Make Smarter, Faster Decisions with a Custom Machine Learning Model

Lending hasn’t slowed down—but many decisioning processes have. Applications are coming in faster. Fraud is becoming more sophisticated. Borrowers expect near-instant responses. And yet, inside many organizations, decisions are still being made across fragmented systems, manual reviews, and rigid strategies that weren’t designed and aren’t optimized for today’s environment. That broadening gap isn’t just an operational issue but often stems from a lack of innovation as well. And it’s quietly costing lenders growth, efficiency, and competitive position. When decisioning falls behind, some symptoms are easy to recognize, like applications taking days to process, teams overloaded with manual reviews, and credit and fraud decisions happening in separate platforms. Others are not as obvious, but arguably more impactful, slipping bottom lines and fraud and therefore losses lurking in lenders’ portfolios. The root issue is a fragmented infrastructure. Experian has reported that while 79% of financial institutions surveyed globally want fewer vendors or more unified approaches, they typically use eight or more tools across credit, fraud and compliance. As most decisioning environments cannot integrate data, adapt strategies, and execute decisions in real time, lenders often have to make tradeoffs. Speed vs. accuracy; growth vs. risk; and automation vs. control are just some. Meanwhile, the market has moved on. Leading lenders are no longer optimizing individual steps. They’re rethinking decisioning as a connected, intelligent system. Gaps forming from status quo in 8 key decision areas Across the lending lifecycle, there are eight critical moments where decisioning can either accelerate growth or create friction. Pre-qualification: Pre-qualification should expand your funnel with confidence. But limited data access and static criteria often result in overly conservative targeting or missed opportunities. Additionally, the delay in acting on a pre-qualification funnel highlights a key area for opportunity among many lenders. Instant credit decisions: Customers expect real-time outcomes. When decisions rely on manual intervention or fragmented inputs, speed and conversions suffer. Prescreen and targeting: Disconnected data and rigid segmentation can lead to poorly aligned offers, reducing response rates and wasting acquisition spend. Credit line management: Without dynamic strategies, credit lines may be too restrictive (limiting growth) or too aggressive (increasing risk). Early delinquency management: Missed early signals and delayed interventions make it harder to prevent accounts from deteriorating. Mid- and late-stage delinquency: Strategies that don’t adapt to evolving borrower behavior reduce recovery effectiveness and increase losses. Collections and recovery: Manual, one-size-fits-all approaches limit recovery rates and increase operational cost. Ongoing strategy optimization: Perhaps the most overlooked gap: many lenders lack the ability to continuously test, learn, and refine decision strategies as conditions change. What these gaps are really costing you Individually, each of these breakdowns may seem manageable. Together, they can create systemic drag on performance. That shows up in four critical ways: Missed growth opportunities: Good borrowers are declined, abandoned, or never targeted in the first place. Credit offers fail to align with actual borrower potential. Higher operational costs: Manual reviews and disconnected workflows consume time and resources that could be spent on higher-value work. Increased fraud exposure and friction: Fraud is proliferating and becoming more expensive to manage. The Federal Trade Commission reported $12.5B were lost to fraud in the U.S. in 2024, a 25% increase over the prior year. For many financial institutions, the first reaction is often to add more steps to the decisioning process, which can impact good borrowers. Increased competitive pressure: Fintechs and modern lenders are focused on delivering faster, more personalized experiences, capturing share while traditional processes lag behind. 80% of banks and credit unions plan to increase their technology spending in 2026, yet many continue to fall short on planned system deployments, according to Cornerstone Advisors’ annual “What’s Going On in Banking” research report. What innovative decisioning leaders are doing differently Leading lenders are changing how decisions are made, creating a competitive advantage. Instead of stitching together point solutions, they’re adopting a more integrated approach that brings together: Comprehensive data – including both credit and fraud insights Optimized decision strategies – designed to balance growth and risk Real-time execution – enabling faster, more consistent outcomes Continuous optimization – adapting to changing market conditions Strategic partnerships – leveraging third-party industry expertise to augment their own This shift eliminates the need for tradeoffs and instead allows lenders to increase approvals while maintaining control, reducing manual effort while improving consistency, and responding faster without sacrificing confidence. The stakes are high and the competition for consumers is even higher, particularly against a backdrop of ever-evolving fraud risks, continuously increasing consumer expectations for seamless, digital-first experiences and often limited resources. Nearly half of banks and 59% of credit unions have already deployed generative AI, with more investing now, according to the Cornerstone Advisors’ report. Closing the innovation gap requires a more fundamental shift toward decisioning systems that are connected, scalable, and built for continuous change. A new foundation for decisioning This is where platforms like Experian Decisioning are changing the landscape. By bringing together credit and fraud insights, decision strategies, and a flexible technology architecture, lenders can move beyond fragmented processes and build a more unified, intelligent decisioning approach. One that fits within existing systems but also evolves with your needs. Where to start Impactful change doesn’t need to be an overhaul of everything at once for most organizations. The first step is understanding where your biggest gaps exist, and which decision areas are creating the most friction or missed opportunity. Once you can see where decisioning is not optimized, you can begin to redesign it in a way that’s faster and more adept for what lending has become. By making better decisions, faster, and with greater confidence, lenders can process applications more efficiently and also break away from the pack by leveraging decisioning as a strategic advantage. Learn more

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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