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

Do you know where your customers stand? Not literally, of course, but do you know how recent macroeconomic changes and their personal circumstances are currently affecting your portfolio? While refreshing your customers’ credit data quarterly works for some aspects of portfolio management, you need more frequent access to fresh data to quickly respond to risky customer behavior and new credit needs before your portfolio takes a hit. Use triggers to improve portfolio management Event-based credit triggers provide daily or real-time alerts about important changes in your customers’ financial situations. You can use these to manage risk by promptly responding to signs of changing creditworthiness or to prevent attrition by proactively reaching out to customers who are shopping for credit. Risk Triggers℠ and Retention Triggers℠ offer a real-time solution that can be customized to fit your needs for daily portfolio management. What are Risk Triggers? Experian’s Risk Triggers alert you of notable information, such as unfavorable utilization rate changes, delinquencies with other lenders and recent activity with high-interest, short-term loan products. This solution allows you to monitor how your customers manage accounts with other lenders to get ahead of potential risk on your book. You can use Risk Triggers to get daily insights into your customers’ activity — allowing you to quickly identify potentially risky behavior and take appropriate action to limit your exposure and losses. Types of Risk Triggers Choose from a defined Risk Triggers package that could help you identify high-risk customers, including: New trades Increasing credit utilization or balances over limit New collection accounts An account is charged-off A credit grantor closes an account New delinquency statuses (30 to 180 days past due) Consumers seeking access to short-term, high-risk financing options Bankruptcy and deceased events How to use Risk Triggers You can use the daily alerts from Risk Triggers to help inform your account management strategy. Depending on the circumstances, you might: Decrease credit limits Close or freeze accounts Accelerate payment requests Continue monitoring accounts for other signs of risk Spotlight on Experian’s Clarity Services events Included in Risk Triggers are events from Experian’s Clarity Services, which draw on expanded FCRA-regulated data* from a leading source of alternative financial credit data.  For example, you could get an alert when someone has a new inquiry from non-traditional loans. These triggers provide a broader view of the customer – offering added protection against risky behavior. What are Retention Triggers? Experian’s Retention Triggers can alert you when a customer improves their creditworthiness, is shopping for new credit, opens a new tradeline or lists property. Proactively responding to these daily alerts can help you retain and strengthen relationships with your customers — which is often less expensive than acquiring new customers. Types of Retention Triggers Choose from over 100 Retention Triggers to bundle, including: New trades New inquiries Credit line increases Property listing statuses Improving delinquency status Past-due accounts are brought current or paid off How to use Retention Triggers You can use Retention Triggers to increase lifetime customer value by proactively responding to your customers’ needs and wants. You might: Increase credit limits Offer promotional financing, such as balance transfers Introduce perks or rewards to strengthen the relationship Append attributes for improved decisioning  By appending credit attributes to Risk and Retention Trigger outputs, you can gain greater insight into your accounts.  Premier AttributesSM is Experian's core set of 2,100-plus attributes. These can quickly summarize data from consumers' credit reports, allowing you to more easily segment accounts to make more strategic decisions across your portfolio.  Trended 3DTM attributes can help you spot and understand patterns in a customer's behavior over time. Integrating trended attributes into a triggers program can help you identify risk and determine the next best action. Trended 3D includes more than 2,000 attributes and provides insights into industries such as bankcard, mortgage, student loans, personal loans, collections and much more.  By working with both triggers and attributes, you'll proactively review an account, so you can then take the next best action to improve your portfolio's profits. Customize your trigger strategy When you partner with Experian, you can bundle and choose from hundreds of Risk and Retention Triggers to focus on risk, customer retention or both. Additionally, you can work with Experian’s experts to customize your trigger strategy to minimize costs and filter out repetitive or unneeded triggers: Use cool-off periods Set triggering thresholds Choose which triggers to monitor Establish hierarchies for which triggers to prioritize Create different strategies for segments of your portfolio Learn more about Risk and Retention Triggers. Learn more *Disclaimer: “Alternative Financial Credit Data” refers to the use of alternative data and its appropriate use in consumer credit lending decisions, as regulated by the Fair Credit Reporting Act. Hence, the term “Expanded FCRA-Regulated Data” may also apply in this instance, and both can be used interchangeably.

  With cyber threats intensifying and data breaches rising, understanding how to respond to incidents is more important than ever. In this interview, Michael Bruemmer, Head of Global Data Breach Resolution at Experian, is joined by Matthew Meade, Chair of the Cybersecurity, Data Protection & Privacy Group at Eckert Seamans, to discuss the realities of data breach response. Their session, “Cyber Incident Response: A View from the Trenches,” brings insights from the field and offers a preview of Experian's 2025 Data Breach Industry Forecast, including the role of generative artificial intelligence (AI) in data breaches. From the surge in business email compromises (BEC) to the relentless threat of ransomware, Bruemmer and Meade dive into key issues facing organizations big and small today. Drawing from Experian's experience handling nearly 5,000 breaches this year, Bruemmer sheds light on effective response practices and reveals common pitfalls. Meade, who served as editor-in-chief for the Sedona Conference’s new Model Data Breach Notification Law, explains the implications of these regulatory updates for organizations and highlights how standardized notification practices can improve outcomes. Bruemmer and Meade’s insights offer a proactive guide to tackling tomorrow’s cyber threats, making it a must-listen for anyone aiming to stay one step ahead. Listen to the full interview for a valuable look at both the current landscape and what's next.  Click here for more insight into safeguarding your organization from emerging cyber threats.  

As online accounts become essential for activities ranging from shopping and social media to banking, "account farming" has emerged as a significant fraud risk. This practice involves creating fake or unauthorized accounts en masse, often for malicious purposes. Understanding how account farming works, why it’s done and how businesses can protect themselves is crucial for maintaining data integrity, safeguarding customer trust and protecting your bottom line. How does account farming work? Account farming is the process of creating and cultivating multiple user accounts, often using fake or stolen identities. These accounts may look like legitimate users, but they’re controlled by a single entity or organization, usually with fraudulent intent. Here’s a breakdown of the typical steps involved in account farming: Identity generation: Account farmers start by obtaining either fake or stolen personal information. They may buy these datasets on the dark web or scrape publicly available information to make each account seem legitimate. Account creation: Using bots or manual processes, fraudsters create numerous accounts on a platform. Often, they’ll employ automated tools to expedite this process, bypassing CAPTCHA or reCAPTCHA systems or using proxy servers to mask their IP addresses and avoid detection. Warm-up phase: After initial creation, account farmers often let the accounts sit for a while, engaging in limited, non-suspicious activity to avoid triggering security alerts. This “warming up” process helps the accounts seem more authentic. Activation for fraudulent activity: Once these accounts reach a level of credibility, they’re activated for the intended purpose. This might include spamming, fraud, phishing, fake reviews or promotional manipulation. Why is account farming done? There are several reasons account farming has become a widespread problem across different industries. Here are some common motivations: Monetary gain: Fraudsters use farmed accounts to commit fraudulent transactions, like applying for loans and credit products, accessing promotional incentives or exploiting referral programs. Spam and phishing: Fake accounts enable widespread spam campaigns or phishing attacks, compromising customer data and damaging brand reputation. Data theft: By creating and controlling multiple accounts, fraudsters may access sensitive data, leading to further exploitation or resale on the dark web. Manipulating metrics and market perception: Some industries use account farming to boost visibility and credibility falsely. For example, on social media, fake accounts can be used to inflate follower counts or engagement metrics. In e-commerce, fraudsters may create fake accounts to leave fake reviews or upvote products, falsely boosting perceived popularity and manipulating purchasing decisions. How does account farming lead to fraud risks? Account farming is a serious problem that can expose businesses and their customers to a variety of risks: Financial loss: Fake accounts created to exploit promotional offers or referral programs can cause victims to experience significant financial losses. Additionally, businesses can incur costs from chargebacks or fraudulent refunds triggered by these accounts. Compromised customer experience: Legitimate customers may suffer from poor experiences, such as spam messages, unsolicited emails or fraudulent interactions. This leads to diminished brand trust, which is costly to regain. Data breaches and compliance risks: Account farming often relies on stolen data, increasing the risk of data breaches. Businesses subject to regulations like GDPR or CCPA may face hefty fines if they fail to protect consumer information adequately. READ MORE: Our Data Breach Industry Forecast predicts what’s in store for the coming year. How can businesses protect themselves from account farming fraud? As account farming tactics evolve, businesses need a proactive and sophisticated approach to detect and prevent these fraudulent activities. Experian’s fraud risk management solutions provide multilayered and customizable solutions to help companies safeguard themselves against account farming and other types of fraud. Here’s how we can help: Identity verification solutions: Experian’s fraud risk and identity verification platform integrates multiple verification methods to confirm the authenticity of user identities. Through real-time data validation, businesses can verify the legitimacy of user information provided at the account creation stage, detecting and blocking fake identities early in the process. Its flexible architecture allows companies to adapt their identity verification process as new fraud patterns emerge, helping them stay one step ahead of account farmers. Behavioral analytics: One effective way to identify account farming is to analyze user behavior for patterns consistent with automated or scripted actions (AKA “bots”). Experian’s behavioral analytics solutions, powered by NeuroID, use advanced machine learning algorithms to identify unusual behavioral trends among accounts. By monitoring how users interact with a platform, we can detect patterns common in farmed accounts, like uniform interactions or repetitive actions that don’t align with human behavior. Device intelligence: To prevent account farming fraud, it’s essential to go beyond user data and examine the devices used to create and access accounts. Experian’s solutions combine device intelligence with identity verification to flag suspicious devices associated with multiple accounts. For example, account farmers often use virtual machines, proxies or emulators to create accounts without revealing their actual location or device details. By identifying and flagging these high-risk devices, we help prevent fraudulent accounts from slipping through the cracks. Velocity checks: Velocity checks are another way to block fraudulent account creation. By monitoring the frequency and speed at which new accounts are created from specific IP addresses or devices, Experian’s fraud prevention solutions can identify spikes indicative of account farming. These velocity checks work in real-time, enabling businesses to act immediately to block suspicious activity and minimize the risk of fake account creation. Continuous monitoring and risk scoring: Even after initial account creation, continuous monitoring of user activity helps to identify accounts that may have initially bypassed detection but later engage in suspicious behavior. Experian’s risk scoring system assigns a fraud risk score to each account based on its behavior over time, alerting businesses to potential threats before they escalate. Final thoughts: Staying ahead of account farming fraud Preventing account farming is about more than just blocking bots — it’s about safeguarding your business and its customers against fraud risk. By understanding the mechanics of account farming and using a multi-layered approach to fraud detection and identity verification, businesses can protect themselves effectively. Ready to take a proactive stance against account farming and other evolving fraud tactics? Explore our comprehensive solutions today. Learn More This article includes content created by an AI language model and is intended to provide general information.

In the latest episode of “The Chrisman Commentary” podcast, Experian experts Joy Mina, Director of Product Commercialization, and Ivan Ahmed, Senior Director of Product Management, explore how lenders can navigate a tight mortgage market, from rates to equity. “Current rates exceed their locked in rates,” said Ivan. “Homeowners are choosing options as alternatives to selling with the refinancing for cash outs and HELOC." Listen to the full episode for all the details and check out the previous episode to learn how mortgage companies can optimize their business expenses and protect prospects. Listen to podcast

For businesses across all sectors solutions that improve productivity are more important than ever. As technology advances, organizations across industries are looking to capitalize by investing in artificial intelligence (AI) solutions. Studies have recently shown that productivity is a leading measure of how well these AI tools are performing. About 60% of organizations surveyed are using “improved productivity” as a metric to measure the success of implementing AI solutions.[1] Experian research shows it takes an average of 15 months to build a model and put it into production. This can hinder productivity and the ability to quickly go to market. Without a deep understanding of key data points, organizations may also have difficulty realizing time to value efficiently. To improve upon the modeling lifecycle, businesses must examine the challenges involved in the process. The challenges of model building One of the most significant challenges of the modeling lifecycle is speed. Slow modeling processes can cause delays and missed opportunities for businesses which they may have otherwise capitalized on. Another difficulty organizations face is having limited access to high-quality data to build more efficient models. Without the right data, businesses can miss out on actionable insights that could give them a competitive edge. In addition, when organizations have inefficient resources, expenses can skyrocket due to the need for experts to intervene and address ongoing issues. This can result in a steep learning curve as new tools and platforms are adopted, making it difficult for organizations to operate efficiently without outside help. Businesses can combat these challenges by implementing tools such as artificial intelligence (AI) to drive efficiency and productivity. The AI journey While generative AI and large language models are becoming more prevalent in everyday life, the path to incorporating a fully functional AI tool into an organization’s business operations involves multiple steps. Beginning with a proof of concept, many organizations start their AI journey with building ideas and use cases, experimentation, and identifying and mitigating potential pitfalls, such as inaccurate or irrelevant information. Once a proof of concept reaches an acceptable state of validity, organizations can move on to production and value at scale. During this phase, organizations will select specific use cases to move into production and measure their performance. Analyzing the results can help businesses glean valuable information about which techniques work most effectively, so they can apply those techniques to new use cases. Following successful iterations of an efficiently functioning AI, the organization can then implement that AI as a part of their business by working the technology into everyday operations. This can help organizations drive productivity at scale across various business processes. Experian’s AI journey has been ongoing, with years of expertise in implementing AI into various products and services. With a goal of providing greater insights to both businesses and consumers while adhering to proper consumer data privacy and compliance, Experian is committed to responsibly using AI to combat fraud and foster greater financial access and inclusion. Our most recent AI innovation, Experian Assistant, is redefining how financial organizations improve productivity with data-driven insights. Introducing Experian Assistant Experian Assistant, a new GenAI tool announced in October at Money20/20 in Las Vegas, is helping organizations take their productivity to the next level by drastically speeding up the modeling lifecycle. To drive automation and greater intelligence for Experian partners, Experian Assistant enables users to interact with a virtual assistant in real time and offers customized guidance and code generation for our suite of software solutions. Our experts – Senior Director of Product Management Ankit Sinha and Director of Analyst Relations Erin Haselkorn – recently revealed the details of how Experian Assistant can cut down model-development timelines from months to days, and in some cases even hours. The webinar, which took place on November 7th, covered a wide range of features and benefits of the new tool, including:  Spending less time writing code  Enhancing understanding of data and attributes Accelerating time to value Improving regulatory compliance A case study in building models faster Continental Finance Company, LLC’s Chief Data Scientist shared their experience using Experian Assistant and how it has improved their organization’s modeling capabilities: “With Experian Assistant, there is a lot of efficiency and improvement in productivity. We have reduced the time spent on data building by almost 75%, so we can build a model much quicker, and the code being generated by Experian Assistant is very high quality, enabling us to move forward much faster.” For businesses looking to accelerate their modeling lifecycle and move more quickly with less effort, Experian Assistant provides a unique opportunity to significantly improve productivity and efficiency. Experian Assistant tech showcase Did you miss the Experian Assistant Tech Showcase webinar? Watch it on demand here and visit our website to learn more. Visit our website [1] Forrester’s Q2 AI Pulse Survey, 2024

By Erik Hjermstad, VP of Product Management for Experian Automotive In today's digital landscape, where consumers increasingly turn to connected TV and addressable TV for entertainment, precision targeting has become more critical than ever for automotive advertisers. By delivering highly relevant ads to the most likely potential customers, advertisers can maximize campaign effectiveness, increase conversion rates, and ultimately drive a better return on investment (ROI). The following is a summary from a recent article I wrote for Ad Age. The Power of Precision Targeting Precision targeting allows advertisers to focus their efforts on the right people with the right message at the right time. This targeted approach helps minimize wasted ad spend and ensures that every dollar invested makes a meaningful impact. By understanding an audience's demographics, interests, and behaviors, automotive marketers can tailor their messaging to resonate with specific consumer segments and pointedly reach those ready to purchase. Leveraging Data Intelligence for Competitive Advantage To stay competitive, automotive marketers must stay on top of the latest data intelligence. Agencies and marketers have turned to data-driven strategies to set their campaigns apart and increase the likelihood of a consumer purchasing a vehicle. When choosing their consumer audiences, automotive marketers turn to companies like Experian Automotive for data like license, registration, and title. They utilize models based on actual vehicle sales and ownership to target the right audience and measure effectiveness more accurately. Measuring Success: Key Metrics and Omnichannel Measurement To ensure that advertising campaigns deliver results, automotive marketers must have a robust measurement strategy. This involves tracking key metrics such as: Sales: Did the buyer purchase a vehicle? Impressions: The number of times an ad is seen. Clicks: The number of times an ad is clicked on. Conversions: The number of people who take a desired action, such as purchasing. Cost per acquisition (CPA): The cost of acquiring a new customer. Return on ad spend (ROAS): The revenue generated by an ad campaign divided by the cost of the campaign. Experian Automotive offers advertisers a robust measurement solution that provides omnichannel measurement, connecting website visitation and ad exposures to automotive sales. Analysis includes make, model, and vehicle class reporting, competitive analysis, and 30-, 60-, and 90-day vehicle sales projections. The Importance of Omnichannel Measurement In today's interconnected world, automotive consumers interact with brands across multiple channels, including TV, digital, social media, and in-store. Advertisers must adopt an omnichannel measurement approach to truly understand their audience and measure the effectiveness of their campaigns. By tracking consumer behavior across all channels, advertisers can gain a more complete picture of the customer journey and identify opportunities for optimization. Omnichannel measurement integrates data from various sources, such as website analytics, social media metrics, and point-of-sale data. This allows advertisers to understand how consumers interact with their brand at different stages of the buying process and attribute conversions to specific channels and touchpoints. By leveraging omnichannel measurement, advertisers can make more informed decisions about their marketing investments and deliver a more cohesive and personalized customer experience. Measurement is critical to advertising success for several reasons: Optimization: By tracking key metrics, advertisers can identify what is working versus what is not and make necessary adjustments to improve campaign performance. Attribution: Measurement helps determine which marketing channels and tactics drive the most conversions. ROI analysis: By measuring the ROI of their campaigns, advertisers can justify their marketing investments and demonstrate the value they bring to the business. Navigating the Challenges of the Streaming Era While precision targeting and measurement are essential in the streaming era, advertisers face unique challenges. The fragmented nature of the streaming landscape, with its multiple platforms and devices, can make it challenging to track consumer behavior and measure campaign effectiveness. However, advancements in data technology and measurement tools provide advertisers with many new opportunities to overcome these challenges. By leveraging data intelligence, using deterministic data models to understand their audience, and tracking key metrics, advertisers can deliver highly relevant ads that drive results and maximize their ROI.

How can lenders ensure they’re making the most accurate and fair lending decisions? The answer lies in consistent model validations. What are model validations? Model validations are vital for effective lending and risk-based pricing programs. In addition to helping you determine which credit scoring model works best on your portfolio, the performance (odds) charts from validation results are often used to set score cutoffs and risk-based pricing tiers. Validations also provide the information you need to implement a new score into your decisioning process. Factors affecting model validations Understanding how well a score predicts behavior, such as payment delinquency or bankruptcy, enables you to make more confident lending decisions. Model performance and validation results can be impacted by several factors, including: Dynamic economic environment – Shifts in unemployment rates, interest rate hikes and other economic indicators can impact consumer behavior. Regulatory changes affecting consumers – For example, borrowers who benefited from a temporary student loan payment pause may face challenges as they resume payments. Scorecard degradation – A model that performed well several years ago may not perform as well under current conditions. When to perform model validations The Office of the Comptroller of the Currency’s Supervisory Guidance on Model Risk Management states model validations should be performed at least annually to help reduce risk. The validation process should be comprehensive and produce proper documentation. While some organizations perform their own validations, those with fewer resources and access to historical data may not be able to validate and meet the guidance recommendations. Regular validations support compliance and can also give you confidence that your lending strategies are built on solid, current data that drive better outcomes. Good model validation practices are critical if lenders are to continue to make data-driven decisions that promote fairness for consumers and financial soundness for the institution. Make better lending decisions If you’re a credit risk manager responsible for the models driving your lending policies, there are several things you can do to ensure that your organization continues to make fair and sound lending decisions: Assess your model inventory. Ensure you have comprehensive documentation showing when each model was developed and when it was last validated. Validate the scores you are using on your data, along with those you are considering, to compare how well each model performs and determine if you are using the most effective model for your needs. Produce validation documentation, including performance (odds) charts and key performance metrics, which can be shared with regulators. Utilize the performance charts produced from the validation to analyze bad rates/approval rates and adjust cutoff scores as needed. Explore alternative credit scoring models to potentially enhance your scoring process. As market conditions and regulations continue to evolve, model validations will remain an essential tool for staying competitive and making sound lending decisions. Ready to ensure your lending decisions are based on the latest data? Learn more about Experian’s flexible validation services and how we can support your ongoing success. Contact us today to schedule a consultation. Learn more

The advent of artificial intelligence (AI) is significantly transforming the landscape of real estate fraud, enabling criminals to execute complex schemes like deed theft with greater ease. A notable case involves Spelling Manor, a $137.5 million mansion in Los Angeles, where the owner alleges they are entangled in deed fraud. Scammers reportedly filed fraudulent documents that have prevented the owner from selling the estate, thwarting offers from buyers, including former Google CEO Eric Schmidt. Understanding deed/title fraud Deed fraud, also known as title or property fraud, occurs when someone illegally transfers ownership of a property without the owner’s knowledge or consent. Typically, fraudsters create fake documents or forge the owner’s signature on a deed to make it look like the property has been legally transferred to them. Once the title is in their name, they may try to sell or mortgage the property, leaving the original owner unaware until it’s too late. How deed fraud works Identify a target: Fraudsters often look for properties that appear vulnerable, such as vacant land, unoccupied homes, or properties owned by elderly individuals who may not check their records frequently. Forge documentation: Using fake IDs and forged signatures, scammers create documents that appear to show a legitimate transfer of ownership. With modern technology, these documents can look highly convincing. Record the fake deed: Fraudsters then file these documents with the local county clerk or recorder’s office. This officially changes the ownership records, making it seem as if the scammer is the legitimate owner. Exploit the ownership: Once listed as the owner, the fraudster may sell the property to an unsuspecting buyer, take out loans against it, or even rent it out. The impact on victims In the summer of 2024, Elvis Presley’s family got confronted to a forged deed scam. A fake firm, Naussany Investments, falsely claimed Lisa Marie Presley owed millions and used Graceland as collateral. They placed a foreclosure notice and attempted to auction the estate. Riley Keough filed a lawsuit, exposing the firm as fraudulent and halting the foreclosure through a judge’s injunction. Lisa Jeanine Findley, who forged documents and posed as firm employees, was arrested and charged with deed forgery fraud and identity theft. She faces up to 22 years in prison if convicted.  The FBI's Internet Crime Complaint Center does not specifically monitor deed fraud. However, in 2023, it processed a total of 9,521 real estate-related complaints defined as the loss of funds from a real estate investment, resulting in more than $145 million in losses. Victims of deed fraud can face severe financial and legal issues. They may discover the fraud only when trying to sell, refinance, or even pay taxes on the property. Reversing deed fraud typically requires a costly and time-consuming legal process, as courts must determine that the transfer was fraudulent and restore the original owner’s rights. Prevention and safeguards There are several preventive measures and fraud prevention solutions that can be established to help mitigate the risks associated with deed/title fraud. These include: For lending institutions: Enhanced ID verification: Implement multi-factor identity checks at the loan approval stage. Regular portfolio audits: Conduct periodic audits to detect unusual property transfers and title changes in their loan portfolios. For title companies: AI-driven document verification tools: Use machine learning algorithms to identify inconsistencies in deed and ownership documents. Real-time fraud monitoring: Employ analytics to track suspicious behavior patterns, such as rapid ownership changes. Seller authentication: Require biometric or multi-step identity verification for anyone claiming ownership or initiating sales. For realtors: Training and awareness: Educate realtors on how to spot warning signs of fraudulent listings and seller impersonations. Pre-transaction verification: Collaborate with title companies to validate ownership early in the listing process. Acting with the right solution Mortgage fraud is a constant threat that demands ongoing vigilance and adaptability. As fraudsters evolve their tactics, the mortgage industry must stay one step ahead to safeguard homeowners and lenders alike. With concerns over deed/title-related fraud rising, it is vital to raise awareness, strengthen preventive measures, and foster collaboration to protect the integrity of the mortgage market. By staying informed and implementing robust safeguards, we can collectively combat and prevent mortgage fraud from disrupting the financial security of individuals and the industry. Experian mortgage powers advanced capabilities across the mortgage lifecycle by gaining market intelligence, enhancing customer experience to remove friction and tapping into industry leading data sources to gain a complete view of borrower behavior. Visit our website to see how these solutions can help your business prevent deed fraud. Learn more

In today's data-driven business landscape, leveraging advanced targeting techniques is crucial for effective consumer engagement, particularly in the financial services sector. Prescreen targeting solutions have evolved significantly, offering a competitive edge through more precise and impactful outreach strategies. The power of data analytics and predictive modeling At the heart of modern prescreen targeting solutions lies the integration of extensive data analytics and predictive modeling. These systems combine detailed consumer information, including purchasing behaviors and credit scores, with sophisticated algorithms to identify potential customers most likely to respond positively to specific promotional campaigns. This approach not only streamlines campaign efforts but also enhances the tactical effectiveness of each interaction. Direct mail: a proven channel for financial services In the competitive North American financial services market, direct mail has demonstrated distinct advantages as a targeting channel. Its tangible nature helps cut through digital noise, capturing consumer attention effectively. For credit products, direct mail typically achieves engagement rates of 0.2-2% for prime consumers and 1-3% for near-prime and subprime consumers[1]. Key advantages of prescreen targeting solutions Enhanced response rates Custom response models can significantly boost prospect response rates by targeting a well-defined, high-propensity audience. These models have the potential to improve average response rates of prescreen direct mail campaigns by 10-25%. Risk mitigation By focusing on well-defined, high-propensity audiences, prescreen targeting via direct mail aims to attract the right prospects, minimizing fraud and delinquency risks. This targeted approach can lead to substantial savings on underwriting costs. Improved customer engagement and retention Personalized direct mail strengthens customer relationships by making recipients feel valued, leading to higher engagement and loyalty – crucial factors for long-term business success. Regulatory compliance and security Prescreen solutions come equipped with compliance safeguards, simplifying adherence to industry regulations and consumer privacy standards. This is particularly critical in the highly regulated financial sector. The future of targeting and enhancement As markets continue to evolve, the strategic importance of precise and efficient marketing techniques will only grow. Financial institutions leveraging optimized prescreen targeting and enhancement solutions can gain a significant competitive advantage, achieving higher immediate returns and fostering long-term customer loyalty and brand strength. Future advancements in AI and machine learning are expected to further refine prescreen targeting capabilities, offering even more sophisticated tools for marketers to engage effectively with their target audiences. Ascend Intelligence Services™ Target Ascend Intelligence Services Target is a sophisticated prescreening solution that boosts direct mail response rates. It uses comprehensive trended and alternative data, capturing credit and behavior patterns to iterate through direct mail response models and mathematical optimization. This enhances the target strategy and maximizes campaign response, take-up rates, and ROI within business constraints. Visit our website to learn more [1] Experian Research, Data Science Team, July 2024 

The risk of identity theft continues to grow yearly for consumers and businesses alike. Identity theft and fraud cases have nearly tripled over the past ten years, with cybercrime losses totaling more than $10 billion this year alone.[1] An effective way for organizations to combat these threats is to implement a policy of identity risk management. Identity risk management Identity risk management refers to the methods used by organizations to anticipate potential fraud threats, protect themselves and their consumers from those vulnerabilities, resolve any fraud incidents that may occur, and prevent future fraud events from happening again. Businesses can implement these methods through a variety of tools and technologies designed to detect fraud risks and mitigate them as quickly and efficiently as possible.  By recognizing the risks of identity theft, helping consumers who fall victim to fraud, and preventing identity theft in the future, financial institutions can take an effective approach to identity risk management and ensure that their business is protected and their consumers stay safe. Recognizing risks of identity theft Identifying high-risk situations Inform consumers about high-risk situations that could lead to identity theft. Emphasize the dangers of data breaches, cyber-attacks, phishing scams, and social engineering tactics. Advise them to be cautious with personal documents and to avoid using public Wi-Fi for sensitive transactions. By raising awareness, financial institutions can help consumers stay vigilant. Risk-based authentication solutions can also help minimize risk with adaptive authentication methods. With sophisticated risk assessment and a combination of front- and back-end authentication methods, organizations can optimize the consumer experience and their identity risk management simultaneously. Protecting vulnerable information Guide consumers on safeguarding their most vulnerable information. Explain the importance of protecting Social Security numbers, credit card and bank account details, PINs, passwords, and medical records. Offer tips on securing this information and the potential consequences of it falling into the wrong hands. Providing practical advice, such as using password managers, enabling multifactor authentication, and regularly updating passwords, can significantly enhance your consumers’ security. Additionally, offering secure storage solutions for sensitive documents can further protect their information. Helping consumers who fall victim to fraud Providing immediate support If a consumer falls victim to identity theft, financial institutions should be ready to provide immediate support. Establish a clear protocol for reporting fraud and ensure that consumer service representatives are trained to handle such situations. Assist consumers in contacting their banks and credit card companies to report fraud and prevent further unauthorized transactions. Having a dedicated fraud response team can streamline this process and provide consumers with the reassurance that their issue is being handled by experts. This team can also offer personalized advice and support, making the recovery process less daunting for the victim. Helping restore identity To support consumers in the process of restoring their identity, financial institutions can offer identity restoration services as part of their consumer support. These services can include helping consumers navigate the complexities of repairing their credit, disputing fraudulent charges, and securing their accounts against future threats. Preventing identity theft in the future Enhancing personal security measures Encourage consumers to strengthen their personal security measures. Promote the use of strong, unique passwords and two-factor authentication (2FA) for all accounts. Advise them to regularly update and patch their software and devices. Offer services like secure document shredding to prevent thieves from accessing sensitive information. Financial institutions can also strengthen their identity risk management efforts by implementing robust security measures within their own systems. Demonstrating a commitment to security can build trust and encourage consumers to adopt similar practices. Implementing monitoring and alerts Financial institutions can offer identity theft protection services that include regular monitoring of credit reports and account alerts for suspicious activities. Educate consumers on the importance of closely monitoring their financial statements and bills to detect any unauthorized transactions early. Providing tools such as mobile apps that offer real-time alerts for suspicious activities can empower consumers to take immediate action if something seems amiss. Additionally, offering complimentary credit monitoring services can add an extra layer of protection. Leveraging data Data and analytics are among the most powerful tools at a financial institution’s disposal. By leveraging advanced analytics, institutions can identify patterns and anomalies that may indicate fraudulent activity. Machine learning algorithms can analyze vast amounts of transaction data in real- time, flagging suspicious behavior before it escalates into fraud. This proactive approach not only helps in early detection but also minimizes the impact on the consumer. Moreover, data analytics can streamline and improve the consumer experience by reducing false positives and ensuring that legitimate transactions are not unnecessarily flagged. This balance between security and convenience is crucial in maintaining consumer trust and satisfaction. Financial institutions can use these insights to tailor their fraud prevention strategies, using digital identity management solutions to provide more value to consumers. Behavioral analytics Fraud detection technology, such as behavioral analytics, is continually evolving as hacking methods become increasingly sophisticated. Insights from behavioral analytics can help mitigate fraud in real time and prevent identity theft, account takeover, and bot attacks — empowering businesses to provide a seamless consumer experience. Experian’s recent acquisition of NeuroID, an industry leader in behavioral analytics, means we now offer even more modern and frictionless capabilities, enhancing our fraud risk suite by providing a new layer of insight into digital behavioral signals and analytics throughout the consumer lifecycle. This additional level of defense against fraud can empower businesses to ensure that their consumers are safe and secure online. Identity management solutions Consumers are more at risk of identity theft than ever before, and it’s the responsibility of financial institutions to provide protection and support to the people they do business with. Offering identity management solutions can help organizations feel safe and secure about their consumer and business data without adding friction or functioning outside of their risk tolerance. Experian’s identity management tools allow financial institutions to confirm the identities of businesses and consumers with minimal friction, balancing end-user experience with enhanced security. This allows organizations to easily manage authentication events with confidence. Next steps Financial institutions have a vital role in helping consumers manage their risk of identity theft. By recognizing vulnerabilities, providing support to victims, and implementing preventive measures, financial institutions can protect their consumers’ personal information and financial well-being. Proactive identity risk management not only benefits consumers but also builds trust and loyalty with your brand. Protect your business from identity fraud today. Discover how Experian’s cutting-edge identity risk management solutions can safeguard your consumers and streamline your operations. Learn more about our identity management solutions This article includes content created by an AI language model and is intended to provide general information. [1] IdentityTheft.org. 2024 Identity Theft Facts and Statistics.

Summary:  Gen Z, Millennials, Gen X, and Boomers each have unique automotive buying preferences and your strategies to reach each generation should reflect these preferences. The automotive landscape is shaped by the distinct preferences of different generations. From Gen Z to Boomers, each generation brings unique buying habits that automotive marketers must understand to stay competitive. Below is a brief overview of the key insights from the latest reports on Gen Z, Millennials, Gen X, and Boomers. To learn more about how to market to each generation, download the respective playbook. Gen Z (Born 1996–2015) Gen Z buyers are digital natives who prefer compact vehicles. The Honda Civic leads in market share for this group, showcasing their preference for smaller, fuel-efficient cars. They rely heavily on digital platforms for their research, with 73% of impressions delivered through Connected TV (CTV). Millennials (Born 1981–1995) Millennials value technology and eco-friendly options. Crossovers (CUVs) are their top choice, making up 50.2% of new vehicle registrations. Electric and hybrid vehicles are also popular, reflecting their environmental consciousness. Popular models include the Honda Civic and Toyota RAV4. Gen X (Born 1965–1980) Gen X buyers favor practicality and reliability, gravitating toward SUVs and trucks. The Ford F-150 is the top model among this group, and they are more likely to invest in luxury or exotic vehicles than younger generations. Boomers (Born 1946–1964) Boomers remain loyal to traditional brands, with a strong preference for non-luxury and luxury cars, such as the Honda CR-V and Ford F-150. They also favor gas-powered vehicles over electric, though hybrid options are gaining ground. Summary Understanding generational differences is crucial to developing effective marketing strategies that resonate with each group’s unique preferences. For a more detailed generational analysis, download the full report. Experian Automotive is here to help you with your marketing needs. If you’d like to learn more about our solutions and how we can support you, contact us below.

The open banking revolution is transforming the financial services landscape, offering banks and financial institutions unprecedented access to consumer-permissioned data. However, during our recent webinar, “Navigating Open Banking: Strategies for Banks and Financial Institutions,” over 78% of attendees stated that they do not currently have an open banking strategy in place. This highlights a significant gap in the industry. By tapping into consumer-permissioned data, you can develop more personalized products, streamline credit decisioning, and improve overall customer engagement. With the right strategies, open banking offers a pathway to growth, innovation, and enhanced customer experiences. Here’s a snippet from the webinar’s Q&A session with Ashley Knight, Senior Vice President of Product Management, who shared her perspective on open banking trends and opportunities. Q: What specific analytic skill is the most important when working on open banking data?A: The ability to parse and transform raw data, a deep understanding of data mining, experience in credit risk, and general modeling skills to improve underwriting. Q: What lessons did the U.S. learn from the experience of other countries that implemented open banking? A: The use cases are common globally; typical uses of open banking data include second-chance underwriting to help score more consumers and customer management, which involves assessing cashflow data to leverage on an existing portfolio (first-party data). This can be used in various ways, such as cross-sell, up-sell, credit line increase, and growing/retaining deposits. Q: Does Experian have access to all a consumer’s bank accounts in cases where the consumer has multiple accounts?A: Data access is always driven by consumer permission unless the organization owns this data (i.e., first-party data). Where first-party data is unavailable, we collect it through clients or lenders who send it to us directly, having gained the proper consent. Yes, we can intake data from multiple accounts and provide a categorization and attribute calculation. Q: Where does the cashflow data come from? Is it only credit card spending?A: It includes all spending data from bank accounts, checking accounts, credit cards, savings, debit cards, etc. All of this can be categorized, and we can calculate attributes and/or scores based on that data. Q: What is the coverage of Experian’s cashflow data, and how is it distributed across risk bands?A: Cashflow data moves through Experian directly from consumer permissioning for B2B use cases or from institutions with first-party data. We perform analytics and calculate attributes on that portfolio. Don’t miss the chance to learn from our industry leaders on how to navigate the complexities of open banking. Whether you are a seasoned professional or just starting to explore its potential, this webinar will equip you with the knowledge you need to stay ahead. Watch on-demand recording Learn more Meet our expert Ashley Knight, Senior VP of Product Management, Experian Ashley leads our product management team focusing on alternative data, scores, and open banking. She fosters innovation and drives financial inclusion by using new data, such as cash flow, analytics, and Experian’s deep expertise in credit.

In 2023, mobile fraud attacks surged by over 50%.1 With people relying more on mobile devices for day-to-day activities, like banking, shopping and healthcare, fraudsters have found new ways to exploit mobile security. With phones housing such sensitive data, how can businesses ensure that the person on the other end of a mobile device is who they claim to be? Enter mobile identity verification, a process designed to protect consumers and businesses in today’s mobile-driven world. Understanding mobile identity Mobile identity refers to the digital identity associated with a mobile device. This includes information like phone numbers, SIM cards, device IDs and user credentials that uniquely identify a person or device. Verifying that the mobile identity belongs to the correct individual is crucial for secure digital transactions. What is mobile identity verification? Mobile identity verification confirms the legitimacy of users accessing services via their mobile device. This process uses personal data, biometrics and mobile network information to authenticate identity, ensuring businesses interact with real customers without unnecessary friction. Why is mobile identity verification important? The rise of mobile banking, mobile payments and other mobile-based services has increased the need for robust security measures. Cybercriminals have found ways to exploit the mobile ecosystem through SIM swapping, phishing and other fraud tactics. This makes mobile identity verification critical for businesses looking to protect sensitive customer data and prevent unauthorized access. Here are some of the key reasons why mobile identity verification is essential: Preventing fraud: Identity theft and fraud are major concerns for businesses and consumers alike. Mobile identity verification helps to reduce the risk of fraud by ensuring that the user is who they say they are. Enhancing user trust: Customers are more likely to trust a service that prioritizes their security. Businesses that implement mobile identity verification solutions provide an extra layer of protection, which can help build customer confidence. Regulatory compliance: Many industries, including finance and healthcare, are subject to strict regulations concerning data privacy and security. Mobile identity verification helps businesses meet these regulatory requirements by offering a secure way to verify customer identities. Improving user experience: While security is essential, businesses must also ensure that they do not create a cumbersome user experience. Mobile identity verification solutions offer a quick and seamless way for users to verify their identities without sacrificing security. This is especially important for onboarding new users or completing transactions quickly. How does mobile identity verification work? Mobile identity verification involves a combination of different techniques and technologies, depending on the service provider and the level of security required. Some common methods include: Biometric authentication: Biometrics like fingerprint scans, facial recognition and voice recognition are becoming increasingly popular for verifying identities. These methods are secure and convenient for users since they don't require remembering passwords or PINs. SMS-based verification: One-time passwords (OTPs) sent via SMS to a user's mobile phone are still widely used. This method links the verification process directly to the user's mobile device, ensuring that they have possession of their registered phone number. Device-based verification: By analyzing the unique identifiers of a mobile device, such as IMEI numbers, businesses can confirm that the device is registered to the user attempting to access services. This helps prevent fraud attempts from unregistered or stolen devices. Mobile network data: Mobile network operators have access to valuable information, such as the user’s location, SIM card status and network activity. By leveraging this data, businesses can further verify that the user is legitimate and actively using their mobile network as expected. Behavioral analytics: By analyzing patterns in user behavior — such as typing speed, navigation habits, and interactions with apps — mobile identity verification solutions can detect anomalies that might indicate fraudulent activity. For instance, if a user’s behavior demonstrates low-to-no familiarity with the PII they provide, it can trigger an additional layer of verification to ensure security. The role of identity solutions in mobile identity verification Mobile identity verification is just one part of a broader range of identity solutions that help businesses authenticate users and protect sensitive data. These solutions not only cover mobile devices but extend to other digital touchpoints, ensuring that organizations have a holistic, multilayered approach to identity verification across all channels. Companies that provide comprehensive identity verification solutions can help organizations build robust security infrastructures while offering seamless customer experiences. For instance, Experian offers cutting-edge solutions designed to meet the growing demand for secure and efficient identity verification and authentication. These solutions can significantly reduce fraud and improve customer satisfaction. The growing importance of digital identity In the digital age, managing and verifying identities extends beyond traditional physical credentials like driver’s licenses or social security numbers. Digital identity plays an essential role in enabling secure online transactions, personalizing user experiences and protecting individuals' privacy. However, with great convenience comes great responsibility. Businesses need to strike a balance between security and personalization to ensure they protect user data while still offering a smooth customer experience. As mobile identity verification becomes more widespread, it’s clear that safeguarding digital identity is more important than ever. To learn more about the importance of digital identity and how businesses can find the right balance between security and personalization, check out this article: Digital identity: finding the balance between personalization and security. How Experian can help Experian is at the forefront of providing innovative identity verification solutions that empower businesses to protect their customers and prevent fraud. With solutions tailored for mobile identity verification, businesses can seamlessly authenticate users while minimizing friction. Experian’s technology integrates behavioral analytics, device intelligence and mobile network data to create a comprehensive and secure identity verification process. Whether you’re looking for a complete identity verification solution or need specialized mobile identity verification services, Experian’s identity verification and authentication solutions offer the solutions and expertise your organization needs to stay secure in the evolving digital landscape. Learn More 1 Kapersky This article includes content created by an AI language model and is intended to provide general information.    

This series will explore our monthly State of the Economy report, which provides a snapshot of the top monthly economic and credit data for financial service professionals to proactively shape their business strategies. After the Federal Reserve announced its first cut since 2020 in September, several pieces of economic data have surpassed expectations. Job creation was almost double economists’ estimates, unemployment ticked down, and personal incomes were revised up. Alongside these areas of strength, inflation continued to prove stubborn. The October State of the Economy report fills in the rest of the developing macroeconomic story. This month’s highlights include:  Unemployment decreased for the second month in a row, down to 4.1%. Core inflation increased from 3.2% to 3.3%, driven by shelter and service costs. Negative rental payment activity has declined 1.9% over the past year. Check out our report for a detailed analysis of the rest of this month’s data, including the latest trends in originations, retail sales, and consumer sentiment. Download October's report As our economy continues to fluctuate, it's critical to stay updated on the latest developments. Subscribe to our new series, The Macro Moment, for economic commentary from Experian NA’s Chief Economist, Joseph Mayans, with additional economic resources, including our new Lending Conditions Chartbook and our new Labor Market Monitor. For more economic trends and market insights, visit Experian Edge.