Tag: credit risk

What if some of your best potential borrowers are the ones your traditional credit strategy can't fully see? A credit score can tell lenders a lot about a consumer, but it doesn't always capture the full picture of how someone is managing their financial life. For consumers with nontraditional income patterns or limited credit histories, that incomplete view can mean missed opportunities. In this Ask the Expert session, David Elmore of Experian talks with Michelle Goeppner, Chief Lending Officer at Vantage West Credit Union, about how alternative data can provide additional context around consumer risk, uncover opportunities traditional data alone might miss and help lenders expand their reach without disrupting strategies that already work. Who could lenders be missing? That question is especially important when a consumer’s financial life doesn’t fit neatly into a traditional credit profile. Take gig workers. Someone driving for Uber or delivering for DoorDash likely has a different income pattern than a salaried employee — irregular, seasonal, spread across platforms. That doesn't mean they aren't reliably managing bills, rent and other obligations. It just means a traditional file may not show it. Goeppner has a name for the risk of overlooking that context: FOMM — Fear of Missing Members. You've heard of FOMO — Fear of Missing Out. I think about it as FOMM — Fear of Missing Members. Who are we leaving behind if we're not using it?Michelle Goeppner, Chief Lending Officer For credit unions especially, that's not just a data question — it's a mission question. A partial view of a member's finances can mean missing a member the institution exists to serve. The credit score alone doesn't tell you where someone's headed Traditional credit data is still  foundational to lending decisions. But alternative data — income, cash flow, payment behavior — adds a layer that a credit score alone can't provide. Goeppner illustrates the distinction with two consumers who have exactly the same credit score: I don't know if you're a 640 score on your way to 720 — or are you a 640 headed southwards to 580? It doesn't show me how you're managing your day-to-day financial lifeMichelle Goeppner, Chief Lending Officer Two borrowers can share the same score and be moving in opposite directions. Alternative data helps lenders tell the difference — and put that score in context rather than treating it as the whole story. Start small and layer it in Adopting alternative data doesn't mean overhauling an existing strategy. As Goeppner puts it, it's additive, not a replacement: It's not a rip and replace. You don't have to let go of your existing playbook. It's additive — you layer it in.Michelle Goeppner, Chief Lending Officer Her advice for getting started: Define the problem first. Are you trying to increase approvals, reach more underserved borrowers, or improve decisioning for a specific product? Test before you scale. Revisit loans you've already booked and ask whether alternative data would have changed the outcome — or pilot it on a single product before rolling it out further. Build in governance from day one. Document what changed, where the new data was used, and what results followed. As Goeppner puts it: “Crawl, walk, run. Slow and grow.” More loans without changing the risk profile For Vantage West, the value of that approach has shown up in its lending results. It has been an absolute game changer for us at Vantage West. We have been able to make more loans to our target members, our target segments, without changes to our risk profile.Michelle Goeppner, Chief Lending Officer That distinction matters. The goal isn't approving more loans for its own sake — it's having enough information to recognize good borrowers that traditional data alone would have missed. The result is a fuller picture of the people behind the credit file, and more confidence in deciding who a lender can serve. Explore alternative data with us Alternative data can help lenders add context to traditional credit information for a more complete view of consumers. Experian works with institutions of all sizes to incorporate additional consumer signals into existing lending strategies — strengthening decisioning, managing risk and identifying new opportunities for growth. Learn more Contact us About our experts Michelle Goeppner Chief Lending Officer, Vantage West Credit Union Michelle Goeppner is a dynamic financial services executive with over two decades of experience driving strategic growth, product innovation, and operational excellence across leading credit unions and financial institutions. Currently serving as the Chief Lending Officer at Vantage West Credit Union, Michelle leads the strategic vision for multi-billion-dollar consumer loan and deposit portfolios, as a member of the Executive Coalition. Her expertise spans consumer lending, product management, integrated marketing, and talent development, with a proven track record of leveraging fintech partnerships, automation, and data-driven strategies to optimize portfolio performance and member engagement. Throughout her career, Michelle has held pivotal leadership roles in organizations such as Alliant Credit Union and Discover Financial Services. She is recognized for her collaborative approach, detail-oriented execution, and commitment to developing future female leaders. Michelle’s contributions include founding Alliant’s Women’s Resource Group, serving on advisory councils and boards, and earning multiple industry awards for excellence and innovation. She holds an Executive Certification in Product Management from UC Berkeley, a Master of Science in Integrated Marketing Communications from Roosevelt University, and a Bachelor of Science in Marketing from Northern Illinois University. David Elmore Vice President of Fintech Sales, Experian David Elmore leads a team of fintech sales professionals at Experian focused on helping fintech organizations drive responsible, scalable growth through data-driven analytics and decisioning. With more than 20 years in financial services — a decade of it focused on fintech — he brings deep expertise in applying traditional and alternative data across the customer lifecycle. David and his team partner with fintech leaders to navigate opportunities across acquisition, underwriting, portfolio management, and collections, balancing innovation, risk, and trust.

Learn how PREMIER Bankcard and Experian are helping expand financial access through data, technology and personalized decisioning.

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.

Discover how data-driven risk management strategies are transforming credit risk management in the fintech industry.

Mid-sized banks should take a data-driven approach to implementing credit risk strategies if they want to expand their loan portfolios.

Experian and Plaid are teaming up to power smarter, faster, and more inclusive lending — fueled by real-time cashflow insights.

The days of managing credit risk, fraud prevention, and compliance in silos are over. As fraud threats evolve, regulatory scrutiny increases, and economic uncertainty persists, businesses need a more unified risk strategy to stay ahead. Our latest e-book, Navigating the intersection of credit, fraud, and compliance, explores why 94% of forward-looking companies expect credit, fraud, and compliance to converge within the next three years — and what that means for your business.1 Key insights include: The line between fraud and credit risk is blurring. Many organizations classify first-party fraud losses as credit losses, distorting the true risk picture. Fear of fraud is costing businesses growth. 68% of organizations say they’re denying too many good customers due to fraud concerns. A unified approach is the future. Integrating risk decisioning across credit, fraud, and compliance leads to stronger fraud detection, smarter credit risk assessments, and improved compliance. Read the full e-book to explore how an integrated risk approach can protect your business and fuel growth. Download e-book 1Research conducted by InsightAvenue on behalf of Experian

Here are the top fraud trends and actionable resolutions to help risk managers stay ahead of fraud in 2025.

Whether consumers are shopping for new credit or experiencing financial stress, monitoring their behavior is crucial — even more so in an ever-changing economy. Our latest infographic explores economic trends impacting consumers’ financial behaviors and how Experian’s Risk and Retention TriggersSM enable lenders to detect early signs of risk or churn. Key highlights include: Credit card balances climbed to $1.17 trillion in Q3 2024. As prices of goods and services remain elevated, consumers may continue to experience financial stress, potentially leading to higher delinquency rates. Increasing customer retention rates by 5% can boost profits by 25% to 95%. View the infographic to learn how Risk and Retention Triggers can help you advance your portfolio management strategy. Access infographic

How can lenders ensure they’re making the most accurate and fair lending decisions? The answer lies in consistent model validations.

Learn how leveraging credit attributes can help you reduce your risk exposure, prioritize accounts, and modify your pre-collections strategy.

Ensuring fair lending practices while leveraging machine learning models is crucial for organizations committed to ethical and compliant operations.

For lenders, mitigating first payment default requires data, advanced analytics, customer engagement, and agile risk management.

New approaches to model operations are also helping lenders accelerate their machine learning model development processes.

Learn how expanded data, AI-driven models, and increased automation can help you enhance your credit risk management strategies.