Search Results for: synthetic id

Despite rising concerns about identity theft, most Americans aren’t taking basic steps to make it harder for their information to be stolen, according to a survey Experian conducted in August 2017: Nearly 3 in 4 consumers said they’re very or somewhat concerned their email, financial accounts or social media information could be hacked. This is up from 69% in a similar survey Experian conducted in 2015. Nearly 80% of survey respondents are concerned about using a public Wi-Fi network. Yet, barely half said they take the precaution of using a password-protected Wi-Fi network when using mobile devices. 59% of respondents are annoyed by safety precautions needed to use technology — up 12% from 2015. When your customer’s identity is stolen, it can negatively impact the consumer and your business. Leverage the tools and resources that can help you protect both. Protect your customers and your business>

Customer Experience during the holiday shopping season During the holidays, consumers transact at a much greater rate than any other time of the year. Many risk-management departments respond by loosening the reins on their decision engines to improve the customer experience — and to ensure that this spike does not trigger a response that would impede a holiday shopper’s desire to grab one more stocking stuffer or a gift for a last-minute guest. As a result, it also is the busy season for fraudsters, and they use this act of goodwill toward your customers to improve their criminal enterprise. Ultimately, you are tasked with providing a great customer experience to your real customers while eliminating any synthetic ones. Recent data breaches resulted in large quantities of personally identifiable information that thieves can use to create synthetic identities being published on the Dark Web. As this data is related to real consumers, it can be difficult for your identity-authentication solution to determine that these identities have been compromised or fabricated, enabling fraudsters to open accounts with your organization. Experian’s Identity Element Network™ can help you determine when synthetic identities are at work within your business. It evaluates nearly 300 data-element combinations to determine if certain elements appear in cyberspace frequently or are being used in combination with data not consistent with your customer’s identity. This proven resource helps you manage fraud across the Customer Life Cycle and hinder the damage that identity thieves cause. Identity Element Network examines a vast attribute repository that grows by more than 2 million transactions each day, revealing up-to-date fraud threats associated with inconsistent or high-risk use of personal identity elements. Our goal is to provide the comfort of knowing that you are transacting with your real customers. Don’t get left in the cold this holiday season — fraudsters are looking for opportunities to take advantage of you and your customers. Contact your Experian account executive to learn how Identity Element Network can help make sure you are not letting fraudsters exploit the customer experience intended for your real customers. Learn more about the delicate balance between customer and criminal by viewing our fraud e-book.

Experian recently contributed to a TSYS whitepaper focused on the various threats associated with first party fraud. I think the paper does a good job at summarizing the problem, and points out some very important strategies that can be employed to help both prevent first party fraud losses and detect those already in an institution’s active and collections account populations. I’d urge you to have a look at this paper as you begin asking the right questions within your own organization. Watch here The bad news is that first party fraud may currently account for up to 20 percent of credit charge-offs. The good news is that scoring models (using a combination of credit attributes and identity element analysis) targeted at various first party fraud schemes such as Bust Out, Never Pay, and even Synthetic Identity are quite effective in all phases of the customer lifecycle. Appropriate implementation of these models, usually involving coordinated decisioning strategies across both fraud and credit policies, can stem many losses either at account acquisition, or at least early enough in an account management stage, to substantially reduce average fraud balances. The key is to prevent these accounts from ending up in collections queues where they’ll never have any chance of actually being collected upon. A traditional customer information program and identity theft prevention program (associated, for example with the Red Flags Rule) will often fail to identify first party fraud, as these are founded in identity element verification and validation, checks that often ‘pass’ when applied to first party fraudsters.

As we step into 2025, the convergence of credit and fraud risk has become more pronounced than ever. With fraudsters leveraging emerging technologies and adapting rapidly to new defenses, risk managers need to adopt forward-thinking strategies to protect their organizations and customers. Here are the top fraud trends and actionable resolutions to help you stay ahead of the curve this year. 1. Combat synthetic identity fraud with advanced AI models The trend: Synthetic identity fraud is surging, fueled by data breaches and advanced AI tooling. Fraudsters are combining genuine credentials with fabricated details, creating identities that evade traditional detection methods. Resolution: Invest in sophisticated identity validation tools that leverage advanced AI models. These tools can differentiate between legitimate and fraudulent identities, ensuring faster and more accurate creditworthiness assessments. Focus on integrating these solutions seamlessly into your customer onboarding process to enhance both security and user experience. 2. Strengthen authentication against deepfakes The trend: Deepfake technology is putting immense pressure on existing authentication systems, particularly in high-value transactions and account takeovers. Resolution: Adopt a multilayered authentication strategy that combines voice and facial biometrics with ongoing transaction monitoring. Dynamic authentication methods that evolve based on user behavior and fraud patterns can effectively counter these advanced threats. Invest in solutions that ensure digital interactions remain secure without compromising convenience. 3. Enhance detection of payment scams and APP fraud The trend: Authorized Push Payment (APP) fraud and scams are increasingly difficult to detect because they exploit legitimate customer behaviors. Resolution: Collaborate with industry peers and explore centralized consortia to share insights and develop robust detection strategies. Focus on monitoring both inbound and outbound transactions to identify anomalies, particularly payments to mule accounts. 4. Optimize Your Fraud Stack for Efficiency and Effectiveness The trend: Outdated device and network solutions are no match for GenAI-enhanced fraud tactics. Resolution: Deploy a layered fraud stack with persistent device ID technology, behavioral analytics, and GenAI-driven anomaly detection. Begin with frictionless first-tier tools to filter out low-hanging fraud vectors, reserving more advanced and costly tools for sophisticated threats. Regularly review and refine your stack to ensure it adapts to evolving fraud patterns. 5. Build collaborative relationships with fraud solution vendors The trend: Vendors offer unparalleled industry insights and long-tail data to help organizations prepare for emerging fraud trends. Resolution: Engage in reciprocal knowledge-sharing with your vendors. Leverage advisory boards and industry insights to stay informed about the latest attack vectors. Choose vendors who provide transparency and are invested in your fraud mitigation goals, turning product relationships into strategic partnerships. Turning resolutions into reality Fraudsters are becoming more ingenious, leveraging GenAI and other technologies to exploit vulnerabilities. To stay ahead of fraud in 2025, let us make fraud prevention not just a resolution but a commitment to safeguarding trust and security in a rapidly evolving landscape. Learn more

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

Today’s fast-paced, digital-first hiring environment calls for a more comprehensive approach to pre-employment screening. With growing pressure on employers and HR teams to make swift, accurate, and secure hiring decisions, having access to the tools and data to enhance efficiency and security is more important than ever. By evolving beyond traditional screening methods, background screeners can better meet these needs and deliver added value to their clients.  Fraud remains a significant challenge. In fact, fraud scams resulted in a staggering $485.6 billion in losses in 20231 — and hiring teams aren’t exempt from these risks. Fraudulent resumes, synthetic identities, and the risk of non-compliance with evolving regulations create a challenging landscape for pre-employment verifications. What if there was a way to make smarter, faster, and more secure hiring decisions? This article explores how background screeners can optimize pre-employment verification processes, reduce fraud risks, and ensure compliance — all while delivering a positive candidate experience. What is pre-employment screening? Employers conduct pre-employment screenings to thoroughly evaluate job candidates and make informed hiring decisions. It’s designed to verify key details about candidates, such as their identity, employment history, and references among others to assess their suitability for a role and ensure compliance with industry regulations. Enhancing traditional screening processes For decades, pre-employment background checks have been a cornerstone of the hiring process. While effective, many traditional methods face challenges in keeping up with the evolving demands of modern hiring. Delays in hiring: Background checks can oftentimes rely on manual processes, which could extend timelines leading to delays of days or even weeks. This not only slows down hiring cycles but can make it harder for employers to compete for top talent in a tight labor market. Errors and inaccuracies: Human errors, incomplete data, and inconsistencies across systems can lead to missed insights or red flags. Fraudulent activity: As hiring becomes increasingly digital, identity theft and synthetic identities present growing challenges to verifying candidate-provided data.  Regulatory challenges: With regulations like the Equal Employment Opportunity Commission (EEOC) and Fair Credit Reporting Act (FCRA), companies must navigate complex compliance requirements to avoid legal and financial repercussions. 1 in 3 HR professionals report losing top candidates due to slow pre-employment screening processes.2 These challenges highlight the opportunity to build on existing screening practices with tools that enhance speed, provide actionable insights and prevent fraud. Adapting to the evolving fraud landscape Employment fraud is becoming increasingly sophisticated, fueled by trends like the rise of remote work and digital applications. In fact, the employment sector accounted for 45% of all false document submissions in 2023, making it the most targeted industry for fraud.3 From fake references and degrees to synthetic identities created using stolen personal information, the risks are higher than ever. Synthetic identity fraud: This form of fraud — where fake identities are created by combining real and fabricated data — makes up more than 80% of all new account fraud.4 Fake credentials: Many candidates falsify qualifications or work histories to enhance their chances of securing a role. Compliance risks: Failure to verify candidate information accurately can result in legal penalties, brand reputation damage, or internal security breaches. Modernizing pre-employment screening The good news? Experian offers advanced solutions that complement existing screening processes, empowering background screeners to deliver more efficient, secure and reliable results for their clients looking to higher faster, and with greater confidence.  Gain a more holistic view of a candidate’s risk profile: Experian’s nationwide database contains files on more than 245 million credit-active consumers, providing the most current, accurate, and comprehensive information available in the industry. Conduct real-time identity verification: Leverage a range of identity verification solutions to authenticate and verify a candidate’s identity by accessing a breadth set of non-credit and credit data sources to create a robust social footprint that defines each consumer as unique individuals. Integrate advanced fraud detection: Powered by purpose-built analytics and machine learning algorithms, Experian’s fraud detection tools can detect synthetic identities, inconsistencies, and other red flags while ensuring a seamless candidate experience. Enhance compliance efforts: Experian’s solutions are designed to help businesses navigate complex compliance requirements with ease. Fraud prevention playbook in preemployment Uncover essential strategies for fraud prevention and identity verification in employment screening. Download now The pre-employment screening landscape is evolving, and staying ahead requires tools that enhance the efficiency and effectiveness of your processes. Experian’s advanced solutions are designed to complement your existing screening services, helping you reduce fraud risks, maintain compliant, and deliver data-driven insights that empower smarter hiring decisions. Get started today Ready to transform your pre-employment verification process with fraud mitigation and identity verification solutions? Explore our innovative solutions today. Learn more 1 Nasdaq finds scams led to $486 billion in losses in 2023, 2024. 2 Research reveals Candidates’ Frustrations with Hiring Process, 2024. 3 Employment Identity Fraud: Do You Know Who You’re Hiring, 2024. 4 Report: Synthetic identity fraud is growing, 2024.

The digital domain is rife with opportunities, but it also brings substantial risks, especially for organizations. Among the innovative tools that have risen to prominence for fraud detection and online security is browser fingerprinting. Whether you're looking to minimize security gaps or bolster your fraud prevention strategy, understanding how this technology works can provide a significant advantage in today’s ever-evolving fraud and identity landscape. This article explores the concept, functionality, and applications of browser fingerprinting while also examining its benefits and relevance for organizations. How does browser fingerprinting work? Browser fingerprinting is a powerful technology designed to collect unique identifying information about a user’s web browser and device. By compiling data points such as browser type, operating system, time zone, and installed plugins, browser fingerprinting creates a distinct profile — or "fingerprint"— that allows websites to recognize returning users without relying on cookies. Here’s a breakdown of its key steps: Data collection: When a user visits a website, their browser sends information, such as user-agent strings or metadata, to the website's servers. This data provides insights about their browser, device, and system. Fingerprint creation: The collected information is processed to generate a unique ID or fingerprint, representing the user's specific configuration. Tracking and analyzing: These fingerprints enable websites to track and analyze user behavior, detect anomalies, and identify users without relying on traditional tracking mechanisms like cookies. For organizations, employing technology that leverages such fingerprints adds an additional layer to identity verification, detecting discrepancies that may indicate fraud attempts. What are the different techniques? Not all browser fingerprinting methods are identical; varying approaches offer different strengths. The most common techniques used today include: Canvas fingerprinting: This method utilizes the "Canvas" element in HTML5. When a website sends a command to draw a hidden image on a user's device, the way the image is rendered reveals unique characteristics about the device's graphics hardware and software. Font fingerprinting: Font fingerprinting involves analyzing the fonts installed on a user's system. Since computers and browsers render text in slightly different ways based on their configurations, the resulting variations aid in identifying users. Plugin enumeration: Browsers and devices often come equipped with plugins or extensions like Flash or Java. Analyzing which plugins are installed, their versions, and their order helps websites build unique fingerprints. What are the benefits of browser fingerprinting? For organizations, browser fingerprinting is not just a technical marvel — it’s a strategic asset. Benefits include: Enhanced fraud detection: Browser fingerprinting detects inconsistencies within user accounts, flagging unauthorized logins, synthetic identity fraud, or account takeover fraud without introducing significant friction for legitimate users. By identifying patterns that deviate from the norm, organizations can better prepare for malicious activities. Learn more about addressing account takeover fraud. Supports multi-layered security: A single security measure often isn't enough to combat advanced fraudulent schemes. Browser fingerprinting pairs seamlessly with other fraud management tools, such as behavioral analytics and risk-based authentication, to provide robust security. See how behavioral analytics can help organizations spot and stop next-generation fraud bots. Seamless user experience: Unlike cookies or authentication codes, browser fingerprinting operates passively in the background. Users remain unaware of the process, ensuring their experience is unaffected while still maintaining security. Level up with Experian's fraud prevention tools Browser fingerprinting offers organizations a game-changing tool to secure online interactions. However, given the growing complexity of fraud threats, organizations will need additional layers of insights and protection. Experian offers integrated, AI-driven fraud prevention solutions tailor-made to tackle challenges in the digital space. By leveraging advanced technologies like browser fingerprinting alongside Experian’s solutions, organizations can safeguard their operations and uphold customer trust while maintaining a frictionless user experience. Learn more about our fraud prevention solutions This article includes content created by an AI language model and is intended to provide general information.

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

U.S. federal prosecutors have indicted Michael Smith of North Carolina for allegedly orchestrating a $10 million fraud scheme involving AI-generated music. Smith is accused of creating fake bands and using AI tools to produce hundreds of tracks, which were streamed by fake listeners on platforms like Spotify, Apple Music, and Amazon Music. Despite the artificial engagement, the scheme generated real royalty payments, defrauding these streaming services. This case marks the first prosecution of its kind and highlights a growing financial risk: the potential for rapid, large-scale fraud in digital platforms when content and engagement can be easily fabricated. A new report from Imperva Inc. highlights the growing financial burden of unsecure APIs and bot attacks on businesses, costing up to $186 billion annually. Key findings highlight the heavy economic burden on large companies due to their complex and extensive API ecosystems, often unsecured. Last year, enterprises managed about 613 API endpoints on average, a number expected to grow, increasing associated risks. APIs exposure to bot attacks Bot attacks, similar to those seen in streaming fraud, are also plaguing financial institutions. The risks are significant, weakening both security and financial stability. 1. Fraudulent transactions and account takeover Automated fraudulent transactions: Bots can perform high volumes of small, fraudulent transactions across multiple accounts, causing financial loss and overwhelming fraud detection systems. Account takeover: Bots can attempt credential stuffing, using compromised login data to access user accounts. Once inside, attackers could steal funds or sensitive information, leading to significant financial and reputational damage. 2. Synthetic identity fraud Creating fake accounts: Bots can be used to generate large numbers of synthetic identities, which are then used to open fake accounts for money laundering, credit fraud, or other illicit activities. Loan or credit card fraud: Using fake identities, bots can apply for loans or credit cards, withdrawing funds without intent to repay, resulting in significant losses for financial institutions. 3. Exploiting API vulnerabilities API abuse: Just as bots exploit API endpoints in streaming services, they can also target vulnerable APIs in financial platforms to extract sensitive data or initiate unauthorized transactions, leading to significant data breaches. Data exfiltration: Bots can use APIs to extract financial data, customer details, and transaction records, potentially leading to identity theft or data sold on the dark web. Bot attacks targeting financial institutions can result in extensive fraud, data breaches, regulatory fines, and loss of customer trust, causing significant financial and operational consequences. Safeguarding financial integrity To safeguard your business from these attacks, particularly via unsupervised APIs, a multi-layered defense strategy is essential. Here’s how you can protect your business and ensure its financial integrity: 1. Monitor and analyze data patterns Real-time analytics: Implement sophisticated monitoring systems to track user behavior continuously. By analyzing user patterns, you can detect irregular spikes in activity that may indicate bot-driven attacks. These anomalies should trigger alerts for immediate investigation. AI, machine learning, and geo-analysis: Leverage AI and machine learning models to spot unusual behaviors that can signal fraudulent activity. Geo-analysis tools help identify traffic originating from regions known for bot farms, allowing you to take preventive action before damage occurs. 2. Strengthen API access controls Limit access with token-based authentication: Implement token-based authentication to limit API access to verified applications and users. This reduces the chances of unauthorized or bot-driven API abuse. Control third-party integrations: Restrict API access to only trusted and vetted third-party services. Ensure that each external service is thoroughly reviewed to prevent malicious actors from exploiting your platform. 3. Implement robust account creation procedures PII identity verification solutions: Protect personal or sensitive data through authenticating someone`s identity and helping to prevent fraud and identity theft. Email and phone verification: Requiring email or phone verification during account creation can minimize the risk of mass fake account generation, a common tactic used by bots for fraudulent activities. Combating Bots as a Service: Focusing on intent-based deep behavioral analysis (IDBA), even the most sophisticated bots can be spotted, without adding friction. 4. Establish strong anti-fraud alliances Collaborate with industry networks: Join industry alliances or working groups that focus on API security and fraud prevention. Staying informed about emerging threats and sharing best practices with peers will allow you to anticipate new attack strategies. 5. Continuous customer and account monitoring Behavior analysis for repeat offenders: Monitor for repeat fraudulent behavior from the same accounts or users. If certain users or transactions display consistent signs of manipulation, flag them for detailed investigation and potential restrictions. User feedback loops: Encourage users to report any suspicious activity. This crowd-sourced intelligence can be invaluable in identifying bot activity quickly and reducing the scope of damage. 6. Maintain transparency and accountability Audit and report regularly: Offer regular, transparent reports on API usage and your anti-fraud measures. This builds trust with stakeholders and customers, as they see your proactive steps toward securing the platform. Real-time dashboards: Provide users with real-time visibility into their data streams or account activities. Unexplained spikes or dips can be flagged and investigated immediately, providing greater transparency and control. Conclusion Safeguarding your business from bot attacks and API abuse requires a comprehensive, multi-layered approach. By investing in advanced monitoring tools, enforcing strict API access controls, and fostering collaboration with anti-fraud networks, your organization can mitigate the risks posed by bots while maintaining credibility and trust. The right strategy will not only protect your business but also preserve the integrity of your platform. Learn more

In this article...Understanding the scope of fintech fraudThe importance of fintech fraud preventionSynthetic identity (ID) fraud: A growing threatHow fintech fraud detection and prevention are evolvingGet started today The integration of technology with traditional financial services has unlocked unprecedented convenience and opportunities for consumers and businesses alike. However, this digital shift has opened the door for more sophisticated fraud tactics. With fraudsters continuously refining their methods, fintech companies must invest in advanced fintech fraud detection and prevention solutions. Understanding the scope of fintech fraud As fintech platforms expand, they also attract the attention of cybercriminals. The accessibility of digital financial services can create vulnerabilities that fraudsters exploit, executing everything from personal account takeovers to larger-scale breaches involving synthetic identities.  Source: Experian’s 2024 U.S. Identity & Fraud Report To counter these threats, fintech companies must deploy innovative fraud management solutions powered by artificial intelligence (AI), machine learning (ML), and advanced analytics. Unlike traditional methods that often rely on static rules and manual reviews, these solutions can process vast amounts of data, learn from historical patterns, and detect anomalies in real-time. This allows organizations to identify suspicious activities before they lead to significant losses. The importance of fintech fraud prevention While detecting fraud is crucial, preventing it from occurring in the first place is even more important. Fraud prevention solutions aim to create robust systems that stop fraudsters in their tracks before they can cause damage. With the rise of digital financial services, the need for proactive fraud prevention measures has never been greater. These solutions protect both consumers and businesses from financial harm, reducing the risk of financial loss and reputational damage. Advanced fraud prevention solutions employ multi-layered strategies, combining AI-driven fraud detection tools with methods such as multifactor authentication and biometric identity verification. These tools create an extra layer of security, making it difficult for fraudsters to access sensitive data or execute fraudulent transactions. Experian’s fraud prevention solutions offer businesses a comprehensive suite of tools designed to prevent various types of fraud. From real-time transaction monitoring to sophisticated user authentication methods, these solutions provide the protection businesses need to stay ahead of evolving fraud tactics. Synthetic identity (ID) fraud: A growing threat One of the most concerning forms of fraud that fintech companies face is synthetic ID fraud. This type of fraud involves the creation of a fake identity using a combination of real and fabricated information. Fraudsters often steal pieces of personal data—such as Social Security numbers or addresses—and then combine them with fictional information to create a new, synthetic identity. These synthetic identities can be used to open bank accounts, apply for credit cards, or take out loans, leaving businesses and consumers vulnerable to significant financial losses. Synthetic ID fraud is particularly difficult to detect because the synthetic identity often looks legitimate to traditional verification systems. As a result, fintech companies must deploy sophisticated fraud detection systems that can identify synthetic identities before they’re used to commit fraud. Machine learning algorithms, for instance, can analyze behavioral data, detecting discrepancies that may indicate a synthetic identity. Experian is ranked #1 by the Center for Financial Professionals (CeFPro®) for Identity and Fraud. The ranking appeared in CeFPro’s Fintech Leaders Report, a comprehensive annual study of the fintech industry. How fintech fraud detection and prevention are evolving As fraudsters continue to evolve their tactics, fintech companies must remain one step ahead by investing in cutting-edge fraud detection and prevention technologies. Real-time monitoring, predictive analytics, and biometrics are just a few of the technologies shaping the future of fraud detection. By integrating these technologies into their fraud management processes, fintech companies can offer a more secure and seamless experience for their users. With the acquisition of NeuroID, an industry leader in behavioral analytics, Experian has amplified its fraud risk suite by providing a new layer of insight into digital behavioral signals and analytics. Available through our fraud solutions on the Experian Ascend Technology PlatformTM, clients can proactively monitor and analyze a user’s real-time digital behavior, allowing them to confidently navigate the online landscape and provide frictionless customer experiences. Get started today As the fraud landscape continues to evolve, fintech companies must adopt comprehensive solutions to stay ahead of emerging threats. By doing so, they can protect themselves and their customers, ensuring the continued success of digital financial services in the years to come. To learn more, check out our fraud management and fintech solutions. Fraud management solutions Fintech solutions This article includes content created by an AI language model and is intended to provide general information. In this article...

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. 

“Learn how to learn.” One of Zack Kass’, AI futurist and one of the keynote speakers at Vision 2024, takeaways readily embodies a sentiment most of us share — particularly here at Vision. Jennifer Schulz, CEO of Experian, North America, talked about AI and transformative technologies of past and present as she kicked off Vision 2024, the 40th Vision. Keynote speaker: Dr. Mohamed El-Erian Dr. Mohamed El-Erian, President of Queens’ College, Cambridge and Chief Economic Advisor at Allianz, returned to the Vision stage to discuss the labor market, “sticky” inflation and the health of consumers. He emphasized the need to embrace and learn how to talk to AI engines and that AI can facilitate content, creation, collaboration and community Keynote speaker: Zack Kass Zack Kass, AI futurist and former Head of Go-To-Market at OpenAI, spoke about the future of work and life and artificial general intelligence. He said AI is aiding in our entering of a superlinear trajectory and compared the thresholds of technology versus those of society. Sessions – Day 1 highlights The conference hall was buzzing with conversations, discussions and thought leadership. Some themes definitely rose to the top — the increasing proliferation of fraud and how to combat it without diminishing the customer experience, leveraging AI and transformative technology in decisioning and how Experian is pioneering the GenAI era in finance and technology. Transformative technologiesAI and emerging technologies are reshaping the finance sector and it's the responsibility of today's industry leaders to equip themselves with cutting-edge strategies and a comprehensive understanding to master the rapidly evolving landscape. That said, transformation is a journey and aligning with a partner that's agile and innovative is critical. Holistic fraud decisioningGenerative AI, a resurgence of bank branch transactions, synthetic identity and pig butchering are all fraud trends that today's organizations must be acutely aware of and armed to protect their businesses and customers against. Leveraging a holistic fraud decisioning strategy is important in finding the balance between customer experience and mitigating fraud. Unlocking cashflow to grow, protect and reduce riskCash flow data can be used not only across the lending lifecycle, but also as part of assessing existing portfolio opportunities. Incorporating consumer-permissioned data into models and processes powers predicatbility and can further assess risk and help score more consumers. Navigating the economyAmid a slowing economy, consumers and businesses continue to struggle with higher interest rates, tighter credit conditions and rising delinquencies, creating a challenging environment for lenders. Experian's experts outlined their latest economic forecasts and provided actionable insights into key consumer and commercial credit trends. More insights from Vision to come. Follow @ExperianVision and @ExperianInsights to see more of the action.

Financial institutions have long relied on anti-money laundering (AML) and anti-fraud systems to protect themselves and their customers. These departments and systems have historically operated in siloes, but that’s no longer best practice.  Now, a new framework that integrates fraud and AML, or FRAML, is taking hold as financial institutions see the value of sharing resources to fight fraud and other financial crimes.  You don’t need to keep them separated For fraudsters, fraud and money laundering go hand-in-hand. By definition, someone opening an account and laundering money is committing a crime. The laundered funds are also often from illegal activity — otherwise, they wouldn’t need to be laundered.  For financial institutions, different departments have historically owned AML and anti-fraud programs. In part, because AML and fraud prevention have different goals: AML is about staying compliant: AML is often owned by an organization’s compliance department, which ensures the proper processes and reporting are in place to comply with relevant regulations.  Fraud is about avoiding losses: The fraud department identifies and stops fraudulent activity to help protect the organization from reputational harm and fraud losses. As fraudsters’ operations become more complex, the traditional separation of the two departments may be doing more harm than good.  Common areas of focus There has always been some overlap in AML and fraud prevention. After all, an AML program can stop criminals from opening or using accounts that could lead to fraud losses. And fraud departments might stop suspicious activity that’s a criminal placing or layering funds. While AML and fraud both involve ongoing account monitoring, let’s take a closer look at similarities during the account creation: Verifying identities: Financial institutions’ AML programs must include know your customer (KYC) procedures and a Customer Identification Program (CIP). Being able to verify the identity of a new customer can be important for tracing transactions back to an individual or entity later. Similarly, fraud departments want to be sure there aren’t any red flags when opening a new account, such as a connection between the person or entity and previous fraudulent activity.  Preventing synthetic identity fraud: Criminals may try to use synthetic identities to avoid triggering AML or fraud checks. Synthetic identity fraud has been a growing problem, but the latest solutions and tools can help financial institutions stop synthetic identity fraud across the customer lifecycle.  Detecting money mules: Some criminals recruit money mules rather than using their own identity or creating a synthetic identity. The mules are paid to use their legitimate bank account to accept and transfer funds on behalf of the criminal. In some cases, the mule is an unwitting victim of a scam and an accomplice in money laundering.  Although the exact requirements, tools, processes, and reports for AML and fraud differ, there’s certainly one commonality — identify and stop bad actors.    Interactive infographic: Building a multilayered fraud and identity strategy The win-win of the FRAML approach Aligning AML and fraud could lead to cost savings and benefits for the organization and its customers in many ways. Save on IT costs: Fraud and AML teams may benefit from similar types of advanced analytics for detecting suspicious activity. In 2023, around 60 percent of businesses were using or trying to use machine learning (ML) in their fraud strategies, but a quarter said cost was impeding implementation.1 If fraud and AML can share IT resources and assets, they might be able to better afford the latest ML and AI solutions.  Avoid duplicate work: Cost savings can also happen if you can avoid having separate AML and fraud investigations into the same case. The diverse backgrounds and approaches to investigations may also lead to more efficient and successful outcomes.  Get a holistic view of customers: Sharing information about customers and accounts also might help you more accurately assess risk and identify fraud groups.  Improve your customer experience: Shared data can also reduce customer outreach for identity or transaction verifications. Creating a single view of each account or customer can also improve customer onboarding and account monitoring, leading to fewer false positives and a better customer experience.  Some financial institutions have implemented collaboration with the creation of a new team, sometimes called the financial crimes unit (FCU). Others may keep the departments separate but develop systems for sharing data and resources.  Watch the webinar: Fraud and identity challenges for Fintechs How Experian can help  Creating new systems and changing company culture doesn’t happen overnight, but the shift toward collaboration may be one of the big trends in AML and fraud for 2024. As a leader in identity verification and fraud prevention, Experian can offer the tools and strategies that organizations need to update their AML and fraud processes across the entire customer lifecycle.  CrossCore® is our integrated digital identity and fraud risk platform which enables organizations to connect, access, and orchestrate decisions that leverage multiple data sources and services. CrossCore cloud platform combines risk-based authentication, identity proofing and fraud detection, which enables organizations to streamline processes and quickly respond to an ever-changing environment. In its 2023 Fraud Reduction Intelligence Platforms (FRIP), Kuppinger Cole wrote, “Once again, Experian is a Leader in Fraud Reduction Intelligence Platforms. Any organizations looking for a full-featured FRIP service with global support should consider Experian CrossCore.”  Learn more about Experian’s AML and fraud solutions. 1. Experian (2023). Experian's 2023 Identity and Fraud Report  

Know Your Customer (KYC) procedures are a requirement for banks and other financial institutions to collect and verify the identity of their customers. When a bank verifies the identity of another organization or its owners, the process may be called Know Your Business (KYB) instead.  As part of banks’ anti-money laundering (AML) programs, KYC can help stop corruption, money laundering and terrorist financing. Creating and maintaining KYC programs is also important for regulatory compliance, reputation management and fraud prevention.  READ: How to Build a Know Your Customer Checklist – Everything You Need to Know The three components of KYC programs Banks can largely determine how to set up their KYC and AML programs within the applicable regulatory guidelines. In the United States, KYC needs to happen when banks initially onboard a new customer. But it’s not a one-and-done event—ongoing customer and transaction monitoring is also important.  Customer Identification Program (CIP) Creating a robust Customer Identification Program (CIP) is an essential part of KYC. At a minimum, a bank’s CIP requires it to collect the following information from new customers: Name Date of birth Address Identification number, such as a Social Security number (SSN) or Employer Identification Number (EIN) Banks' CIPs also have to use risk-based procedures to verify customers’ identities and form a reasonable belief that they know the customer's true identity.1 This might involve comparing the information from the application to the customer’s government-issued ID, other identifying documents and authoritative data sources, such as credit bureau databases. Additionally, the bank's CIP will govern how the bank:  Retains the customer’s identifying information Compares customer to government lists  Provides customers with adequate notices Banks can create CIPs that meet all the requirements in various ways, and many use third-party solutions to quickly collect data, detect forged or falsified documents and verify the provided information.  INFOGRAPHIC: Streamlining the Digital Onboarding Process: Beating Fraud at its Game Customer due diligence (CDD)  CIP and CDD overlap, but the CIP primarily verifies a customer’s identity while customer due diligence (CDD) helps banks understand the risk that each customer poses. To do this, banks try to understand what various types of customers do, what those customers’ normal banking activity looks like, and in contrast, what could be unusual or suspicious activity.  Financial institutions can use risk ratings and scores to evaluate customers and then use simplified, standard or enhanced due diligence (EDD) processes based on the results. For example, customers who might pose a greater risk of laundering money or financing terrorism may need to undergo additional screenings and clarify the source of their funds. Ongoing monitoring Ongoing or continuous monitoring of customers’ identities and transactions is also important for staying compliant with AML regulations and stopping fraud.  The monitoring can help banks spot a significant change in the identity of the customer, beneficial owner or account, which may require a new KYC check. Unusual transactions can also be a sign of money laundering or fraud, and they may require the bank to file a suspicious activity report (SAR). Why is KYC important in banking? Understanding and implementing KYC processes can be important for several reasons:  Regulatory compliance: Although the specific laws and rules can vary by country or region, many banks are required to have AML procedures, including KYC. The fines for violating AML regulations can be in the hundreds of millions— a few banks have been fined over $1 billion for lax AML enforcement and sanctions breaching. Reputation management: In some cases, enforcement actions and fines were headline news. Banks that don’t have robust KYC procedures in place risk losing their customers' trust and respect.  Fraud prevention: In addition to the regulatory requirements, KYC policies and systems can also work alongside fraud management solutions for banks. Identity verification at onboarding can help banks identify synthetic identities attempting to open money mule accounts or take out loans. Ongoing monitoring can also be important for identifying long-term fraud schemes and large fraud rings.  ON-DEMAND WEBINAR: Fraud Strategies for a Positive Customer Experience KYC in a digital-first world Many financial institutions have been going through digital transformations. Part of that journey is updating the systems and tools in place to meet the expectations of customers and regulators.  An Experian survey found that about half of consumers (51 percent) consider abandoning the creation of a new account because of friction or a less-than-positive experience — that increased to 69 percent for high-income households.2 The survey wasn’t specific to financial services, but friction could be a problem for banks wanting to attract new account holders. Just as access to additional data sources and machine learning help automate underwriting, financial institutions can use technological advances to add an appropriate amount of friction based on various risk signals. Some of these can be run in the background, such as an electronic Consent Based Social Security Number Verification (eCBSV) check to verify the customer’s name, SSN and date of birth match the Social Security Administration’s records. Others may require more customer involvement, such as taking a selfie that’s then compared to the image on their photo ID — Experian CrossCore® Doc Capture enables this type of verification.  Experian is a leader in identity and data management  Experian's identity verification solutions use proprietary and third-party data to help banks manage their KYC procedures, including identity verification and Customer Identification Programs. By bundling identity verification with fraud assessment, banks can stop fraudsters while quickly resolving identity discrepancies. The automated processes also allow you to offer a low-friction identity verification experience and use step-up authentications as needed.  Learn more about Experian’s identity solutions.  1FDIC (2021). Customer Identification Program 2Experian (2023). Experian's 2023 Identity and Fraud Report

Finding a reliable, customer-friendly way to protect your business against new account fraud is vital to surviving in today's digital-driven economy. Not only can ignoring the problem cause you to lose valuable money and client goodwill, but implementing the wrong solutions can lead to onboarding issues that drive away potential customers. The Experian® 2023 Identity and Fraud Report revealed that nearly 70 percent of businesses reported fraud loss in recent years, with many of these involving new account fraud. At the same time, problems with onboarding caused 37 percent of consumers to drop off and take their business elsewhere. In other words, your customers want protection, but they aren't willing to compromise their digital experience to get it. You need to find a way to meet both these needs when combating new account fraud. What is new account fraud? New account fraud occurs any time a bad actor creates an account in your system utilizing a fake or stolen identity. This process is referred to by different names, such as account takeover fraud, account creation fraud, or account opening fraud. Examples of some of the more common types of new account fraud include: Synthetic identity (ID) fraud: This type of fraud occurs when the scammer uses a real, stolen credential combined with fake credentials. For example, they might use someone's real Social Security number combined with a fake email. Identity theft: In this case, the fraudster uses personal information they stole to create a new scam account. Fake identity: With this type of fraud, scammers create an account with wholly fake credentials that haven't been stolen from any particular person. New account fraud may target individuals, but the repercussions spill over to impact entire organizations. In fact, many scammers utilize bots to attempt to steal information or create fake accounts en masse, upping the stakes even more. How does new account fraud work? New account fraud begins at a single weak security point, such as: Data breaches: The Bureau of Justice reported that in 2021 alone, 12 percent of people ages 16 or older received notifications that their personal information was involved in a data breach.1 Phishing scams: The fraudster creates an email or social media account that pretends to be from a legitimate organization or person to gain confidential information.2 Skimmers: These are put on ATMs or fuel pumps to steal credit or debit card information.2 Bot scrapers: These tools scrape information posted publicly on social media or on websites.2 Synthetic ID fraud: 80 percent of new account fraud is linked to synthetic ID fraud.3 The scammer just needs one piece of legitimate information. If they have a real Social Security number, they might combine it with a fake name and birth date (or vice versa.) After the information is stolen, the rest of the fraud takes place in steps. The fake or stolen identity might first be used to open a new account, like a credit card or a demand deposit account. Over time, the account establishes a credit history until it can be used for higher-value targets, like loans and bank withdrawals. How can organizations prevent new account fraud? Some traditional methods used to combat new account fraud include: Completely Automated Public Turing Tests (CAPTCHAs): These tests help reduce bot attacks that lead to data breaches and ensure that individuals logging into your system are actual people. Multifactor authentication (MFA): MFA bolsters users' password protection and helps guard against account takeover. If a scammer tries to take over an account, they won't be able to complete the process. Password protection: Robust password managers can help ensure that one stolen password doesn't lead to multiple breaches. Knowledge-based authentication: Knowledge-based authentication can be combined with MFA solutions, providing an additional layer of identity verification. Know-your-customer (KYC) solutions: Businesses may utilize KYC to verify customers via government IDs, background checks, ongoing monitoring, and the like. Additional protective measures may involve more robust identity verification behind the scenes. Examples include biometric verification, government ID authentication, public records analysis, and more. Unfortunately, these traditional protective measures may not be enough, for many reasons: New account fraud is frequently being perpetrated by bots, which can be tougher to keep up with and might overwhelm systems. Institutions might use multiple security solutions that aren't built to work together, leading to overlap and inefficiency. Security measures may create so much friction in the account creation process that potential new customers are turned away. How we can help Experian's fraud management services provide a multi-layered approach that lets businesses customize solutions to their particular needs. Advanced machine learning analytics utilizes extensive, proprietary data to provide a unique experience that not only protects your company, but it also protects your customers' experience. Customer identification program (CIP) Experian's KYC solutions allow you to confidently identify your customers via a low-friction experience. The tools start with onboarding, but continue throughout the customer journey, including portfolio management. The tools also help your company comply with relevant KYC regulations. Cross-industry analysis of identity behavior Experian has created an identity graph that aggregates consumer information in a way that gives companies access to a cross-industry view of identity behavior as it changes over time. This means that when a new account is opened, your company can determine behind the scenes if any part of the identity is connected to instances of fraud or presents actions not normally associated with the customer's identity. It's essentially a new paradigm that works faster behind the scenes and is part of Experian's Ascend Fraud Platform™. Multifactor authentication solutions Experian's MFA solutions utilize low-friction techniques like two-factor authentication, knowledge-based authentication, and unique one-time password authentication during remote transactions to guard against hacking. Synthetic ID fraud protection Experian's fraud management solutions include robust protection against synthetic ID fraud. Our groundbreaking technology detects and predicts synthetic identities throughout the customer lifecycle, utilizing advanced analytics capabilities. CrossCore® CrossCore combines risk-based authentication, identity proofing, and fraud detection into one cloud platform, allowing for real-time decisions to be made with flexible decisioning workflows and advanced analytics. Interactive infographic: Building a multilayered fraud and identity strategy Precise ID® The Precise ID platform lets customers choose the combination of fraud analytics, identification verification, and workflows that best meet their business needs. This includes machine-learned fraud risk models, robust consumer data assets, one-time passwords (OTPs), knowledge-based authentication (KBAs), and powerful insights via the Identity Element Network®. Account takeover fraud represents a significant threat to your business that you can't ignore. But with Experian's broad range of solutions, you can keep your systems secure while not sacrificing customer experience. Experian can keep your business secure from new account fraud Experian's innovative approach can streamline your new account fraud protection. Learn more about how our fraud management solutions can help you. Learn more References 1. Harrell, Erika. "Just the Stats: Data Breach Notifications and Identity Theft, 2021." Bureau of Justice Statistics, January 2024. https://bjs.ojp.gov/data-breach-notifications-and-identity-theft-2021 2. "Identity Theft." USA.gov, December 6, 2023. https://www.usa.gov/identity-theft 3. Purcell, Michael. "Synthetic Identity Fraud: What is It and How to Combat It." Thomson Reuters, April 28, 2023. https://legal.thomsonreuters.com/blog/synthetic-identity-fraud-what-is-it-and-how-to-combat-it/