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We are squarely in the holiday shopping season. From the flurry of promotional emails to the endless shopping lists, there are many to-dos and even more opportunities for financial institutions at this time of year. The holiday shopping season is not just a peak period for consumer spending; it’s also a critical time for financial institutions to strategize, innovate, and drive value. According to the National Retail Federation, U.S. holiday retail sales are projected to approach $1 trillion in 2024, , and with an ever-evolving consumer behavior landscape, financial institutions need actionable strategies to stand out, secure loyalty, and drive growth during this period of heightened spending. Download our playbook: "How to prepare for the Holiday Shopping Season" Here’s how financial institutions can capitalize on the holiday shopping season, including key insights, actionable strategies, and data-backed trends. 1. Understand the holiday shopping landscape Key stats to consider: U.S. consumers spent $210 billion online during the 2022 holiday season, according to Adobe Analytics, marking a 3.5% increase from 2021. Experian data reveals that 31% of all holiday purchases in 2022 occurred in October, highlighting the extended shopping season. Cyber Week accounted for just 8% of total holiday spending, according to Experian’s Holiday Spending Trends and Insights Report, emphasizing the importance of a broad, season-long strategy. What this means for financial institutions: Timing is crucial. Your campaigns are already underway if you get an early start, and it’s critical to sustain them through December. Focus beyond Cyber Week. Develop long-term engagement strategies to capture spending throughout the season. 2. Leverage Gen Z’s growing spending power With an estimated $360 billion in disposable income, according to Bloomberg, Gen Z is a powerful force in the holiday market​. This generation values personalized, seamless experiences and is highly active online. Strategies to capture Gen Z: Offer digital-first solutions that enhance the holiday shopping journey, such as interactive portals or AI-powered customer support. Provide loyalty incentives tailored to this demographic, like cash-back rewards or exclusive access to services. Learn more about Gen Z in our State of Gen Z Report. To learn more about all generations' projected consumer spending, read new insights from Experian here, including 45% of Gen X and 52% of Boomers expect their spending to remain consistent with last year. 3. Optimize pre-holiday strategies Portfolio Review: Assess consumer behavior trends and adjust risk models to align with changing economic conditions. Identify opportunities to engage dormant accounts or offer tailored credit lines to existing customers. Actionable tactics: Expand offerings. Position your products and services with promotional campaigns targeting high-value segments. Personalize experiences. Use advanced analytics to segment clients and craft offers that resonate with their holiday needs or anticipate their possible post-holiday needs. 4. Ensure top-of-mind awareness During the holiday shopping season, competition to be the “top of wallet” is fierce. Experian’s data shows that 58% of high spenders shop evenly across the season, while 31% of average spenders do most of their shopping in December​. Strategies for success: Early engagement: Launch educational campaigns to empower credit education and identity protection during this period of increased transactions. Loyalty programs: Offer incentives, such as discounts or rewards, that encourage repeat engagement during the season. Omnichannel presence: Utilize digital, email, and event marketing to maintain visibility across platforms. 5. Combat fraud with multi-layered strategies The holiday shopping season sees an increase in fraud, with card testing being the number one attack vector in the U.S. according to Experian’s 2024 Identity and Fraud Study. Fraudulent activity such as identity theft and synthetic IDs can also escalate​. Fight tomorrow’s fraud today: Identity verification: Use advanced fraud detection tools, like Experian’s Ascend Fraud Sandbox, to validate accounts in real-time. Monitor dormant accounts: Watch these accounts with caution and assess for potential fraud risk. Strengthen cybersecurity: Implement multi-layered strategies, including behavioral analytics and artificial intelligence (AI), to reduce vulnerabilities. 6. Post-holiday follow-up: retain and manage risk Once the holiday rush is over, the focus shifts to managing potential payment stress and fostering long-term relationships. Post-holiday strategies: Debt monitoring: Keep an eye on debt-to-income and debt-to-limit ratios to identify clients at risk of defaulting. Customer support: Offer tailored assistance programs for clients showing signs of financial stress, preserving goodwill and loyalty. Fraud checks: Watch for first-party fraud and unusual return patterns, which can spike in January. 7. Anticipate consumer trends in the New Year The aftermath of the holidays often reveals deeper insights into consumer health: Rising credit balances: January often sees an uptick in outstanding balances, highlighting the need for proactive credit management. Shifts in spending behavior: According to McKinsey, consumers are increasingly cautious post-holiday, favoring savings and value-based spending. What this means for financial institutions: Align with clients’ needs for financial flexibility. The holiday shopping season is a time that demands precise planning and execution. Financial institutions can maximize their impact during this critical period by starting early, leveraging advanced analytics, and maintaining a strong focus on fraud prevention. And remember, success in the holiday season extends beyond December. Building strong relationships and managing risk ensures a smooth transition into the new year, setting the stage for continued growth. Ready to optimize your strategy? Contact us for tailored recommendations during the holiday season and beyond. Download the Holiday Shopping Season Playbook

 Technology has dramatically transformed the financial services landscape, fostering innovation and enhancing operational efficiency. In an interview at this year’s Money20/20 conference, Scott Brown, Group President of Financial and Marketing Services for Experian, sat down with Fintech Futures’ North America Correspondent Heather Sugg to share how Experian is leveraging data, analytics, and artificial intelligence (AI) to modernize the financial services industry. During the discussion, Scott highlighted the recent launch of Experian Assistant — our newest generative AI tool designed to accelerate the modeling lifecycle, resulting in greater productivity, improved data visibility, and reduced delays and expenses. While Experian Assistant is a business-to-business solution built alongside our clients, Scott also noted its broader impact — helping increase credit access for underserved consumers. “At Experian, we’re really focused on addressing the underserved community who doesn’t have access to credit,” said Scott. “And we think that this tool helps lenders reach those customers in an easier way.”  Learn more about Experian Assistant and watch our tech showcase to see the solution in action. Learn more Watch tech showcase

Despite being a decades-old technology, behavioral analytics is often still misunderstood. We’ve heard from fraud, identity, security, product, and risk professionals that exploring a behavior-based fraud solution brings up big questions, such as: What does behavioral analytics provide that I don’t get now? (Quick answer: a whole new signal and an earlier view of fraud) Why do I need to add even more data to my fraud stack? (Quick answer: it acts with your stack to add insights, not overload) How is this different from biometrics? (Quick answer: while biometrics track characteristics, behavioral analytics tracks distinct actions) These questions make sense — stopping fraud is complex, and, of course, you want to do your research to fully understand what ROI any tool will add. NeuroID, now part of Experian, is one of the only behavioral analytics-first businesses built specifically for stopping fraud. Our internal experts have been crafting behavioral-first solutions to detect everything from simple script fraud bots through to generative AI (genAI) attacks. We know how behavioral analytics works best within your fraud stack, and how to think strategically about using it to stop fraud rings, bot fraud, and other third-party fraud attacks. This primer will provide answers to the biggest questions we hear, so you can make the most informed decisions when exploring how our behavioral analytics solutions could work for you. Q1. What is behavioral analytics and how is it different from behavioral biometrics? A common mistake is to conflate behavioral analytics with behavioral biometrics. But biometrics rely on unique physical characteristics — like fingerprints or facial scans — used for automated recognition, such as unlocking your phone with Face ID. Biometrics connect a person’s data to their identity. But behavioral analytics? They don’t look at an identity. They look at behavior and predict risk. While biometrics track who a person is, behavioral analytics track what they do. For example, NeuroID’s behavioral analytics observes every time someone clicks in a box, edits a field, or hovers over a section. So, when a user’s actions suggest fraudulent intent, they can be directed to additional verification steps or fully denied. And if their actions suggest trustworthiness? They can be fast-tracked. Or, as a customer of ours put it: "Using NeuroID decisioning, we can confidently reject bad actors today who we used to take to step-up. We also have enough information on good applicants sooner, so we can fast-track them and say ‘go ahead and get your loan, we don’t need anything else from you.’ And customers really love that." – Mauro Jacome, Head of Data Science for Addi (read the full Addi case study here). The difference might seem subtle, but it’s important. New laws on biometrics have triggered profound implications for banks, businesses, and fraud prevention strategies. The laws introduce potential legal liabilities, increased compliance costs, and are part of a growing public backlash over privacy concerns. Behavioral signals, because they don’t tie behavior to identity, are often easier to introduce and don’t need the same level of regulatory scrutiny. The bottom line is that our behavioral analytics capabilities are unique from any other part of your fraud stack, full-stop. And it's because we don’t identify users, we identify intentions. Simply by tracking users’ behavior on your digital form, behavioral analytics powered by NeuroID tells you if a user is human or a bot; trustworthy or risky. It looks at each click, edit, keystroke, pause, and other tiny interactions to measure every users’ intention. By combining behavior with device and network intelligence, our solutions provide new visibility into fraudsters hiding behind perfect PII and suspicious devices. The result is reduced fraud costs, fewer API calls, and top-of-the-funnel fraud capture with no tuning or model integration on day one. With behavioral analytics, our customers can detect fraud attacks in minutes, instead of days. Our solutions have proven results of detecting up to 90% of fraud with 99% accuracy (or <1% false positive rate) with less than 3% of your population getting flagged. Q2. What does behavioral analytics provide that I don’t get now? Behavioral analytics provides a net-new signal that you can’t get from any other tools. One of our customers, Josh Eurom, Manager of Fraud for Aspiration Banking, described it this way: “You can quantify some things very easily: if bad domains are coming through you can identify and stop it. But if you see things look odd, yet you can’t set up controls, that’s where NeuroID behavioral analytics come in and captures the unseen fraud.” (read the full Aspiration story here) Adding yet another new technology with big promises may not feel urgent. But with genAI fueling synthetic identity fraud, next-gen fraud bots, and hyper-efficient fraud ring attacks, time is running out to modernize your stack. In addition, many fraud prevention tools today only focus on what PII is submitted — and PII is notoriously easy to fake. Only behavioral analytics looks at how the data is submitted. Behavioral analytics is a crucial signal for detecting even the most modern fraud techniques. Watch our webinar: The Fraud Bot Future-Shock: How to Spot and Stop Next-Gen Attacks  Q3. Why do I need to add even more data to my fraud stack? Balancing fraud, friction, and financial impact has led to increasingly complex fraud stacks that often slow conversions and limit visibility. As fraudsters evolve, gaps grow between how quickly you can keep up with their new technology. Fraudsters have no budget constraints, compliance requirements, or approval processes holding them back from implementing new technology to attack your stack, so they have an inherent advantage. Many fraud teams we hear from are looking for ways to optimize their workflows without adding to the data noise, while balancing all the factors that a fraud stack influences beyond overall security (such as false positives and unnecessary friction). Behavioral analytics is a great way to work smarter with what you have. The signals add no friction to the onboarding process, are undetectable to your customers, and live on a pre-submit level, using data that is already captured by your existing application process. Without requiring any new inputs from your users or stepping into messy biometric legal gray areas, behavioral analytics aggregates, sorts, and reviews a broad range of cross-channel, historical, and current customer behaviors to develop clear, real-time portraits of transactional risks. By sitting top-of-funnel, behavioral analytics not only doesn’t add to the data noise, it actually clarifies the data you currently rely on by taking pressure off of your other tools. With these insights, you can make better fraud decisions, faster. Or, as Eurom put it: “Before NeuroID, we were not automatically denying applications. They were getting an IDV check and going into a manual review. But with NeuroID at the top of our funnel, we implemented automatic denial based on the risky signal, saving us additional API calls and reviews. And we’re capturing roughly four times more fraud. Having behavioral data to reinforce our decision-making is a relief.” The behavioral analytics difference Since the world has moved online, we’re missing the body language clues that used to tell us if someone was a fraudster. Behavioral analytics provides the digital body language differentiator. Behavioral cues — such as typing speed, hesitation, and mouse movements — highlight riskiness. The cause of that risk could be bots, stolen information, fraud rings, synthetic identities, or any combination of third-party fraud attack strategies. Behavioral analytics gives you insights to distinguish between genuine applicants and potentially fraudulent ones without disrupting your customer’s journey. By interpreting behavioral patterns at the very top of the onboarding funnel, behavior helps you proactively mitigate fraud, reduce false positives, and streamline onboarding, so you can lock out fraudsters and let in legitimate users. This is all from data you already capture, simply tracking interactions on your site. Stop fraud, faster: 5 simple uses where behavioral analytics shine  While how you approach a behavioral analytics integration will vary based on numerous factors, here are some of the immediate, common use cases of behavioral analytics.  Detecting fraud bots and fraud rings Behavioral analytics can identify fraud bots by their frameworks, such as Puppeter or Stealth, and through their behavioral patterns, so you can protect against even the most sophisticated fourth-generation bots. NeuroID provides holistic coverage for bot and fraud ring detection — passively and with no customer friction, often eliminating the need for CAPTCHA and reCAPTCHA. With this data alone, you could potentially blacklist suspected fraud bot and fraud ring attacks at the top of the fraud prevention funnel, avoiding extra API calls. Sussing out scams and coercions When users make account changes or transactions under coercion, they often show unfamiliarity with the destination account or shipping address entered. Our real-time assessment detects these risk indicators, including hesitancy, multiple corrections, and slow typing, alerting you in real-time to look closer. Stopping use of compromised cards and stolen IDs Traditional PII methods can fall short against today’s sophisticated synthetic identity fraud. Behavioral analytics uncovers synthetic identities by evaluating how PII is entered, instead of relying on PII itself (which is often corrupted). For example, our behavioral signals can assess users’ familiarity with the billing address they’re entering for a credit card or bank account. Genuine account holders will show strong familiarity, while signs of unfamiliarity are indicators of an account under attack. Detecting money mules Our behavioral analytics solutions track how familiar users are with the addresses they enter, conducting a real-time, sub-millisecond familiarity assessment. Risk markers such as hesitancy, multiple corrections, slow typing speed raise flags for further exploration. Stopping promotion and discount abuse Our behavioral analytics identifies risky versus trustworthy users in promo and discount fields. By assessing behavior, device, and network risk, we help you determine if your promotions attract more risky than trustworthy users, preventing fraudsters from abusing discounts. Learn more about our behavioral analytics solutions. Learn more Watch webinar