All posts by Julie Lee

Picture this: you’re sipping your morning coffee when an urgent email from your CEO pops up in your inbox, requesting sensitive information. Everything about it seems legit — their name, email address, even their usual tone. But here’s the twist: it’s not actually them. This is the reality of spoofing attacks. And these scenarios aren’t rare. According to the Federal Bureau of Investigation (FBI), spoofing/phishing is the most common type of cybercrime.¹   In these attacks, bad actors disguise their identity to trick individuals or systems into believing the communication is from a trusted source. Whether it’s email spoofing, caller ID spoofing, or Internet Protocol (IP) spoofing, the financial and reputational consequences can be severe. By understanding how these attacks work and implementing strong defenses, organizations can reduce their risk and protect sensitive information. Let’s break down the key strategies for staying one step ahead of cybercriminals. What is a spoofing attack? A spoofing attack occurs when a threat actor impersonates a trusted source to gain access to sensitive information, disrupt operations or manipulate systems. Common types of spoofing attacks include: Email spoofing: Fraudulent emails are carefully crafted to mimic legitimate senders, often including convincing details like company logos, real employee names, and professional formatting. These emails trick recipients into sharing sensitive information, such as login credentials or financial details, or prompt them to download malware disguised as attachments. For example, attackers might impersonate a trusted vendor to redirect payments or a senior executive requesting immediate access to confidential data. Caller ID spoofing: Attackers manipulate phone numbers to impersonate trusted contacts, making calls appear as if they are coming from legitimate organizations or individuals. This tactic is often used to extract sensitive information, such as account credentials, or to trick victims into making payments. For instance, a scammer might pose as a bank representative calling to warn of suspicious activity on an account, coercing the recipient into sharing private information or transferring funds. IP spoofing: IP addresses are falsified to disguise the origin of malicious traffic to bypass security measures and mask malicious activity. Cybercriminals use this method to redirect traffic, conduct man-in-the-middle attacks, where a malicious actor intercepts and possibly alters the communication between two parties without their knowledge, or overwhelm systems with distributed denial-of-service (DDoS) attacks. For example, attackers might alter the source IP address of a data packet to appear as though it is coming from a trusted source, making it easier to infiltrate networks and compromise sensitive data. These tactics are often used in conjunction with other cyber threats, such as phishing or bot fraud, making detection and prevention more challenging. How behavioral analytics can combat spoofing attacks Traditional fraud prevention methods provide a strong foundation but behavioral analytics adds a powerful layer to fraud stacks. By examining user behavior patterns, behavioral analytics enhances existing tools to: Detect anomalies that signal a spoofing attack. Identify bot fraud attempts, where automated scripts mimic legitimate users. Enhance fraud prevention solutions with friction-free, real-time insights. Behavioral analytics is particularly effective when paired with device and network intelligence and machine learning (ML) solutions. These advanced tools can continuously adapt to new fraud tactics, ensuring robust protection against evolving threats. The role of artificial intelligence (AI) and ML in spoofing attack prevention AI fraud detection is revolutionizing how organizations protect themselves from spoofing attacks. By leveraging AI analytics and machine learning solutions, organizations can: Analyze vast amounts of data to identify spoofing patterns. Automate threat detection and response. Strengthen overall fraud prevention strategies. These technologies are essential for staying ahead of cybercriminals, particularly as they increasingly use AI to perpetrate attacks.   Best practices for preventing spoofing attacks Organizations can take proactive steps to minimize the risk of spoofing attacks. Key strategies include: Implementing robust authentication protocols: Use multifactor authentication (MFA) to verify the identity of users and systems. Monitoring network traffic: Deploy tools that can analyze traffic for signs of IP spoofing or other anomalies. Leveraging behavioral analytics: Adopt advanced fraud prevention solutions that include behavioral analytics to detect and mitigate threats. Educating employees: Provide training on recognizing phishing attempts and other spoofing tactics. Partnering with fraud prevention experts: Collaborate with trusted providers like Experian to access cutting-edge solutions tailored to your needs. Why proactive prevention matters The financial and reputational damage caused by spoofing attacks can be devastating. Organizations that fail to implement effective prevention measures risk: Losing customer trust. Facing regulatory penalties. Incurring significant financial losses. Businesses can stay ahead of cyber threats by prioritizing spoofing attack prevention and leveraging advanced technologies such as behavioral analytics, AI fraud detection, and machine learning, Investing in fraud prevention solutions today is essential for protecting your organization’s future. How we help organizations detect spoofing attacks Spoofing attacks are an ever-present danger in the digital age. With tactics like IP spoofing and bot fraud becoming more sophisticated, businesses must adopt advanced strategies to safeguard their operations. Our comprehensive suite of fraud prevention solutions can help businesses tackle spoofing attacks and other cyber threats. Our advanced technologies like behavioral analytics, AI fraud detection and machine learning solutions, enable organizations to: Identify and respond to spoofing attempts in real-time. Detect anomalies and patterns indicative of fraudulent behavior. Strengthen defenses against bot fraud and IP spoofing. Ensure compliance with industry regulations and standards. Click ‘learn more’ below to explore how we can help protect your organization. Learn more 1 https://www.ic3.gov/AnnualReport/Reports/2023_IC3Report.pdf This article includes content created by an AI language model and is intended to provide general information. 

Bots have been a consistent thorn in fraud teams’ side for years. But since the advent of generative AI (genAI), what used to be just one more fraud type has become a fraud tsunami. This surge in fraud bot attacks has brought with it:  A 108% year-over-year increase in credential stuffing to take over accounts1  A 134% year-over-year increase in carding attacks, where stolen cards are tested1  New account opening fraud at more than 25% of businesses in the first quarter of 2024  While fraud professionals rush to fight back the onslaught, they’re also reckoning with the ever-evolving threat of genAI. A large factor in fraud bots’ new scalability and strength, genAI was the #1 stress point identified by fraud teams in 2024, and 70% expect it to be a challenge moving forward, according to Experian’s U.S. Identity and Fraud Report.  This fear is well-founded. Fraudsters are wasting no time incorporating genAI into their attack arsenal. GenAI has created a new generation of fraud bot tools that make bot development more accessible and sophisticated. These bots reverse-engineer fraud stacks, testing the limits of their targets’ defenses to find triggers for step-ups and checks, then adapt to avoid setting them off.   How do bot detection solutions fare against this next generation of bots?  The evolution of fraud bots   The earliest fraud bots, which first appeared in the 1990s2 , were simple scripts with limited capabilities. Fraudsters soon began using these scripts to execute basic tasks on their behalf — mainly form spam and light data scraping. Fraud teams responded, implementing bot detection solutions that continued to evolve as the threats became more sophisticated.   The evolution of fraud bots was steady — and mostly balanced against fraud-fighting tools — until genAI supercharged it. Today, fraudsters are leveraging genAI’s core ability (analyzing datasets and identifying patterns, then using those patterns to generate solutions) to create bots capable of large-scale attacks with unprecedented sophistication. These genAI-powered fraud bots can analyze onboarding flows to identify step-up triggers, automate attacks at high-volume times, and even conduct “behavior hijacking,” where bots record and replicate the behaviors of real users.  How next-generation fraud bots beat fraud stacks  For years, a tried-and-true tool for fraud bot detection was to look for the non-human giveaways: lightning-fast transition speeds, eerily consistent keystrokes, nonexistent mouse movements, and/or repeated device and network data were all tell-tale signs of a bot. Fraud teams could base their bot detection strategies off of these behavioral red flags.  Stopping today’s next-generation fraud bots isn’t quite as straightforward. Because they were specifically built to mimic human behavior and cycle through device IDs and IP addresses, today’s bots often appear to be normal, human applicants and circumvent many of the barriers that blocked their predecessors. The data the bots are providing is better, too3, fraudsters are using genAI to streamline and scale the creation of synthetic identities.4 By equipping their human-like bots with a bank of high-quality synthetic identities, fraudsters have their most potent, advanced attack avenue to date.   Skirting traditional bot detection with their human-like capabilities, next-generation fraud bots can bombard their targets with massive, often undetected, attacks. In one attack analyzed by NeuroID, a part of Experian, fraud bots made up 31% of a business's onboarding volume on a single day. That’s nearly one-third of the business’s volume comprised of bots attempting to commit fraud. If the business hadn’t had the right tools in place to separate these bots from genuine users, they wouldn’t have been able to stop the attack until it was too late.   Beating fraud bots with behavioral analytics: The next-generation approach  Next-generation fraud bots pose a unique threat to digital businesses: their data appears legitimate, and they look like a human when they’re interacting with a form. So how do fraud teams differentiate fraud bots from an actual human user?  NeuroID’s product development teams discovered key nuances that separate next-generation bots from humans, and we’ve updated our industry-leading bot detection capabilities to account for them. A big one is mousing patterns: random, erratic cursor movements are part of what makes next-generation bots so eerily human-like, but their movements are still noticeably smoother than a real human’s. Other bot detection solutions (including our V1 signal) wouldn’t flag these advanced cursor movements as bot behavior, but our new signal is designed to identify even the most granular giveaways of a next-generation fraud bot.  Fraud bots will continue to evolve. But so will we. For example, behavioral analytics can identify repeated actions — down to the pixel a cursor lands on — during a bot attack and block out users exhibiting those behaviors. Our behavior was built specifically to combat next-gen challenges with scalable, real-time solutions. This proactive protection against advanced bot behaviors is crucial to preventing larger attacks.  For more on fraud bots’ evolution, download our Emerging Trends in Fraud: Understanding and Combating Next-Gen Bots report.  Learn more Sources 1 HUMAN Enterprise Bot Fraud Benchmark Report  2 Abusix 3 NeuroID 4 Biometric Update

There’s a common saying in the fraud prevention industry: where there’s opportunity, fraudsters are quick to follow. Recent advances in technology are providing ample new opportunities for cybercriminals to exploit. One of the most prevalent techniques being observed today is password spraying. From email to financial and health records, consumers and businesses are being impacted by this pervasive form of fraud. Password spraying attacks often fly under the radar of traditional security measures, presenting a unique and growing threat to businesses and individuals.  What is password spraying?  Also known as credential guessing, password spraying involves an attacker applying a list of commonly used passwords against a list of accounts in order to guess the correct password. When password spraying first emerged, an individual might hand key passwords to try to gain access to a user’s account or a business’s management system.   Credential stuffing is a similar type of fraud attack in which an attacker gains access to a victim’s credentials in one system (e.g., their email, etc.) and then attempts to apply those known credentials via a script/bot to a large number of sites in order to gain access to other sites where the victim might be using the same credentials. Both are brute-force attack vectors that eventually result in account takeover (ATO), compromising sensitive data that is subsequently used to scam, blackmail, or defraud the victim.  As password spraying and other types of fraud evolved, fraud rings would leverage “click farms” or “fraud farms” where hundreds of workers would leverage mobile devices or laptops to try different passwords in order to perpetrate fraud attacks on a larger scale. As technology has advanced, bot attacks fueled by generative AI (Gen AI) have taken the place of humans in the fraud ring. Now, instead of hand-keying passwords into systems, workers at fraud farms are able to deploy hundreds or thousands of bots that can work exponentially faster.  The rise and evolution of bots  Bots are not necessarily new to the digital experience — think of the chatbot on a company’s support page that helps you find an answer more quickly. These automated software applications carry out repetitive instructions mimicking human behavior. While they can be helpful, they can also be leveraged by fraudsters, to automate fraud on a brute-force attack, often going undetected resulting in substantial losses.   Generation 4 bots are the latest evolution of these malicious programs, and they’re notoriously hard to detect. Because of their slow, methodical, and deliberate human-like behavior, they easily bypass network-level controls such as firewalls and popular network-layer security.  Stopping Gen4 bots  For any company with a digital presence or that leverages digital networks as part of doing business, the threat from Gen AI enabled fraud is paramount. The traditional stack for fighting fraud including firewalls, CAPTCHA and block lists are not enough in the face of Gen4 bots. Companies at the forefront of fighting fraud are leveraging behavioral analytics to identify and mitigate Gen AI-powered fraud. And many have turned to industry leader, Neuro ID, which is now part of Experian.  Watch our on-demand webinar: The fraud bot future-shock: How to spot & stop next-gen attacks  Behavioral analytics is a key component of passive and continuous authentication and has become table stakes in the fraud prevention space. By measuring how a user interacts with a form field (e.g., a website, mobile app, etc.) our behavioral analytics solutions can determine if the user is: a potential fraudster, a bot, or a genuine user familiar with the PII entered. Because it’s available at any digital engagement, behavioral data is often the most consistent signal available throughout the customer lifecycle and across geographies. It allows risky users to be rejected or put through more rigorous authentication, while trustworthy users get a better experience, protecting businesses and consumers from Gen AI-enabled fraud.  As cyber threats evolve, so must our defenses. Password spraying exemplifies the sophisticated methods and technologies attackers now employ to scale their fraud efforts and gain access to sensitive information. To fight next-generation fraud, organizations must employ next-generation technologies and techniques to better defend themselves against this and other types of cyberattacks.  Experian’s approach embodies a paradigm shift where fraud detection increases efficiency and accuracy without sacrificing customer experience. We can help protect your company from bot attacks, fraudulent accounts and other malicious attempts to access your sensitive data. Learn more about behavioral analytics and our other fraud prevention solutions.  Learn more

Dormant fraud, sleeper fraud, trojan horse fraud . . . whatever you call it, it’s an especially insidious form of account takeover fraud (ATO) that fraud teams often can’t detect until it’s too late. Fraudsters create accounts with stolen credentials or gain access to existing ones, onboard under the fake identity, then lie low, waiting for an opportunity to attack.   It takes a strategic approach to defeat the enemy from within, and fraudsters assume you won’t have the tools in place to even know where to start.   Dormant fraud uncovered: A case study  NeuroID, a part of Experian, has seen the dangers of dormant fraud play out in real time.  As a new customer to NeuroID, this payment processor wanted to backtest their user base for potential signs of fraud. Upon analyzing their customer base’s onboarding behavioral data, we discovered more than 100K accounts were likely to be dormant fraud. The payment processor hadn’t considered these accounts suspicious and didn’t see any risk in letting them remain active, despite the fact that none of them had completed a transaction since onboarding.  Why did we flag these as risky?  Low familiarity: Our testing revealed behavioral red flags, such as copying and pasting into fields or constant tab switching. These are high indicators that the applicant is applying with personally identifiable information (PII) that isn’t their own.  Fraud clusters: Many of these accounts used the same web browser, device, and IP address during sign-up, suggesting that one fraudster was signing up for multiple accounts. We found hundreds of clusters like these, many with 50 or more accounts belonging to the same device and IP address within our customer’s user base.  It was clear that this payment processor’s fraud stack had gaps that left them vulnerable. These dormant accounts could have caused significant damage once mobilized: receiving or transferring stolen funds, misrepresenting their financial position, or building toward a bust-out.   Dormant fraud thrives in the shadows beyond onboarding. These fraudsters keep accounts “dormant” until they’re long past onboarding detection measures. And once they’re in, they can often easily transition to a higher-risk account — after all, they’ve already confirmed they’re trustworthy. This type of attack can involve fraudulent accounts remaining inactive for months, allowing them to bypass standard fraud detection methods that focus on immediate indicators.   Dormant fraud gets even more dangerous when a hijacked account has built trust just by existing. For example, some banks provide a higher credit line just for current customers, no matter their activities to date. The more accounts an identity has in good standing, the greater the chance that they’ll be mistaken for a good customer and given even more opportunities to commit higher-level fraud.  This is why we often talk to our customers about the idea of progressive onboarding as a way to overcome both dormant fraud risks and the onboarding friction caused by asking for too much information, too soon.   Progressive onboarding, dormant fraud, and the friction balance  Progressive onboarding shifts from the one-size-fits-all model by gathering only truly essential information initially and asking for more as customers engage more. This is a direct counterbalance to the approach that sometimes turns customers off by asking for too much too soon, and adding too much friction at initial onboarding. It also helps ensure ongoing checks that fight dormant fraud. We’ve seen this approach (already growing popular in payment processing) be especially useful in every type of financial business. Here’s how it works:  A prospect visits your site to explore options. They may just want to understand fees and get a feel for your offerings. At this stage, you might ask for minimal information — just a name and email — without requiring a full fraud check or credit score. It’s a low commitment ask that keeps things simple for casual prospects who are just browsing, while also keeping your costs low so you don’t spend a full fraud check on an uncommitted visitor.   As the prospect becomes a true customer and begins making small transactions, say a $50 transfer, you request additional details like their date of birth, physical address, or phone number. This minor step-up in information allows for a basic behavioral analytics fraud check while maintaining a low barrier of time and PII-requested for a low-risk activity.  With each new level of engagement and transaction value, the information requested increases accordingly. If the customer wants to transfer larger amounts, like $5,000, they’ll understand the need to provide more details — it aligns with the idea of a privacy trade-off, where the customer’s willingness to share information grows as their trust and need for services increase. Meanwhile, your business allocates resources to those who are fully engaged, rather than to one-time visitors or casual sign-ups, and keeps an eye on dormant fraudsters who might have expected no barrier to additional transactions.  Progressive onboarding is not just an effective approach for dormant fraud and onboarding friction, but also in fighting fraudsters who sneak in through unseen gaps. In another case, we worked with a consumer finance platform to help identify gaps in their fraud stack. In one attack, fraudsters probed until they found the product with the easiest barrier of entry: once inside they went on to immediately commit a full-force bot attack on higher value returns. The attack wasn’t based on dormancy, but on complacency. The fraudsters assumed this consumer finance platform wouldn’t realize that a low controls onboarding for one solution could lead to ease of access to much more. And they were right.  After closing that vulnerability, we helped this customer work to create progressive onboarding that includes behavior-based fraud controls for every single user, including those already with accounts, who had built that assumed trust, and for low-risk entry-points. This weeded out any dormant fraudsters already onboarded who were trying to take advantage of that trust, as they had to go through behavioral analytics and other new controls based on the risk-level of the product.   Behavioral analytics gives you confidence that every customer is trustworthy, from the moment they enter the front door to even after they’ve kicked off their shoes to stay a while.  Behavioral analytics shines a light on shadowy corners  Behavioral analytics are proven beyond just onboarding — within any part of a user interaction, our signals detect low familiarity, high-risk behavior and likely fraud clusters. In our experience, building a progressive onboarding approach with just these two signal points alone would provide significant results — and would help stop sophisticated fraudsters from perpetrating dormant fraud, including large-scale bust outs.  Want to find out how progressive onboarding might work for you? Contact us for a free demo and deep dive into how behavioral analytics can help throughout your user journey.  Contact us for a free demo

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

  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 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.    

In this article...What is reject inference? How can reject inference enhance underwriting? Techniques in reject inference Enhancing reject inference design for better classification How Experian can assist with reject inference  In the lending world, making precise underwriting decisions is key to minimizing risks and optimizing returns. One valuable yet often overlooked technique that can significantly enhance your credit underwriting process is reject inferencing. This blog post offers insights into what reject inference is, how it can improve underwriting, and various reject inference methods.  What is reject inference?  Reject inference is a statistical method used to predict the potential performance of applicants who were rejected for a loan or credit — or approved but did not book. In essence, it helps lenders and financial institutions gauge how rejected or non-booked applicants might have performed had they been accepted or booked. By incorporating reject inference, you gain a more comprehensive view of the applicant pool, which leads to more informed underwriting decisions.  Utilizing reject inference helps reduce biases in your models, as decisions are based on a complete set of data, including those who were initially rejected. This technique is crucial for refining credit risk models, leading to more accurate predictions and improved financial outcomes.  How can reject inference enhance underwriting?  Incorporating reject inference into your underwriting process offers several advantages:  Identifying high-potential customers: By understanding the potential behavior of rejected applicants, you can uncover high-potential customers who might have been overlooked before.  Improved risk assessment: Considering the full spectrum of applicants provides a clearer picture of the overall risk landscape, allowing for more informed lending decisions. This can help reduce default rates and enhance portfolio performance.  Optimizing credit decisioning models: Including inferred data from rejected and non-booked applicants makes your credit scoring models more representative of the entire applicant population. This results in more robust and reliable predictions.  Techniques in reject inference  Several techniques are employed in reject inference, each with unique strengths and applications. Understanding these techniques is crucial for effectively implementing reject inference in your underwriting process. Let's discuss three commonly used techniques:  Parceling: This technique involves segmenting rejected applicants based on their characteristics and behaviors, creating a more detailed view of the applicant pool for more precise predictions.  Augmentation: This method adds inferred data to the dataset of approved applicants, producing a more comprehensive model that includes both approved and inferred rejected applicants, leading to better predictions.  Reweighting: This technique adjusts the weights of approved applicants to reflect the characteristics of rejected applicants, minimizing bias towards the approved applicants and improving prediction accuracy.  Pre-diction method  The pre-diction method is a common approach in reject inference that uses data collected at the time of application to predict the performance of rejected applicants. The advantage of this method is its reliance on real-time data, making it highly relevant and current.  For example, pre-diction data can include credit bureau attributes from the time of application. This method helps develop a model that predicts the outcomes of rejected applicants based on performance data from approved applicants. However, it may not capture long-term trends and could be less effective for applicants with unique characteristics.  Post-diction method  The post-diction method uses data collected after the performance window to predict the performance of rejected applicants. Leveraging historical data, this method is ideal for capturing long-term trends and behaviors.  Post-diction data may include credit bureau attributes from the end of the performance window. This method helps develop a model based on historical performance data, which is beneficial for applicants with unique characteristics and can lead to higher performance metrics. However, it may be less timely and require more complex data processing compared to pre-diction.  Enhancing reject inference design for better classification  To optimize your reject inference design, focus on creating a model that accurately classifies the performance of rejected and non-booked applicants. Utilize a combination of pre-diction and post-diction data to capture both real-time and historical trends.  Start by developing a parceling model using pre-diction data, such as credit bureau attributes from the time of application, to predict rejected applicants' outcomes. Regularly update your model with the latest data to maintain its relevance.  Next, incorporate post-diction data, including attributes from the end of the performance window, to capture long-term trends. Combining both data types will result in a more comprehensive model.  Consider leveraging advanced analytics techniques like machine learning and artificial intelligence to refine your model further, identifying hidden patterns and relationships for more accurate predictions. How Experian can assist with reject inference  Reject inference is a powerful tool for enhancing your underwriting process. By predicting the potential performance of rejected and non-booked applicants, you can make more inclusive and accurate decisions, leading to improved risk assessment and optimized credit scoring models.  Experian offers various services and solutions to help financial institutions and lenders effectively implement reject inference into their decisioning strategy. Our solutions include comprehensive and high-quality datasets, which empower you to build models that are more representative of the entire applicant population. Additionally, our advanced analytics tools simplify data analysis and model development, enabling you to implement reject inference efficiently without extensive technical expertise.  Ready to elevate your underwriting process? Contact us today to learn more about our suite of advanced analytics solutions or hear what our experts have to say in this webinar.  Watch Webinar Learn More This article includes content created by an AI language model and is intended to provide general information. 

Experian’s ninth annual report on identity and fraud highlights persistent worries among consumers and businesses about fraud, including growing threats from GenAI. In this report, we explore how the evolving fraud landscape is impacting identity verification, customer experience, and business priorities for the future. Our 2024 U.S. Identity and Fraud Report draws insights from surveys of over 2,000 U.S. consumers and 200 businesses. This year’s report dives into: Evolving consumer sentiment over security and experience Businesses’ investments to tackle growing fraud challenges Effective technology solutions to accurately identify and authenticate consumers The impact of GenAI on the fraud landscape   To keep pace with the evolving landscape, businesses will need to apply a multi-faceted strategy that leverages multiple types of recognition and security to stop all types of fraud while allowing real customers through. To learn more about our findings and perspective, read the full 2024 U.S. Identity and Fraud Report, watch our on-demand webinar, or read the press release. Download Now Watch Webinar Read Press Release

In this article...Rise of AI in fraudulent activitiesFighting AI with AI Addressing fraud threatsBenefits of leveraging AI fraud detectionFinancial services use caseExperian's AI fraud detection solutions In a world where technology evolves at lightning speed, fraudsters are becoming more sophisticated in their methods, leveraging advancements in artificial intelligence (AI). According to our 2024 U.S. Identity and Fraud Report, 70% of businesses expect AI fraud to be their second-greatest challenge over the next two to three years. To combat emerging fraud threats, organizations are turning to AI fraud detection to stay ahead and protect their businesses and their customers, essentially fighting AI with AI. This blog post explores the evolving AI fraud and AI fraud detection landscape. The rise of AI in fraudulent activities Technology is a double-edged sword. While it brings numerous advancements, it also provides fraudsters with new tools to exploit. AI is no exception. Here are some ways fraudsters are utilizing AI: Automated attacks: Fraudsters employ AI to design automated scripts that launch large-scale attacks on systems. These scripts can perform credential stuffing, where stolen usernames and passwords are automatically tested across multiple sites to gain unauthorized access. Deepfakes and synthetic identities: Deepfake technology and the creation of synthetic identities are becoming more prevalent, as we predicted in our 2024 Future of Fraud Forecast. Fraudsters use AI to manipulate videos and audio, making it possible to impersonate individuals convincingly. Similarly, synthetic identities blend real and fake information to create false personas. Phishing and social engineering: AI-driven phishing attacks are more personalized and convincing than traditional methods. By analyzing social media profiles and other online data, fraudsters craft tailored messages that trick individuals into revealing sensitive information. Watch now: Our 2024 Future of Fraud Forecast: Gen AI and Emerging Trends webinar explores five of our fraud predictions for the year. Fighting AI with AI in fraud detection To combat these sophisticated threats, businesses must adopt equally advanced measures. AI fraud detection offers a robust solution: Machine learning algorithms: Fraud detection machine learning algorithms analyze vast datasets to identify patterns and anomalies that indicate fraudulent behavior. These algorithms can continuously learn and adapt, improving their accuracy over time. Real-time monitoring: AI systems provide real-time monitoring of transactions and activities. This allows businesses to detect and respond to fraud attempts instantly, minimizing potential damage. Predictive analytics: Predictive analytics uses historical data to forecast future fraud trends. By anticipating potential threats, organizations can take proactive measures to safeguard their assets. Addressing fraud threats with AI fraud detection AI's versatility allows it to tackle various types of fraud effectively: Identity theft: 84% of consumers rank identity theft as their top online concern.* AI systems can help safeguard consumers by cross-referencing multiple data points to verify identities. They can spot inconsistencies that indicate identity theft, such as mismatched addresses or unusual login locations. Payment fraud: Coming in second to identity theft, 80% of consumers rank stolen credit card information as their top online concern.* Payment fraud includes unauthorized credit card transactions and chargebacks. AI can be used in payment fraud detection to surface unusual spending patterns and flag suspicious transactions for further investigation. Account takeover: Account takeover fraud, the topmost encountered fraud event reported by U.S. businesses in 2023, occurs when fraudsters gain access to user accounts and conduct unauthorized activities.* AI identifies unusual login behaviors and implements additional security measures to prevent account breaches. Synthetic identity fraud: Synthetic identity fraud involves the creation of fake identities using real and fabricated information. Notably, retail banks cite synthetic identity fraud as the operational challenge putting the most stress on their business.* AI fraud solutions detect these false identities by analyzing data inconsistencies and behavioral patterns. Benefits of leveraging AI fraud detection Implementing AI fraud detection offers numerous advantages: Enhanced accuracy: AI systems are highly accurate in identifying fraudulent activities. Their ability to analyze large datasets and detect subtle anomalies surpasses traditional methods. Cost savings: By preventing fraud losses, AI systems save businesses significant amounts of money. They also reduce the need for manual investigations, freeing up resources for other tasks. Improved customer experience: AI fraud detection minimizes false positives, ensuring genuine customers face minimal friction. This enhances the overall customer experience and builds trust in the organization. Scalability: AI systems can handle large volumes of data, making them suitable for organizations of all sizes. Whether you're a small business or a large enterprise, AI can scale to meet your needs. Financial services use case The financial sector is particularly vulnerable to fraud, making AI an invaluable tool for fraud detection in banking. Protecting transactions: Banks use AI to monitor transactions for signs of fraud. Machine learning algorithms analyze transaction data in real time, flagging suspicious activities for further review. Enhancing security: AI enhances security by implementing multifactor authentication and behavioral analytics. These measures make it more challenging for fraudsters to gain unauthorized access. Reducing fraud losses: By detecting and preventing fraudulent activities, AI helps banks reduce their fraud losses throughout the customer lifecycle. This not only saves money but also protects the institution's reputation. Experian's AI fraud detection solutions AI fraud detection is revolutionizing the way organizations combat fraud. Its ability to analyze vast amounts of data, detect anomalies, and adapt to new threats makes it an essential element of any comprehensive fraud strategy. Experian’s range of AI fraud detection solutions help organizations enhance their security measures, reduce fraud losses, authenticate identity with confidence, and improve the overall customer experience. If you're interested in learning more about how AI can protect your business, explore our fraud management solutions or contact us today. Learn More *Source: Experian. 2024 U.S. Identity and Fraud Report. This article includes content created by an AI language model and is intended to provide general information. 

In this article...Recent trends in credit card debtThe rising tide of delinquenciesWhat is credit limit optimization?Benefits of credit limit optimizationEconomic indicators and CLO ImpactEnhanced profitability and risk mitigation This post was originally published on our Global Insights Blog. As credit card issuers grow, the size of their customer base expands, bringing both opportunities and challenges. One of the most critical challenges is managing growth while controlling default rates. Credit limit optimization (CLO) has emerged as a vital tool for banks and credit lenders to achieve this balance. By leveraging machine learning models and mathematical optimization, CLO enables lenders to tailor credit limits to individual customers, enhancing profitability while mitigating risk. Recent trends in credit card debt To understand the significance of CLO, it is essential to consider the current economic landscape. The first quarter of 2024 saw total household debt in the U.S. rise by $184 billion, reaching $17.69 trillion. While credit card balances declined slightly (a reflection of seasonal factors and consumer spending patterns), they remain a substantial component of household liabilities, with total credit card debt standing at approximately $1.26 trillion in early 2024. On average, American households hold around $10,479 in credit card debt, which is down from previous years but still significant. The average APR for credit cards in the first quarter of 2024 was 21.59%.* The rising tide of delinquencies In the first quarter of 2024, about 8.9% (annualized) of credit card balances transitioned into delinquency. This trend underscores the need for credit card issuers to adopt more sophisticated methods to assess credit risk and adjust credit limits accordingly. The rising rate of credit card delinquencies is a key driver behind the adoption of CLO strategies. What is credit limit optimization? Credit limit optimization uses advanced analytics to assess individual customers’ creditworthiness. By analyzing various data points, including payment history, income levels, spending patterns, and economic indicators, these tools can recommend optimal credit limits that maximize customer spending potential while minimizing the risk of default, all within the constraints set by the business in terms of its appetite for risk and capacity. For instance, a customer with a strong payment history and stable income might receive a higher credit limit, encouraging more spending and enhancing the lender’s revenue through interest and interchange fees. Conversely, customers showing signs of financial stress might see their credit limit reduced to prevent them from accumulating unmanageable debt. Benefits of credit limit optimization Improved profitability – By setting credit limits reflecting customers’ credit risk and spending potential, lenders can increase their revenue through higher interest and fee income. Reduced default rates – Lenders can significantly reduce the incidence of bad debt by identifying customers at risk of default and adjusting their credit limits accordingly. Improved customer satisfaction – Personalized credit limits can improve customer satisfaction, as customers are more likely to receive credit that matches their needs and financial situation. Regulatory compliance – CLO can help lenders comply with regulatory requirements by ensuring that credit limits are set based on objective, data-driven criteria. Economic indicators and CLO Impact Several economic indicators provide context for the importance of CLO in the current market. For instance, the Federal Reserve reported that in 2023, fewer than half of adult credit cardholders carried a balance on their cards, down from previous years. This indicates a more cautious approach to credit use among consumers, likely influenced by economic uncertainty and rising interest rates. Moreover, the disparity in credit card debt across different states highlights the varying economic conditions and the need for tailored credit strategies. States like New Jersey have some of the highest average credit card debts, while states like Mississippi have the lowest. This regional variation underscores lenders’ need to adopt flexible, data-driven approaches to credit limit setting. Enhanced profitability and risk mitigation Credit limit optimization is critical for credit card issuers aiming to balance growth and risk management. As economic conditions evolve and consumer behaviors shift, the ability to set personalized credit limits will become increasingly important. By leveraging advanced analytics and machine learning, CLO enhances profitability and contributes to a more stable and resilient financial system. One such solution is Experian’s Ascend Intelligence Services™ Limit, which provides an optimized strategy designed to enhance the precision and effectiveness of credit limit assignments. Ascend Intelligence Services™ Limit combines best-in-class bureau data with machine learning to simulate the impact of different credit limits in real time. This capability allows lenders to quickly test and refine their credit limit strategies without the lengthy trial-and-error period traditionally required. Ascend Intelligence Services Limit enables lenders to set credit limits that align with their business objectives and risk tolerance. By providing insights into the likelihood of default and potential revenue for each credit limit scenario, Ascend Intelligence Services Limit helps design optimal limit strategies. This not only maximizes revenue but also minimizes the risk of defaults by ensuring credit limits are appropriate for each customer’s financial situation. In a landscape marked by rising delinquencies and varying regional debt levels, the strategic use of CLO like Ascend Intelligence Services Limit represents a forward-thinking approach to credit management, benefiting both lenders and consumers. Learn More * HOUSEHOLD DEBT AND CREDIT REPORT (Q1 2024) – Federal Reserve Bank of New York

In this article...What is credit card fraud?Types of credit card fraudWhat is credit card fraud prevention and detection?How Experian® can help with card fraud prevention and detection With debit and credit card transactions becoming more prevalent than cash payments in today’s digital-first world, card fraud has become a significant concern for organizations. Widespread usage has created ample opportunities for cybercriminals to engage in credit card fraud. As a result, millions of Americans fall victim to credit card fraud annually, with 52 million cases reported last year alone.1 Preventing and detecting credit card fraud can save organizations from costly losses and protect their customers and reputations. This article provides an overview of credit card fraud detection, focusing on the current trends, types of fraud, and detection and prevention solutions. What is credit card fraud? Credit card fraud involves the unauthorized use of a credit card to obtain goods, services or funds. It's a crime that affects individuals and businesses alike, leading to financial losses and compromised personal information. Understanding the various forms of credit card fraud is essential for developing effective prevention strategies. Types of credit card fraud Understanding the different types of credit card fraud can help in developing targeted prevention strategies. Common types of credit card fraud include: Card not present fraud occurs when the physical card is not present during the transaction, commonly seen in online or over-the-phone purchases. In 2023, card not present fraud was estimated to account for $9.49 billion in losses.2 Account takeover fraud involves fraudsters gaining access to a victim's account to make unauthorized transactions. In 2023, account takeover attacks increased 354% year-over-year, resulting in almost $13 billion in losses.3,4 Card skimming, which is estimated to cost consumers and financial institutions over $1 billion per year, occurs when fraudsters use devices to capture card information from ATMs or point-of-sale terminals.5 Phishing scams trick victims into providing their card information through fake emails, texts or websites. What is credit card fraud prevention and detection? To combat the rise in credit card fraud effectively, organizations must implement credit card fraud prevention strategies that involve a combination of solutions and technologies designed to identify and stop fraudulent activities. Effective fraud prevention solutions can help businesses minimize losses and protect their customers' information. Common credit card fraud prevention and detection methods include: Fraud monitoring systems: Banks and financial institutions employ sophisticated algorithms and artificial intelligence to monitor transactions in real time. These systems analyze spending patterns, locations, transaction amounts, and other variables to detect suspicious activity. EMV chip technology: EMV (Europay, Mastercard, and Visa) chip cards contain embedded microchips that generate unique transaction codes for each purchase. This makes it more difficult for fraudsters to create counterfeit cards. Tokenization: Tokenization replaces sensitive card information with a unique identifier or token. This token can be used for transactions without exposing actual card details, reducing the risk of fraud if data is intercepted. Multifactor authentication (MFA): Adding an extra layer of security beyond the card number and PIN, MFA requires additional verification such as a one-time code sent to a mobile device, knowledge-based authentication or biometric/document confirmation. Transaction alerts: Many banks offer alerts via SMS or email for every credit card transaction. This allows cardholders to spot unauthorized transactions quickly and report them to their bank. Card verification value (CVV): CVV codes, typically three-digit numbers printed on the back of cards (four digits for American Express), are used to verify that the person making an online or telephone purchase physically possesses the card. Machine learning and AI: Advanced algorithms can analyze large datasets to detect unusual patterns that may indicate fraud, such as sudden large transactions or purchases made in different geographic locations within a short time frame. Advanced algorithms can analyze large datasets to detect unusual patterns that may indicate fraud, such as sudden large transactions or purchases made in different geographic locations within a short time frame. Behavioral analytics: Monitoring user behavior to detect anomalies that may indicate fraud. Education and awareness: Educating consumers about phishing scams, identity theft, and safe online shopping practices can help reduce the likelihood of falling victim to credit card fraud. Fraud investigation units: Financial institutions have teams dedicated to investigating suspicious transactions reported by customers. These units work to confirm fraud, mitigate losses, and prevent future incidents. How Experian® can help with card fraud prevention and detection Credit card fraud detection is essential for protecting businesses and customers. By implementing advanced detection technologies, businesses can create a robust defense against fraudsters. Experian® offers advanced fraud management solutions that leverage identity protection, machine learning, and advanced analytics. Partnering with Experian can provide your business with: Comprehensive fraud management solutions: Experian’s fraud management solutions provide a robust suite of tools to prevent, detect and manage fraud risk and identity verification effectively.  Account takeover prevention: Experian uses sophisticated analytics and enhanced decision-making capabilities to help businesses drive successful transactions by monitoring identity and flagging unusual activities. Identifying card not present fraud: Experian offers tools specifically designed to detect and prevent card not present fraud, ensuring secure online transactions.  Take your fraud prevention strategies to the next level with Experian's comprehensive solutions. Explore more about how Experian can help. Learn More Sources 1 https://www.security.org/digital-safety/credit-card-fraud-report/ 2 https://www.emarketer.com/chart/258923/us-total-card-not-present-cnp-fraud-loss-2019-2024-billions-change-of-total-card-payment-fraud-loss 3 https://pages.sift.com/rs/526-PCC-974/images/Sift-2023-Q3-Index-Report_ATO.pdf 4 https://www.aarp.org/money/scams-fraud/info-2024/identity-fraud-report.html 5 https://www.fbi.gov/how-we-can-help-you/scams-and-safety/common-scams-and-crimes/skimming This article includes content created by an AI language model and is intended to provide general information. 

In this article...What is fair lending?Understanding machine learning modelsThe pitfalls: bias and fairness in ML modelsFairness metricsRegulatory frameworks and complianceHow Experian® can help As the financial sector continues to embrace technological innovations, machine learning models are becoming indispensable tools for credit decisioning. These models offer enhanced efficiency and predictive power, but they also introduce new challenges. These challenges particularly concern fairness and bias, as complex machine learning models can be difficult to explain. Understanding how to ensure fair lending practices while leveraging machine learning models is crucial for organizations committed to ethical and compliant operations. What is fair lending? Fair lending is a cornerstone of ethical financial practices, prohibiting discrimination based on race, color, national origin, religion, sex, familial status, age, disability, or public assistance status during the lending process. This principle is enshrined in regulations such as the Equal Credit Opportunity Act (ECOA) and the Fair Housing Act (FHA). Overall, fair lending is essential for promoting economic opportunity, preventing discrimination, and fostering financial inclusion. Key components of fair lending include: Equal treatment: Lenders must treat all applicants fairly and consistently throughout the lending process, regardless of their personal characteristics. This means evaluating applicants based on their creditworthiness and financial qualifications rather than discriminatory factors. Non-discrimination: Lenders are prohibited from discriminating against individuals or businesses on the basis of race, color, religion, national origin, sex, marital status, age, or other protected characteristics. Discriminatory practices include redlining (denying credit to applicants based on their location) and steering (channeling applicants into less favorable loan products based on discriminatory factors). Fair credit practices: Lenders must adhere to fair and transparent credit practices, such as providing clear information about loan terms and conditions, offering reasonable interest rates, and ensuring that borrowers have the ability to repay their loans. Compliance: Financial institutions are required to comply with fair lending laws and regulations, which are enforced by government agencies such as the Consumer Financial Protection Bureau (CFPB) in the United States. Compliance efforts include conducting fair lending risk assessments, monitoring lending practices for potential discrimination, and implementing policies and procedures to prevent unfair treatment. Model governance: Financial institutions should establish robust governance frameworks to oversee the development, implementation and monitoring of lending models and algorithms. This includes ensuring that models are fair, transparent, and free from biases that could lead to discriminatory outcomes. Data integrity and privacy: Lenders must ensure the accuracy, completeness, and integrity of the data used in lending decisions, including traditional credit and alternative credit data. They should also uphold borrowers’ privacy rights and adhere to data protection regulations when collecting, storing, and using personal information. Understanding machine learning models and their application in lending Machine learning in lending has revolutionized how financial institutions assess creditworthiness and manage risk. By analyzing vast amounts of data, machine learning models can identify patterns and trends that traditional methods might overlook, thereby enabling more accurate and efficient lending decisions. However, with these advancements come new challenges, particularly in the realms of model risk management and financial regulatory compliance. The complexity of machine learning models requires rigorous evaluation to ensure fair lending. Let’s explore why. The pitfalls: bias and fairness in machine learning lending models Despite their advantages, machine learning models can inadvertently introduce or perpetuate biases, especially when trained on historical data that reflects past prejudices. One of the primary concerns with machine learning models is their potential lack of transparency, often referred to as the "black box" problem. Model explainability aims to address this by providing clear and understandable explanations of how models make decisions. This transparency is crucial for building trust with consumers and regulators and for ensuring that lending practices are fair and non-discriminatory. Fairness metrics Key metrics used to evaluate fairness in models can include standardized mean difference (SMD), information value (IV), and disparate impact (DI). Each of these metrics offers insights into potential biases but also has limitations. Standardized mean difference (SMD). SMD quantifies the difference between two groups' score averages, divided by the pooled standard deviation. However, this metric may not fully capture the nuances of fairness when used in isolation. Information value (IV). IV compares distributions between control and protected groups across score bins. While useful, IV can sometimes mask deeper biases present in the data. Disparate impact (DI). DI, or the adverse impact ratio (AIR), measures the ratio of approval rates between protected and control classes. Although DI is widely used, it can oversimplify the complex interplay of factors influencing credit decisions. Regulatory frameworks and compliance in fair lending Ensuring compliance with fair lending regulations involves more than just implementing fairness metrics. It requires a comprehensive end-to-end approach, including regular audits, transparent reporting, and continuous monitoring and governance of machine learning models. Financial institutions must be vigilant in aligning their practices with regulatory standards to avoid legal repercussions and maintain ethical standards. Read more: Journey of a machine learning model How Experian® can help By remaining committed to regulatory compliance and fair lending practices, organizations can balance technological advancements with ethical responsibility. Partnering with Experian gives organizations a unique advantage in the rapidly evolving landscape of AI and machine learning in lending. As an industry leader, Experian offers state-of-the-art analytics and machine learning solutions that are designed to drive efficiency and accuracy in lending decisions while ensuring compliance with regulatory standards. Our expertise in model risk management and machine learning model governance empowers lenders to deploy robust and transparent models, mitigating potential biases and aligning with fair lending practices. When it comes to machine learning model explainability, Experian’s clear and proven methodology assesses the relative contribution and level of influence of each variable to the overall score — enabling organizations to demonstrate transparency and fair treatment to auditors, regulators, and customers. Interested in learning more about ensuring fair lending practices in your machine learning models?    Learn More This article includes content created by an AI language model and is intended to provide general information.

Experian’s award-winning platform now brings together market-leading data, generative AI and cutting-edge machine learning solutions for analytics, credit decisioning and fraud into a single interface — simplifying the deployment of analytical models and enabling businesses to optimize their practices. The platform updates represent a notable milestone, fueled by Experian’s significant investments in innovation over the last eight years as part of its modern cloud transformation.  “The evolution of our platform reaffirms our commitment to drive innovation and empower businesses to thrive. Its capabilities are unmatched and represent a significant leap forward in lending technology, democratizing access to data in compliant ways while enabling lenders of all sizes to seamlessly validate their customers’ identities with confidence, help expand fair access to credit and offer awesome user and customer experiences,” said Alex Lintner CEO Experian Software Solutions. The enhanced Experian Ascend Platform dramatically reduces time to install and offers streamlined access to many of Experian's award-winning integrated solutions and tools through a single sign-on and a user-friendly dashboard. Leveraging generative AI, the platform makes it easy for organizations of varying sizes and experience levels to pivot between applications, automate processes, modernize operations and drive efficiency. In addition, existing clients can easily add new capabilities through the platform to enhance business outcomes. Read Press Release Learn More Check out Experian Ascend Platform in the media: Transforming Software for Credit, Fraud and Analytics with Experian Ascend Platform™ (Episode 160) Reshaping the Future of Financial Services with Experian Ascend Platform Introducing Experian’s Cloud-based Ascend Technology Platform with GenAI Integration 7 enhancements of Experian Ascend Platform