Tag: fraud prevention

Fraud is evolving faster than ever, driven by digitalization, real-time payments and increasingly sophisticated scams. For Warren Jones and his team at Santander Bank, staying ahead requires more than tools. It requires the right partner. The partnership with Santander Bank began nearly a decade ago, during a period of rapid change in the fraud and banking landscape. Since then, the relationship has grown into a long-term collaboration focused on continuous improvement and innovation. Experian products helped Santander address one of its most pressing operational challenges: a high-volume manual review queue for new account applications. While the vast majority of alerts in the queue were fraudulent and ultimately declined, a small percentage represented legitimate customers whose account openings were delayed. This created inefficiencies for staff and a poor first impression of genuine applicants. We worked alongside Santander to tackle this challenge head-on, transforming how applications were reviewed, how fraud was detected and how legitimate customers were approved. In addition to fraud prevention, implementing Experian's Ascend PlatformTM, with its intuitive user experience and robust data environment, has unlocked additional value across the organization. The platform supports multiple use cases, enabling collaboration between fraud and marketing teams to align strategies based on actionable insights. Learn more about our Ascend Platform

For lenders, the job has never been more complex. You’re expected to protect portfolio performance, meet regulatory expectations, and support growth, all while fraud tactics evolve faster than many traditional risk frameworks were designed to handle. One of the biggest challenges of the job? The line between credit loss and fraud loss is increasingly blurred, and misclassified losses can quietly distort portfolio performance. First-party fraud can look like standard credit risk on the surface and synthetic identity fraud can be difficult to identify, allowing both to quietly slip through decisioning models and distort portfolio performance. That’s where fraud risk scores come into play. Used correctly, they don’t replace credit models; they strengthen them. And for credit risk teams under pressure to approve more genuine customers without absorbing unnecessary losses, understanding how fraud risk scores fit into modern decisioning has become essential. What is a fraud risk score (and what isn’t it) At its core, a fraud risk score is designed to assess the likelihood that an applicant or account is associated with fraudulent behavior, not simply whether they can repay credit. That distinction matters. Traditional credit scores evaluate ability to repay based on historical financial behavior. Fraud risk scores focus on intent and risk signals, patterns that suggest an individual may never intend to repay, may be manipulating identity data, or may be building toward coordinated abuse. Fraud risk scores are not: A replacement for credit scoring A blunt tool designed to decline more applicants A one-time checkpoint limited to account opening Instead, they provide an additional lens that helps credit risk teams separate true credit risk from fraud that merely looks like credit loss. How fraud scores augment decisioning Credit models were never built to detect fraud masquerading as legitimate borrowing behavior. Consider common fraud scenarios facing lenders today: First-payment default, where an applicant appears creditworthy but never intends to make an initial payment Bust-out fraud, where an individual builds a strong credit profile over time, then rapidly maxes out available credit before disappearing Synthetic identity fraud, where criminals blend real and fabricated data to create identities that mature slowly and evade traditional checks In all three cases, the applicant may meet credit criteria at the point of decision. Losses can get classified as charge-offs rather than fraud, masking the real source of portfolio degradation. When credit risk teams rely solely on traditional models, the result is often an overly conservative response: tighter credit standards, fewer approvals, and missed growth opportunities. How fraud risk scores complement traditional credit decisioning Fraud risk scores work best when they augment credit decisioning. For credit risk officers, the value lies in precision. Fraud risk scores help identify applicants or accounts where behavior, velocity or identity signals indicate elevated fraud risk — even when credit attributes appear acceptable. When integrated into decisioning strategies, fraud risk scores can: Improve confidence in approvals by isolating high-risk intent early Enable adverse-actionable decisions for first-party fraud, supporting compliance requirements Reduce misclassified credit losses by clearly identifying fraud-driven outcomes Support differentiated treatment strategies rather than blanket declines The goal isn’t to approve fewer customers. It’s to approve the right customers and to decline or treat risk where intent doesn’t align with genuine borrowing behavior. Fraud risk across the credit lifecycle One of the most important shifts for credit risk teams is recognizing that fraud risk is not static. Fraud risk scores can deliver value at multiple stages of the credit lifecycle: Marketing and prescreen: Fraud risk insights help suppress high-risk identities before offers are extended, ensuring marketing dollars are maximized by targeting low risk consumers. Account opening and originations: Real-time fraud risk scoring supports early detection of first-party fraud, synthetic identities, and identity misuse — before losses are booked. Prequalification and instant decisioning: Fraud risk scores can be used to exclude high-risk applicants from offers while maintaining speed and customer experience. Account management and portfolio review: Fraud risk doesn’t end after onboarding. Scores applied in batch or review processes help identify accounts trending toward bust-out behavior or coordinated abuse, informing credit line management and treatment strategies. This lifecycle approach reflects a broader shift: fraud prevention is no longer confined to front-end controls — it’s a continuous risk discipline. What credit risk officers should look for in a fraud risk score Not all fraud risk scores are created equal. When evaluating or deploying them, credit risk officers should prioritize: Lifecycle availability, so fraud risk can be assessed beyond originations Clear distinction between intent and ability to repay, especially for first-party fraud Adverse-action readiness, including explainability and reason codes Regulatory alignment, supporting fair lending and compliance requirements Seamless integration alongside existing credit and decisioning frameworks Increasingly, credit risk teams also value platforms that reduce operational complexity by enabling fraud and credit risk assessment through unified workflows rather than fragmented point solutions. A more strategic approach to fraud and credit risk The most effective credit risk strategies today are not more conservative, they’re more precise. Fraud risk scores give credit risk officers the ability to stop fraud earlier, classify losses accurately and protect portfolio performance without tightening credit across the board. When fraud and credit insights work together, teams can gain a clearer view of risk, stronger decision confidence and more flexibility to support growth. As fraud tactics continue to evolve, the organizations that succeed will be those that can effectively separate fraud from credit loss. Fraud risk scores are no longer a nice-to-have. They’re a foundational tool for modern credit risk strategies. How credit risk teams can operationalize fraud risk scores For credit risk officers, the challenge isn’t just understanding fraud risk, it’s operationalizing it across the credit lifecycle without adding friction, complexity or compliance risk. Rather than treating fraud as a point-in-time decision, credit risk teams should assess fraud risk where it matters most, from acquisition through portfolio management. Fraud risk scores are designed to complement credit decisioning by focusing on intent to repay, helping teams distinguish fraud-driven behavior from traditional credit risk. Key ways Experian supports credit risk teams include: Lifecycle coverage: Experian award-winning fraud risk scores are available across marketing, originations, prequalification, instant decisioning and ongoing account review. This allows organizations to apply consistent fraud strategies beyond account opening. First-party and synthetic identity fraud intelligence: Experian’s fraud risk scoring addresses first-payment default, bust-out behavior and synthetic identity fraud, which are scenarios that often bypass traditional credit models because they initially appear creditworthy. Converged fraud and credit decisioning: By delivering fraud and credit insights together, often through a single integration, Experian can help reduce operational complexity. Credit risk teams can assess fraud and credit risk simultaneously rather than managing disconnected tools and workflows. Precision over conservatism: The emphasis is not on declining more applicants, but on approving more genuine customers by isolating high-risk intent earlier. This precision helps protect portfolio performance without sacrificing growth. For lenders navigating increasing fraud pressure, Experian’s approach reflects a broader shift in the industry: fraud prevention and credit risk management are no longer separate disciplines; they are most effective when aligned. Explore our fraud solutions Contact us

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.

Financial services leaders are dealing with numerous pressures at the same time. These growing challenges for financial services organizations include sophisticated fraud, rapid Artificial Intelligence (AI) adoption without clear regulatory direction, rising customer expectations and the need for compliant, sustainable growth. Businesses are rethinking how they manage risk, growth and customer trust. These financial industry challenges are no longer confined to internal risk teams. They directly impact long-term customer loyalty. How organizations navigate these challenges will determine how effectively they deliver value to their customers. We’ve outlined the six challenges for financial services oranizations that consistently rank highest among industry leaders today. Challenge 1: Fraud is becoming harder to detect and eroding customer trust 72% of business leaders expect AI-generated fraud and deepfakes to be major challenges by 20261 As fraud tactics evolve quickly, driven in part by AI, customers are being targeted through identity-based attacks from account takeovers to synthetic identities and misuse of personal information. When these threats go undetected, or when legitimate activity is incorrectly flagged, the result isn’t just financial loss. It’s a breakdown of trust. Organizations that want to stay ahead must move beyond isolated fraud controls. By embedding identity management and monitoring into the customer experience, organizations can move from reactive fraud response to proactive identity protection. Identity theft protection and monitoring help organizations turn fraud prevention into a visible, trust-building experience for customers — offering early alerts, guidance, and peace of mind when identity risks arise. Challenge 2: AI decisions must be trusted by customers, not just regulators 76% of businesses say implementing responsible AI is one of their biggest challenges2 As AI becomes more embedded in financial services, it shapes the experiences customers see every day. From credit decisions to eligibility outcomes and personalized offers. While AI can drive faster and more inclusive decisions, it also introduces a new expectation: customers want to understand why a decision was made. Responsible AI is no longer just about regulatory compliance. It’s about delivering outcomes that feel fair, consistent and easy to understand. When decisions appear unclear, confidence erodes. When organizations can clearly explain outcomes, not just internally, they build confidence across regulators, partners and customers. This allows AI to scale responsibly while reinforcing trust in every interaction. Financial wellness tools such as credit scores, reports and education help make AI-driven decisions more transparent, giving customers clarity into outcomes and confidence in how their financial health is assessed. Challenge 3: Digital experiences are failing to deliver clarity and confidence 57% of U.S. consumers remain concerned about conducting activities online3 Customer confidence is affected by day-to-day interactions such as onboarding, payments and issue resolution. Inconsistent decisions, unclear outcomes and friction in digital journeys can quickly erode confidence and increase confusion, disengagement and abandonment. Financial services leaders will need to rebuild and strengthen confidence. Improving key decision points with better data and analytics helps ensure customers receive timely insights, understandable outcomes and meaningful guidance, turning everyday interactions into opportunities to build stronger relationships. By delivering ongoing financial wellness insights and education, organizations can replace confusion with clarity — helping consumers better understand their financial standing and stay engaged over time. Challenge 4: Gen Z continues to raise the bar It's no secret that Gen Z stands out for its strong preference for digital financial services and digital interactions, but Gen Z is also pushing the envelope on financial wellness. 48% of Gen Z report that they do not feel financially secure, indicating strong demand for financial support and tools4 Their expectations for instant decisions, seamless digital experiences, transparency and tools that help them manage their financial lives are quickly becoming the baseline. To meet and exceed these expectations, financial institutions will need to support real-time, data-driven decisioning that adapt to individual needs. Delivering modern, app-like financial experiences, without compromising risk management. Increasingly, organizations are meeting Gen Z expectations by offering financial wellness and protection tools through employee benefits, supporting everyday financial confidence beyond traditional compensation. Challenge 5: Limited data limits meaningful consumer engagement 62 million U.S. consumers are thin-file or credit invisible under traditional credit scoring.5 Growth will always be a priority, but it must be responsible and inclusive. Traditional credit data alone often provides an incomplete picture of consumer financial behavior, limiting visibility and making it harder to confidently expand access. By incorporating alternative and expanded data, organizations can gain a more holistic view of consumers. This broader perspective supports smarter decisions, personalized insights and more inclusive engagement, which enables growth while maintaining compliance and managing risk responsibly. Expanded data supports more personalized financial wellness experiences, enabling organizations to provide relevant insights, responsible access and guidance tailored to individual consumer needs. Challenge 6: Disconnected decisions create inconsistent customer experiences Increasingly, fintech leaders are moving toward unified risk and decisioning strategies to deliver more personalized experiences6 While customers interact with a single institution, decisions are often made across disconnected data sources, systems and teams. These silos create inconsistent experiences, slow responses and operational complexities that customers feel directly through conflicting messages and uneven outcomes. Experian helps organizations break down these silos by unifying data, analytics and decisioning across the enterprise. When data incidents occur, integrated experiences enable faster data breach resolution, helping consumers understand what happened, take action, and recover with confidence. Looking ahead These challenges for financial services organizations are not emerging; they’re already here and reshaping how financial institutions engage with consumers. Leaders who proactively address financial industry challenges by connecting data, analytics, and responsible AI are better positioned to deliver trusted, transparent and meaningful experiences. Learn More References:1. https://www.experian.com/blogs/insights/2025-identity-fraud-report2. https://www.techradar.com/pro/businesses-are-struggling-to-implement-responsible-ai-but-it-could-make-all-the-difference3. https://www.experian.com/blogs/insights/2025-identity-fraud-report4. https://www.deloitte.com/global/en/issues/work/genz-millennial-survey.html5. https://www.experian.com/thought-leadership/business/the-roi-of-alternative-data6. https://us-go.experian.com/2025-state-of-fintech-report?cmpid=IM-2025-state-of-fintech-report-livesocial-share

In today’s fast-evolving digital landscape, fraud prevention is no longer a reactive function, it’s a strategic imperative. As financial institutions, fintechs and government agencies face increasingly sophisticated threats, the need for scalable, transparent and AI-powered solutions has never been greater. Experian stands at the forefront of this transformation, delivering proven technology, unmatched data intelligence and regulatory-ready innovation that empowers organizations to stay ahead of fraud. One platform. Every fraud challenge. Experian’s fraud prevention ecosystem delivers scale, speed and sophistication. Unlike fragmented solutions that require patchwork integrations, Experian offers a unified platform that spans the entire fraud lifecycle from identity verification to transaction monitoring and case management.  With the exciting acquisition of NeuroID, Experian is delivering more value than ever before with our shared commitment to staying ahead of emerging fraud threats.   Embedding NeuroID’s behavioral expertise into Experian’s data systems and platforms is transformative. Together, we’re redefining what fraud prevention can look like in a real-time, AI-driven world. – Kathleen Peters, Chief Innovation Officer, Experian With tools like NeuroID, FraudNet and Precise ID, Experian delivers real-time decisioning and orchestration across diverse use cases. These technologies are not just buzzwords, they’re battle-tested engines driving measurable impact across millions of daily decisions. Data dominance that drives accuracy Experian’s proprietary datasets and global consortia provide unparalleled access to fraud intelligence. This data advantage enables clients to detect anomalies faster, reduce false positives and optimize fraud strategies with precision.  Experian supports over five billion fraud events annually across the largest banks, fintechs and government agencies. That’s 10x more fraud and identity use cases than most competitors can manage across industries and institutions of all sizes. AI innovation with guardrails While many vendors are just beginning to explore AI, Experian has spent the last two decades embedding it into its core products and services. The launch of the Experian Assistant for Model Risk Management exemplifies this commitment. Integrated into the Ascend Platform and powered by ValidMind technology, this AI assistant streamlines model governance, enhances auditability, and accelerates deployment, all while remaining compliant with evolving regulations. Experian’s AI is not a black box. It’s explainable, auditable and developed with governance in mind. This transparency gives clients the confidence to innovate without compromising compliance.  Compliance is built in, not bolted on Experian’s solutions are designed with compliance at the core. From FCRA and GLBA to KYC and CIP, Experian has a long-standing track record of aligning with regulatory frameworks. The company’s ability to demystify machine learning and make it transparent and explainable sets it apart in an industry where trust is paramount. As AI adoption accelerates, Experian’s governance models ensure that innovation doesn’t outpace accountability. Clients benefit from automated documentation, synthetic data generation and model transparency which are all essential for navigating today’s complex regulatory landscape. Empowering clients to own their outcomes Experian doesn’t just deliver tools, it empowers users. With self-service model building, clients can customize fraud strategies, optimize performance, and respond to threats in real time. This flexibility ensures that organizations aren’t just reacting to fraud, they’re proactively shaping their defenses.  Experian’s fraud prevention solutions are designed to be intuitive, scalable, and user-centric, enabling teams to make smarter decisions faster. A global brand you can trust Trust is earned, not claimed. Experian’s decades-long commitment to data stewardship, innovation and client success has made it a globally recognized authority in fraud prevention. With thousands of enterprise clients and strategic partnerships, Experian delivers unmatched reliability and scale. From supporting the largest financial institutions to enabling fintech startups, Experian’s infrastructure is built to manage complexity with confidence. Thought leadership that moves the industry  Experian continues to lead the conversation on fraud prevention and identity verification. As a sponsor of the 2025 Federal Identity Forum & Expo, Experian showcased its latest innovations in behavioral analytics and fraud detection, helping government agencies stay ahead of evolving threats.   The company’s U.S. Identity & Fraud Report, now in its tenth year, provides actionable insights into shifting fraud patterns and consumer behavior reinforcing Experian’s role as a trusted thought leader. In a market flooded with noise, Experian delivers clarity. Its unified fraud prevention platform, backed by decades of AI innovation and regulatory expertise, empowers organizations to protect their customers, optimize operations, and lead with confidence. Experian isn’t just keeping up with the future of fraud prevention, it’s defining it. Learn more

E-commerce is booming. Global online sales continue to rise with forecasts predicting growth to $7.89 trillion by 2028. Unfortunately, with any lucrative market comes fraudulent activity. As e-commerce grows by leaps and bounds, so do fraud incidents. E-commerce fraud is defined as any illegal or deceptive activity conducted during an online transaction with the intent to steal money, goods or sensitive information. As digital shopping flourishes, the tactics criminals use to exploit vulnerabilities in payment systems, customer accounts and merchant operations is rapidly expanding. According to Experian’s tenth annual Identity & Fraud Report, nearly 60% of U.S. businesses reported higher fraud losses in 2025, driven by more sophisticated attacks and legacy security gaps. The same report highlighted the damage from e-commerce fraud goes beyond the loss of revenue, directly impacting consumer trust. The survey found that only 13% of consumers feel fully secure opening new accounts. Chief amongst their concerns, 68% of consumer worry about identity theft, while 61% are fearful of stolen credit card data. The constant threat of e-commerce fraud has placed tremendous pressure on merchants and retailers to take robust steps in mitigating these attacks. In addition to protecting the bottom line, such measures are essential to earning consumer trust. According to Experian’s merchant-focused edition of our Identity & Fraud Report, consumers consistently perceive physical and behavioral biometrics tools as the most secure authentication methods — yet merchants are slow to adopt them. This gap highlights a key opportunity for businesses to strengthen security practices and build trust without adding friction to the user experience. After all, 74% of consumers say security is the most important factor when deciding to engage with a business.3 E-commerce fraud comes in many shapes and sizes E-commerce fraud is an umbrella term for a variety of attacks that target merchants and retailers. Amongst these is chargeback fraud, which occurs when a customer makes a legitimate purchase and then falsely disputes the charge with their credit card issuer, claiming the item never arrived or the transaction was unauthorized. The merchant loses both the product and the payment. Another is account takeover fraud, which happens when cybercriminals gain access to a customer’s online account, often through stolen login credentials, and use it to make unauthorized purchases, change shipping details or withdraw loyalty points. In card-not-present (CNP) fraud, attackers use stolen credit card information to make purchases online or by phone, where the physical card isn’t required. Because identity verification is limited, merchants bear the financial losses. This type of fraud includes BIN attacks, targeting the Bank Identification Number (BIN) on a credit or debit card that identifies the issuing financial institution. The goal of a BIN attack is to discover valid card numbers that can be used for fraudulent transactions. There are also refund fraud attacks, which involve scammers exploiting return or refund policies — such as claiming an item didn’t arrive or sending back a different or counterfeit product for reimbursement. Together, different forms of e-commerce fraud cost businesses billions annually, demanding strong fraud detection, authentication and monitoring systems to combat them. E-commerce fraud prevention should be a priority for every merchant and retailer. E-commerce fraud prevention: Ways merchants can fight back Merchants report the highest rates of new account fraud, yet it ranks just 15th among their active investments for 2025. While fraudsters continue to find new and innovative ways to attack, merchants and retailers can better prepare by following industry best practices in e-commerce fraud prevention: Chargeback fraud: When it comes to preventing and managing chargeback fraud, merchants should ensure customers are fully aware of return and refund policies. Utilize Address Verification Services (AVS) and Card Verification Value (CVV2) verification for online and over-the-phone transactions to establish the validity of a purchase. Keeping meticulous records of all transactions can serve as compelling evidence to defend the transaction. Leverage advanced fraud detection tools, such as tokenization and machine learning and AI fraud detection solutions that flag potentially fraudulent transactions and detect suspicious spending patterns and anomalies. Account takeover fraud: Merchants can minimize the risk of account takeover fraud using holistic, risk-based identity and device authentication, as well as behavioral analytics or targeted, knowledge-based authentication. End-to-end fraud management solutions can help reduce manual processes and remove the risk of information silos. Card-not-present fraud: Mitigating the risk of CNP fraud can be accomplished by implementing additional security measures at the time of transaction. These can include requiring verification information, such as a CVV code or a billing zip code to further authenticate the card holder’s identity. Advanced e-commerce fraud prevention tools To stay ahead of the fraudsters, merchants and retailers should take a multilayered approach to e-commerce fraud prevention that takes advantage of the latest, most advanced tools.  At Experian®, we offer innovative fraud management solutions that provide the right level of security without causing customer friction. Three advanced e-commerce fraud prevention tools that every merchant should have in their arsenal include: Experian LinkTM: This tool enhances credit card authentication by linking the payment instrument with the digital identity presented for payment. Experian Link enables merchants to quickly and accurately identify legitimate customers to reduce friction and increase acceptance rates, reduce operation costs by preventing fraudulent credit card use, make better risk decisions to protect legitimate customers, limit false declines and identify potential fraudsters. Behavioral analytics: With the growth of AI, fraudsters can now replicate static data, but mimicking human behavior remains challenging. Behavioral analytics detects subtle interaction patterns that are extremely difficult for GenAI-driven fraudsters, including fraud rings and next-generation fraud bots, to replicate. Powered by NeuroID, our behavioral analytics capabilities help organizations proactively mitigate fraud, reduce false positives and streamline risk detection, ultimately creating a secure and frictionless experience for trustworthy users — while locking out fraudsters earlier. Precise ID®: This advanced tool enables businesses to pursue growth confidently by providing robust, real-time identity verification, as well as the ability to accurately identify a wide range of fraud risks including identity theft, synthetic identity and first-party fraud, along with tools that facilitate confirmation when risks are detected. The threat of fraud never stops   Merchants and retailers are under a constant and unrelenting threat of attacks by fraudsters. Vigilance is required to protect the customer experience and the bottom line. Fortunately, innovative tools are leveling the playing field, offering much needed e-commerce fraud protection. To learn how Experian can help you combat fraud and meet consumers’ demands for trust and privacy, explore our best-in-class fraud management solutions and download our latest report on closing the trust gap in e-commerce. Explore our solutions Download report

Income and employment verification fraud is surging in the tenant screening industry, putting traditional verification methods under intense pressure. As economic uncertainty grows and document forgery becomes more sophisticated, it's clear that legacy processes are no longer sufficient. Recent findings highlight the urgency for change. According to the NMHC Pulse Survey, 93.3% of property managers reported encountering fraud in the past year, with 84.3% citing falsified paystubs and fake employment references as the most common tactics. As AI-generated forgeries become increasingly convincing and accessible, relying solely on manual review is proving inadequate. A Shift in Strategy: Toward Smarter Income and Employment Verification Historically, tenant screeners have relied on methods such as manual document review, direct employer contact, payroll APIs, and verification of assets (VOA). While these remain important, they are no longer capable of keeping pace with today’s verification challenges. In response, many screening companies are exploring new income verification tools that offer improved fraud prevention, lower operational costs, and faster turnaround. These innovations include layered approaches that combine observed data, permissioned uploads, and automated fraud detection technologies. Introducing Experian Observed DataTM in Tenant Screening One emerging solution in the fight against rental application fraud is the use of observed data during tenant screening. This method uses collectively sourced insights to assess whether an applicant’s income and employment claims are likely to be accurate. Experian Observed Data is takes inputs from many sources including creditors, property managers and others. This type of data starts out as consumer stated data but is substantiated by third party creditors who have originated lending products and report on the performance of these products.   Although this method is not FCRA-compliant and cannot be used to approve or deny applications, it is highly effective as an early step in the screening process. Some sources of Experian Observed Data include a confidence score that can help screeners assess how closely an applicant’s stated information aligns with observed trends and can help screening companies to better assess their prioritization queue to determine if more data points are needed. Why Experian Observed Data Matters To combat fraud without driving up costs or slowing down the tenant screening process, screening companies need reliable, efficient tools. Experian Observed Data supports this need by offering a faster, more scalable approach to assessing risk. Key benefits include: Early detection of discrepancies in reported income or employment The ability to prioritize high-risk applications for further review A more cost-effective alternative before committing to premium verification services For instance, if an applicant has a strong credit report and clean background check, and Experian Observed Data supports their stated income, further verification may be unnecessary. If inconsistencies are flagged, screening companies can escalate to tools such as AI document analysis or direct outreach. Fraud Prevention Through Smarter Workflows The use of Experian Observed Data also aligns with a broader shift toward AI document fraud detection and layered verification strategies. Instead of applying the same tools to every application, screening companies can now implement decision trees that use lower-cost tools first, escalating only when risk or uncertainty increases. This adaptive approach is particularly relevant as screener companies strive to improve accuracy and efficiency at scale. By deploying Experian Observed Data as a first step, tenant screening professionals can better allocate resources while remaining vigilant against fraud Future Proofing Verificaiton As the income and employment verification landscape evolves, screening companies must move beyond legacy methods and adopt tools that are responsive to today’s challenges. Experian Observed Data provides a scalable, low friction starting point that supports smarter decision-making and better fraud detection. Coming to our next blog: We will explore how manual research verifications and AI-powered document upload solutions enhance the effectiveness of modern income verification tools, creating a more resilient and adaptable tenant screening process.  

In today’s digital lending landscape, fraudsters are more sophisticated, coordinated, and relentless than ever. For companies like Terrace Finance — a specialty finance platform connecting over 5,000 merchants, consumers, and lenders — effectively staying ahead of these threats is a major competitive advantage. That is why Terrace Finance partnered with NeuroID, a part of Experian, to bring behavioral analytics into their fraud prevention strategy. It has given Terrace’s team a proactive, real-time defense that is transforming how they detect and respond to attacks — potentially stopping fraud before it ever reaches their lending partners. The challenge: Sophisticated fraud in a high-stakes ecosystem Terrace Finance operates in a complex environment, offering financing across a wide range of industries and credit profiles. With applications flowing in from countless channels, the risk of fraud is ever-present. A single fraudulent transaction can damage lender relationships or even cut off financing access for entire merchant groups. According to CEO Andy Hopkins, protecting its partners is a top priority for Terrace:“We know that each individual fraud attack can be very costly for merchants, and some merchants will get shut off from their lending partners because fraud was let through ... It is necessary in this business to keep fraud at a tolerable level, with the ultimate goal to eliminate it entirely.” Prior to NeuroID, Terrace was confident in its ability to validate submitted data. But with concerns about GenAI-powered fraud growing, including the threat of next-generation fraud bots, Terrace sought out a solution that could provide visibility into how data was being entered and detect risk before applications are submitted. The solution: Behavioral analytics from NeuroID via Experian After integrating NeuroID through Experian’s orchestration platform, Terrace gained access to real-time behavioral signals that detected fraud before data was even submitted. Just hours after Terrace turned NeuroID on, behavioral signals revealed a major attack in progress — NeuroID enabled Terrace to respond faster than ever and reduce risk immediately. “Going live was my most nerve-wracking day. We knew we would see data that we have never seen before and sure enough, we were right in the middle of an attack,” Hopkins said. “We thought the fraud was a little more generic and a little more spread out. What we found was much more coordinated activities, but this also meant we could bring more surgical solutions to the problem instead of broad strokes.” Terrace has seen significant results with NeuroID in place, including: Together, NeuroID and Experian enabled Terrace to build a layered, intelligent fraud defense that adapts in real time. A partnership built on innovation Terrace Finance’s success is a testament to what is  possible when forward-thinking companies partner with innovative technology providers. With Experian’s fraud analytics and NeuroID’s behavioral intelligence, they have built a fraud prevention strategy that is proactive, precise, and scalable. And they are not stopping there. Terrace is now working with Experian to explore additional tools and insights across the ecosystem, continuing to refine their fraud defenses and deliver the best possible experience for genuine users. “We use the analogy of a stream,” Hopkins explained. “Rocks block the flow, and as you remove them, it flows better. But that means smaller rocks are now exposed. We can repeat these improvements until the water flows smoothly.” Learn more about Terrace Finance and NeuroID Want more of the story? Read the full case study to explore how behavioral analytics provided immediate and long-term value to Terrace Finance’s innovative fraud prevention strategy. Read case study

In today’s digital payments landscape, fraudsters are constantly developing new tactics to exploit vulnerabilities. One of the most common credit card schemes financial institutions and merchants face are BIN attacks. But what exactly is a BIN attack, and how does BIN attack fraud work? What is a BIN attack? BIN attacks, a type of card not present fraud, target the Bank Identification Number (BIN) ­— the first six to eight digits of a credit or debit card number that identify the issuing financial institution. Fraudsters use these digits to systematically generate and test potential card number combinations. The goal of a BIN attack is to discover valid card numbers that can be used for fraudulent transactions. Because BINs are publicly available and consistent across card issuers, they provide a predictable framework for attackers. How does it differ from other types of payment fraud? Payment fraud takes many forms, but BIN attacks stand apart because of their scale and automation. Card testing fraud vs. BIN attacks: Both involve criminals running authorization attempts to identify valid card details. However, card testing typically uses data from a single stolen card, while BIN attacks systematically generate thousands of possible card numbers from a known BIN range. Account takeover fraud vs. BIN attacks: In an account takeover, fraudsters gain access to a customer’s existing account, often through phishing or stolen login credentials. BIN attacks don’t require account access — instead, they exploit card number patterns to guess valid accounts. What are the consequences of a BIN attack? BIN attacks don’t just result in stolen card numbers — they create wide-ranging business risks that can impact operations, revenue and customer trust. For financial institutions and merchants, the ripple effects can be significant: High transaction volumes: BIN attacks are carried out using automated scripts or bots that fire off thousands of transaction attempts per minute. This traffic can overwhelm payment systems, slow down processing and disrupt the checkout experience for legitimate customers. Increased chargebacks: Once fraudsters identify valid cards, they make unauthorized purchases that often result in chargebacks. Both merchants and issuers absorb these losses — merchants lose revenue, while issuers reimburse cardholders. Network and processing costs: Every transaction attempt — even those declined during a BIN attack — still incurs network and processing fees. Merchants and issuers can end up paying for thousands of authorization requests, draining resources. Reputational damage: Today’s consumers expect seamless and secure payments. If they experience frequent declines, blocked cards or fraudulent activity, their trust in the institution or merchant erodes. How to protect against BIN attack fraud Mitigating BIN attacks requires a proactive, layered defense strategy. Financial institutions and merchants should consider: Advanced fraud detection and analytics: BIN attacks generate massive volumes of fraudulent traffic. By leveraging AI-driven analytics and machine learning, institutions and merchants can monitor for unusual transaction patterns, velocity spikes and bot-driven activity. Identity and device intelligence: Fraudsters often hide behind bots, stolen IP addresses and compromised devices. With identity verification and device intelligence solutions, merchants and institutions can better determine whether a transaction is coming from a legitimate customer or a fraudster testing card details. Multi-factor authentication (MFA): BIN attacks succeed on speed and automation, firing off thousands of transactions. MFA can help disrupt this process by requiring additional proof of identity from the customer, such as facial recognition or one-time passcodes. Credit card authentication: BIN attacks exploit the gap between payment credentials and the identity of the person using them. A solution like Experian LinkTM seamlessly connects the payment instrument with the digital identity presented for payment, helping merchants to reduce false declines, fraud and operating expenses. Build a stronger defense against BIN attacks BIN attacks are a growing threat in today’s digital payments ecosystem. But with the right safeguards in place, organizations can stay ahead. Learn how Experian can help you strengthen your fraud defenses to reduce losses and protect customer trust. Learn more

Lending fraud – what is it? Lending fraud is a deceptive practice in which individuals or entities intentionally provide false or misleading information during the loan application process to secure credit or financial gain. This can include using fake identities, inflating income, forging documentation, or applying for loans without the intention of repayment.   The consequences are significant: lenders suffer financial losses, consumers experience identity theft or damaged credit scores, and the economic system bears increased risk and regulatory scrutiny. Loan fraud is a growing concern across consumer, commercial, and mortgage lending sectors, affecting institutions of all sizes. How do I safeguard my organization from loan fraud?    Preventing lending fraud is a complex, ongoing challenge that requires a multi-layered and holistic approach. As fraud tactics become more sophisticated, especially with the rise of generative AI and digital lending channels, financial institutions must continually evolve their defenses.  Strong identity verification is the first line of defense. Lenders should implement advanced authentication tools beyond basic KYC (Know Your Customer) checks. This includes biometric verification, document verification, and device intelligence —technologies that assess the authenticity of the user and the device used during the application process. These tools can help detect synthetic identities — false identities created using a blend of real and fabricated information — increasingly used in loan fraud schemes.  Another crucial strategy is real-time data analytics and behavioral monitoring. Lenders can quickly identify anomalies that may indicate fraudulent activity by analyzing applicant behavior, credit history, device usage patterns, and geolocation data in real time. For example, if an applicant submits multiple loan applications from different IP addresses in a short time frame, that could raise a red flag for potential lending fraud.  Employee training and awareness are also essential. Frontline staff must be equipped to identify warning signs, such as inconsistencies in application documents or rushed, high-pressure loan requests. Regular fraud prevention training helps employees stay alert and aligned with the organization’s risk management protocols.  57% of financial institutions reported direct fraud losses exceeding $500,000 in the past year, with 25% exceeding $1 million.1 Consumers reported losing more than $12.5 billion to fraud in 2024, which represents a 25% increase over the prior year.2 In addition, robust internal controls and auditing mechanisms are critical in prevention. Organizations should regularly audit loan origination processes and investigate unusual approval patterns to detect insider fraud or systemic vulnerabilities.  Finally, consumer education is a vital, often overlooked, aspect of combating loan fraud. Lenders should provide resources to help customers understand the risks of identity theft, encourage them to monitor their credit reports regularly, and empower them to report any suspicious activity. A well-informed customer base can be a valuable early warning system for fraud.  With digital lending becoming the norm, preventing lending fraud means staying ahead of increasingly tech-savvy fraudsters. Leveraging data, technology, and education together builds a stronger, more resilient fraud defense framework.  Lending fraud + Experian – How we can help  With access to the industry’s most advanced fraud detection and identity verification tools, partnering with us gives you a potent edge in combating lending fraud. As a global leader in data, analytics, and technology, our comprehensive and accurate sets of consumer information enable you to spot risks that might be invisible through conventional means. Our approach combines rich data insights with powerful machine learning algorithms, delivering fraud prevention tools that are intelligent, scalable, and highly adaptive.  Our fraud detection technologies are designed to protect every stage of the lending lifecycle. From real-time identity verification and multi-factor authentication solutions to behavioral biometrics and device intelligence, so you can detect synthetic identities, manipulated applications, and other forms of loan fraud before they lead to financial loss.  In an era where trust is currency, partnering with us doesn’t just help protect against lending fraud — it enhances your reputation as a secure, responsible lender. You gain the confidence of your customers by providing safe, streamlined lending experiences while meeting compliance requirements and reducing operational risk. With us, you’re not just reacting to fraud—you’re anticipating it, preventing it, and confidently growing your business.  Learn more 1State of Fraud Benchmark Report. Alloy. (2024). 2New FTC Data Show a Big Jump in Reported Losses to Fraud to $12.5 Billion in 2024. Federal Trade Commission. (2025, March 10). 

Experian is proud to be a Thought Leadership Sponsor at this year’s Federal Identity Forum & Expo (FedID)! We’re bringing the latest innovations in fraud prevention, identity verification, and behavioral analytics – all designed to help government agencies protect access, ensure trust, and stay ahead of evolving threats.

Now in its tenth year, Experian’s U.S. Identity and Fraud Report continues to uncover the shifting tides of fraud threats and how consumers and businesses are adapting. Our latest edition sheds light on a decade of change and unveils what remains consistent: trust is still the cornerstone of digital interactions. This year’s report draws on insights from over 2,000 U.S. consumers and 200 businesses to explore how identity, fraud and trust are evolving in a world increasingly shaped by generative artificial intelligence (GenAI) and other emerging technologies. Highlights: Over a third of companies are using AI, including generative AI, to combat fraud. 72% of business leaders anticipate AI-generated fraud and deepfakes as major challenges by 2026. Nearly 60% of companies report rising fraud losses, with identity theft and payment fraud as top concerns. Digital anxiety persists with 57% of consumers worried about doing things online. Ready to go deeper? Explore the full findings and discover how your organization can lead with confidence in an evolving fraud landscape. Download report Watch on-demand webinar Read press release  

Powered by GenAI and increasingly accessible fraud tools, fraud threats are evolving faster than ever. Traditional fraud detection solutions alone are struggling to keep up with evolving fraud rings, fraud bots, and attack strategies, pushing businesses to explore smarter, more adaptive defenses. That’s why many organizations are turning to User and Entity Behavior Analytics (UEBA) as protection against growing threats, especially internal ones. But what exactly is UEBA, and how does it differ from other solutions, like behavioral analytics?

Bot fraud has long been a major concern for digital businesses, but evolving attacks at all stages in the customer lifecycle have overshadowed an ever-present issue: click fraud. Click fraud is a cross-departmental challenge for businesses, and stopping it requires a level of insight and understanding that many businesses don’t yet have. It’s left many fraud professionals asking: What is click fraud? Why is it so dangerous? How can it be prevented? What is click fraud? A form of bot fraud, click fraud occurs when bots drive fraudulent clicks to websites, digital ads, and emails. Click fraud typically exploits application flows or digital advertising; traffic from click bots appears to be genuine but is actually fraudulent, incurring excessive costs through API calls or ad clicks. These fraudulent clicks won’t result in any sales but will reveal sensitive information, inflate costs, and clutter data. What is the purpose of click fraud? It depends on the target. We've seen click bots begin (but not complete) insurance quotes or loan applications, gathering information on competitors’ rates. In other cases, fraudsters use click fraud to drive artificial clicks to ads on their sites, resulting in increased revenue from PPC/CPC advertising. The reasons behind click fraud vary widely, but, regardless of its intent, the impacts of it affect businesses deeply. The dangers of click fraud On the surface, click fraud may seem less harmful than other types of fraud. Unlike application fraud and account takeover fraud, consumers’ data isn’t being stolen, and fraud losses are relatively minuscule. But click fraud can still be detrimental to businesses' bottom lines: every API call incurred by a click bot is an additional expense, and swarms of click bots distort data that’s invaluable to fraud attack detection and customer acquisition. The impact of click fraud extends beyond that, though. Not only can click bots gather sensitive data like insurance quotes, but click fraud can also be a gateway to more insidious fraud schemes. Fraud rings are constantly looking for vulnerabilities in businesses’ systems, often using bots to probe for back-door entrances to applications and ways to bypass fraud checks. For example: if an ad directs to an unlisted landing page that provides an alternate entry to a business’s ecosystem, fraudsters can identify this through click fraud and use bots to find vulnerabilities in the alternate application process. In doing so, they lay the groundwork for larger attacks with more tangible losses. Keys to click fraud prevention Without the right tools in place, modern bots can appear indistinguishable from humans — many businesses struggle to identify increasingly sophisticated bots on their websites as a result. Allowing click fraud to remain undetected can make it extremely difficult to know when a more serious fraud attack is at your doorstep. Preventing click fraud requires real-time visibility into your site’s traffic, including accurate bot detection and analysis of bot behavior. It’s one of many uses for behavioral analytics in fraud detection: behavioral analytics identifies advanced bots pre-submit, empowering businesses to better differentiate click fraud from genuine traffic and other fraud types. With behavioral analytics, bot attacks can be detected and stopped before unnecessary costs are incurred and sensitive information is revealed. Learn more about our behavioral analytics for fraud detection.

Fake IDs have been around for decades, but today’s fraudsters aren’t just printing counterfeit driver’s licenses — they’re using artificial intelligence (AI) to create synthetic identities. These AI fake IDs bypass traditional security checks, making it harder for businesses to distinguish real customers from fraudsters. To stay ahead, organizations need to rethink their fraud prevention solutions and invest in advanced tools to stop bad actors before they gain access. The growing threat of AI Fake IDs   AI-generated IDs aren’t just a problem for bars and nightclubs; they’re a serious risk across industries. Fraudsters use AI to generate high-quality fake government-issued IDs, complete with real-looking holograms and barcodes. These fake IDs can be used to commit financial fraud, apply for loans or even launder money. Emerging services like OnlyFake are making AI-generated fake IDs accessible. For $15, users can generate realistic government-issued IDs that can bypass identity verification checks, including Know Your Customer (KYC) processes on major cryptocurrency exchanges.1 Who’s at risk? AI-driven identity fraud is a growing problem for: Financial services – Fraudsters use AI-generated IDs to open bank accounts, apply for loans and commit credit card fraud. Without strong identity verification and fraud detection, banks may unknowingly approve fraudulent applications. E-commerce and retail – Fake accounts enable fraudsters to make unauthorized purchases, exploit return policies and commit chargeback fraud. Businesses relying on outdated identity verification methods are especially vulnerable. Healthcare and insurance – Fraudsters use fake identities to access medical services, prescription drugs or insurance benefits, creating both financial and compliance risks. The rise of synthetic ID fraud Fraudsters don’t just stop at creating fake IDs — they take it a step further by combining real and fake information to create entirely new identities. This is known as synthetic ID fraud, a rapidly growing threat in the digital economy. Unlike traditional identity theft, where a criminal steals an existing person’s information, synthetic identity fraud involves fabricating an identity that has no real-world counterpart. This makes detection more difficult, as there’s no individual to report fraudulent activity. Without strong synthetic fraud detection measures in place, businesses may unknowingly approve loans, credit cards or accounts for these fake identities. The deepfake threat AI-powered fraud isn’t limited to generating fake physical IDs. Fraudsters are also using deepfake technology to impersonate real people. With advanced AI, they can create hyper-realistic photos, videos and voice recordings to bypass facial recognition and biometric verification. For businesses relying on ID document scans and video verification, this can be a serious problem. Fraudsters can: Use AI-generated faces to create entirely fake identities that appear legitimate Manipulate real customer videos to pass live identity checks Clone voices to trick call centers and voice authentication systems As deepfake technology improves, businesses need fraud prevention solutions that go beyond traditional ID verification. AI-powered synthetic fraud detection can analyze biometric inconsistencies, detect signs of image manipulation and flag suspicious behavior. How businesses can combat AI fake ID fraud Stopping AI-powered fraud requires more than just traditional ID checks. Businesses need to upgrade their fraud defenses with identity solutions that use multidimensional data, advanced analytics and machine learning to verify identities in real time. Here’s how: Leverage AI-powered fraud detection – The same AI capabilities that fraudsters use can also be used against them. Identity verification systems powered by machine learning can detect anomalies in ID documents, biometrics and user behavior. Implement robust KYC solutions – KYC protocols help businesses verify customer identities more accurately. Enhanced KYC solutions use multi-layered authentication methods to detect fraudulent applications before they’re approved. Adopt real-time fraud prevention solutions – Businesses should invest in fraud prevention solutions that analyze transaction patterns and device intelligence to flag suspicious activity. Strengthen synthetic identity fraud detection – Detecting synthetic identities requires a combination of behavioral analytics, document verification and cross-industry data matching. Advanced synthetic fraud detection tools can help businesses identify and block synthetic identities. Stay ahead of AI fraudsters AI-generated fake IDs and synthetic identities are evolving, but businesses don’t have to be caught off guard. By investing in identity solutions that leverage AI-driven fraud detection, businesses can protect themselves from costly fraud schemes while ensuring a seamless experience for legitimate customers. At Experian, we combine cutting-edge fraud prevention, KYC and authentication solutions to help businesses detect and prevent AI-generated fake ID and synthetic ID fraud before they cause damage. Our advanced analytics, machine learning models and real-time data insights provide the intelligence businesses need to outsmart fraudsters. Learn more *This article includes content created by an AI language model and is intended to provide general information. 1 https://www.404media.co/inside-the-underground-site-where-ai-neural-networks-churns-out-fake-ids-onlyfake/