Regarding Student Loans New to Markets? Standard loan-level data disclosed by the GSEs and GNMA contain no student-loan-specific fields. Theoretically, fields related to DTI at origination might capture some aspect of student loan debt. So, in the best case scenario for an investor relying solely on standard disclosure, a DTI value as of origination is provided -- yet is never updated as the loan seasons and the borrower’s debt and income change (see more here).  But in the case of federal student loan debt attached to mortgages originated from early 2020 to late 2023, the level of detail provided by disclosure may be even more unknown due to COVID-era repayment and reporting moratoriums. The student loan repayment moratorium was a temporary federal policy that paused required payments, set interest rates to 0%, and suspended collections on most federally-held student loans. The moratorium began in March 2020, with payments resuming in October 2023, making it approximately 3.5 years in duration—the longest consumer credit payment pause in U.S. history. (Source: NCUA ) During the moratorium, student loan-related debt loads may have been understated as federal loans were in a temporary state of $0 repayment.  As an alternative to leaving student loan debt completely out of DTI calculations, an imputed payment equal to only 0.50% of the outstanding balance was often used as a placeholder for a borrower’s DTI calculation. As a result, mortgages originated during the moratorium may have artificially low reported DTIs for borrowers with student loan debt, materially understating true post-moratorium debt .  Accordingly, prepayment risk for these loans is likely overstated in mainstream market models. Standard data only reports information related to the primary mortgage and does not include any details on the borrower’s other debts with the exception of DTI at origination, which is never updated throughout the life of the loan. In contrast, MLP provides a comprehensive view of the borrower’s full credit profile, including other obligations such as credit cards, mortgages on other properties, student loan balances, and much more. Is Student Loan debt material to the residential mortgage market? Approximately $11 trillion of residential mortgage loans were originated during the student loan payment moratorium (Source: Experian MLP Dataset), a period marked by historically low mortgage rates during the COVID era.  As discussed above, DTI data contained in standard market disclosure may be particularly inaccurate for these loans. As the Wall Street Journal recently reported, a new report from the Federal Reserve of New York shows a rise in student loan default rates by age group.  Student l Of today’s $13 trillion in outstanding mortgage debt, more than 10% of that debt ($1.5 trillion) is associated with borrowers who carry student loan debt.  For these borrowers, the average amount of student loan debt outstanding is approximately $50,000, versus a mortgage balance of approximately ~$289,000. In other words, the average student loan debt balance is almost 20% of the mortgage balance for the average borrower who carries both. For this set of borrowers, the average monthly payment is approximately $400 for student loan vs. approximately $2,200 for 1st lien mortgage—so that monthly student loan payments are a significant debt load, approximately 20% of the monthly mortgage payment.  (Source:  Experian MLP Dataset) Is the effect of student loan debt a significant driver of performance? Figure 1 illustrates prepayments by student loan balance for a sample of loans drawn from MLP. The chart illustrates that borrowers with larger student loan balances prepay much more slowly, likely because some are effectively locked out of refinancing once student loan payments resume due to elevated DTI. The debt-to-income (DTI) ratio calculated using actual student loan payments may be significantly higher than the DTI calculated during the moratorium, in some cases exceeding GSE eligibility thresholds. As illustrated in Figure 1, for in-the-money (ITM) collateral, the differential between loans with material student loan balances (greater than $200,000) and loans with no student debt can reach up to 5 CPR. Notably, even for out-of-the-money (OTM) collateral, loans with student debt prepay 1 to 3 CPR slower, likely reflecting reduced mobility due to tighter financing constraints when purchasing a new home. Pools with otherwise similar prepayment characteristics may exhibit different prepayment behavior depending on the distribution of student loan exposure within their collateral. In addition, because loans with student debt tend to prepay more slowly, this effect increases over time due to burnout: loans without student debt prepay and exit the pools more quickly, leaving a higher concentration of slower-paying loans behind.  Given that 10% of the $13 trillion outstanding mortgage market is associated with borrowers who have student loans (Source:  Experian MLP dataset)—and that student loans have a meaningful impact on prepayments—many pools issued between March 2020 and October 2023 may be subject to this student loan debt CPR throttle, and therefore mispriced by investors relying exclusively on standard market data. Fig 1. Prepayment S-Curve: Student Loans Balance Source:  Experian MLP dataset hosted on IVolatility Data-Driven Platform   _____________________________________________________ Michael Pyatski advises MBS traders, portfolio managers, quants, risk managers, loan originators, and technology professionals on making informed, data-driven business decisions that drive revenue growth, enhance risk management, and reduce trading costs. With more than 15 years of experience as an Agency RMBS trader—including serving as Head of the Proprietary Trading Desk at BNP Paribas—Michael developed and successfully implemented relative-value, data-driven profitable trading strategies to capture market opportunities embedded in data but not fully priced by the market. His trading experience, combined with a Ph.D. in econometrics, led him to found the Data-Driven Portal (https://datadrivenportal.com/), a platform that provides advanced technology for MBS trading and risk management. The platform’s No-Model Data-Driven technology leverages big data, econometric analysis, and AI to help traders identify relative-value opportunities in RMBS markets and generate above-market, risk-adjusted returns. _____________________________________________________

When disputes become a fraud strategy  First-party fraud is quietly reshaping the risk landscape for merchants. Unlike third-party fraud, it originates from the consumer, often through a dispute that triggers a chargeback. Mastercard’s research highlights a shift in consumer dispute behavior: when consumers dispute a transaction and later realize it was a mistake, many do not rectify their error and reverse the dispute. Across 4,500 surveyed consumers, 775 admitted to disputing a transaction, and up to 37% admitted to not correcting a mistaken dispute (consumer fraud originates with). Convenience remains the driving force for consumers, who increasingly turn to their bank first when a transaction looks questionable rather than contacting the merchant. In fact, 76% of consumers prefer resolving disputes through their bank rather than the merchant. This removes the merchant’s ability to resolve the issue and avoid costly chargebacks, creating higher operational costs and risk exposure. This is especially problematic considering ClearSale estimates that 40% of consumers who request a chargeback will do so again within 90 days.  What could be causing more consumers to use the dispute process?  Mastercard’s consumer research sheds light into the shift of behavior. Among Gen Z, 26% admitted they did not contact the merchant or app to return funds after realizing the dispute was wrong, compared with 22% of Millennials and 18% of Gen X. What’s driving this trend? Globally, chargebacks are on the rise, projected to reach 324 million transactions by 2028, a 24% increase over 2025 estimates, according to Mastercard. So, what is driving this trend? Economic pressure  U.S. household debt reached $18.39 trillion in Q2 2025, with credit card balances at $1.21 trillion (up $27 billion in a quarter). At the same time, 39% of households report declining income, and 70% expect a recession within 12 months. These pressures make short-term financial relief, even through disputes — tempting.  BNPL and buyer’s remorse  Buy now,pay later (BNPL) usage is surging 52% of U.S. consumers have used BNPL in 2025, and Gen Z leads the trend, with 59% opting for BNPL. The average BNPL borrower originated 9.5 loans in a year, often stacking multiple loans across providers. This creates a cycle of deferred pain and buyer remorse, which can lead to disputes. Lack of transparency and complex subscription models   One of the most significant accelerators of first-party fraud is the ease with which consumers can file disputes today. According to Mastercard's 2025 State of Chargeback Report, mobile banking apps and digital wallets have transformed dispute initiation from a multistep process into something that can be completed in seconds. If the consumer doesn’t recognize a transaction or the name of the merchant, they are able to raise a dispute in a couple of taps. Recurring billing models and complex subscription models also amplifies the problem. If a consumer forgets about a subscription service or doesn’t recognize a billing descriptor, this can lead to a dispute that could have been avoided with better transparency.  “Disputes are no longer just a backend operational issue — they’re becoming a frontline fraud vector. When consumers default to their bank instead of the merchant, context is lost, resolution slows, and chargebacks escalate. The opportunity now is to reintroduce transparency and collaboration earlier in the journey, so issues are resolved before they turn into costly disputes.” Gaurav Mittal, Executive Vice President of Ethoca at Mastercard Dispute systems designed for consumer protection can sometimes be misused, increasing the frequency of disputes. As card-not-present transactions grow, protecting against both third-party fraud and first-party fraud is essential.   The solution: tools consumers want — and merchants need Consumers aren’t opposed to security. In fact, 85% prioritize security over convenience, and 83% expect businesses to address their security and privacy concerns. They want visible and invisible protections that make them feel safe without slowing them down.  Merchants can meet this expectation, and reduce fraud, by adding intelligent safeguards at checkout: Behavioral biometrics: In Experian’s consumer survey, consumers ranked behavioral biometrics among the most trusted methods (72% feel it’s secure). These tools analyze typing speed, mouse movement, and hesitation patterns to distinguish genuine users from bots or fraudsters, invisibly and in real time. Physical biometrics: 76% of consumers trust physical biometrics (fingerprint, facial recognition) more than passwords. Offering biometric login or checkout options gives consumers confidence while reducing reliance on vulnerable credentials.  Passive identity verification: Experian’s patented account ownership verification matches payment card numbers to identity attributes without requiring extra input. This protects merchants from stolen card fraud while keeping checkout friction low. Device and network intelligence: Secondary device checks and network analysis can silently validate identity during guest checkout or BNPL flows, reducing risk without slowing conversion.   Enhancing transaction clarity: Consumers are open to sharing more data for security: 77% would share more when shopping online, and 76% with financial institutions. Secure, real-time data exchange between merchants and issuers, such as through Mastercard’s First-Party Trust program, can strengthen fraud detection and reduce false declines.  Better purchase recognition: Improving purchase recognition in digital banking apps can help reduce disputes caused by consumers confusing their own transactions. Providing clear purchase descriptors, itemized receipts and better subscription management gives users the details they need to understand their purchase history and prevent first-party fraud.  “Reducing first-party fraud isn’t about adding friction; it’s about adding clarity. When merchants can surface the right information at the right moment, they not only prevent disputes, but they also strengthen trust and protect long-term customer relationships.” Gaurav Mittal, Executive Vice President of Ethoca at Mastercard Closing thought  First-party fraud’s impact extends beyond operations, affecting profitability, customer trust and brand reputation. Merchants that act now to strengthen checkout security with visible and invisible protections will reduce losses, protect trust and deliver the seamless experiences consumers expect. Learn more Read part 1

Electric vehicle (EV) registration growth has become a common topic of discussion throughout the automotive industry for the last few years, but the bigger story may lie in what consumers are choosing when they return to market for their next vehicle. According to Experian’s Automotive Market Trends Report: Q1 2026, the bulk of EV owners (72.6%) purchased another EV, while 17.7% replaced their EV with a gas-powered vehicle and 5.6% switched to a hybrid this quarter. A similar trend was seen in hybrid owners, as 54.9% remained loyal to the fuel type through the quarter, while 32.7% replaced their hybrid with a gas-powered vehicle and 7.5% switched to an EV. Notably, 78.2% of consumers with gas-powered vehicles stayed with the same fuel type, with 5.6% swapping their gas vehicle for a hybrid and only 4.5% transitioning to an EV through Q1 2026. These purchase styles suggest that while most consumers are not making a direct leap from gasoline to fully electric vehicles, some are beginning their electrified journey through hybrid ownership. At the same time, the high rate of fuel-type loyalty across all powertrain categories highlights the importance of the ownership experience. Consumers who are satisfied with their current vehicle can often be inclined to remain with the same segment rather than exploring alternative fuel types. New vehicle registration trends reflect changing consumer preferences Looking at the new vehicle registration data from a broader level, gas-powered vehicles experienced a slight uptick, coming in at 69.5% through Q1 2026, from 67.3% last year. Meanwhile, hybrids continue to grow, going from 12.1% to 13.5% year-over-year while EVs steadily decline from 7.8% last year to 5.6% this quarter. As consumers weigh their next vehicle purchase, many seem to be sticking with the standard gas-powered choice, and others are finding a happy medium in hybrid vehicles. And while EVs receive much of the industry’s attention, buyers are exploring alternatives that allow them to adopt the electrified vehicles incrementally rather than all at once. To learn more about vehicle market trends, view the full Automotive Market Trends Report: Q1 2026 presentation on demand.